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Computers and Chemical Engineering 41 (2012) 24– 51 Contents lists available at SciVerse ScienceDirect Computers and Chemical Engineering jou rn al h om epa ge: w ww.elsev ier .com/ locate /compchemeng Review Hybrid A critic Christod Department of a r t i c l Article history: Received 20 D Received in re Accepted 10 F Available onlin Keywords: Optimization Hybrid feedsto Supply chain Energy proces Energy system Transportation Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2. Single feedstock energy processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.1. 2.2. 2.3. 3. Hybri 3.1. 3.2. 3.3. 3.4. ∗ Correspon E-mail add 0098-1354/$ – doi:10.1016/j. Coal to liquids (CTL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.1.1. Process description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.1.2. Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.1.3. Assessment of state of the art approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Gas to liquids (GTL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2.1. Process description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2.2. Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2.3. Assessment of state of the art approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Biomass to liquids (BTL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.3.1. Process description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.3.2. Literature review—stand alone processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.3.3. Literature review—supply chain networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.3.4. Assessment of state of the art approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 d feedstock energy processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Coal and natural gas to liquids (CGTL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Coal and biomass to liquids (CBTL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2.1. Single stand-alone processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2.2. Supply chain networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Natural gas and biomass to liquids (BGTL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Coal, biomass, and natural gas to liquids (CBGTL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.4.1. Single stand-alone processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.4.2. Supply chain networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 ding author. Tel.: +1 609 258 4595; fax: +1 609 258 0211. ress: fl[email protected] (C.A. Floudas). see front matter © 2012 Elsevier Ltd. All rights reserved. compchemeng.2012.02.008 and single feedstock energy processes for liquid transportation fuels: al review oulos A. Floudas ∗, Josephine A. Elia, Richard C. Baliban Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA e i n f o ecember 2011 vised form 6 February 2012 ebruary 2012 e 7 March 2012 cks ses s engineering fuels a b s t r a c t This review provides a detailed account of the key contributions within the energy communities with specific emphasis on thermochemically based hybrid energy systems for liquid transportation fuels. Specifically, the advances in the indirect liquefaction of coal to liquid (CTL), natural gas to liquid (GTL), biomass to liquid (BTL), coal and natural gas to liquid (CGTL), coal and biomass to liquid (CBTL), natural gas and biomass to liquid (BGTL), and coal, biomass, and natural gas to liquid (CBGTL) are presented. This review is the first work that provides a comprehensive description of the contributions for the single- feedstock energy systems and the hybrid feedstock energy systems, for single stand-alone processes and energy supply chain networks. The focus is on contributions in (a) conceptual design, (b) process simula- tion, (c) economic analysis, (d) heat integration, (e) power integration, (f) water integration, (g) process synthesis, (h) life cycle analysis, (i) sensitivity analysis, (j) uncertainty issues, and (k) supply chain. A clas- sification of the contributions based on the products, as well as different research groups is also provided. © 2012 Elsevier Ltd. All rights reserved. C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 25 4. Research groups contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5. Future challenges and opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Refer . . . . . . 1. Introdu The cur sil fuels (i.e being the lo Energy Info will remain up to 2035 ingly thoug prices, vola greenhouse Alternative nuclear stil nificant rol emissions. quake and t may have p (Energy Info To addr produce liq focus of in source that (Lynd et al 2008). In th a large ma els can com imports and sustainably Today, c a majority fuel produc price and a et al., 2009) residue) are in the future to generate production National Re In light o have explor ing thermo and biomas sion to hyd Syngas is p gasification processes t Utilizing co dependence be readily in of coal, com ventional a of fuel supp lio of energ order to co have pursue siderations, of the proce the plant (e ger/ ng ut h th tura giona gatio e up tting ce to hwat . Wh s far tion re on al fre ) (Ke ssum y Info biom ll exc mplo inte ize fr cally and put treat atme , 200 pme ustri astew Ahm Karu ddit lso b y po nsid from t of p ss-ba of b and am regi lfill a proc natu verin ation his p s des oal, the i ences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ction rent global energy sector is mostly driven by fos- ., petroleum, coal, and natural gas), with petroleum ngstanding, primary energy source. The United States rmation Administration also projects that petroleum the primary fuel of choice in its various projection cases (Energy Information Administration., 2011). Increas- h, the sector is faced with challenges over high energy tility of the global oil market, and the pressure to reduce gas (GHG) emissions from fossil fuel consumption. energy sources such as solar, wind, hydropower, and l need major technological developments to play a sig- e in replacing fossil fuels and abating greenhouse gas Additionally, uncertainties such as the recent earth- sunami that damaged several nuclear reactors in Japan rofound impacts on the future of world nuclear power rmation Administration., 2011). ess some of these challenges, the use of biomass to uid transportation fuels (biofuels) has emerged as a terest since they provide a renewable carbon-based can absorb atmospheric CO2 during photosynthesis ., 2009; NAS, NAE and NRC, 2009; Science & Board, e United States transportation sector, which consumes jority of the petroleum supply to the country, biofu- plement and/or replace petroleum, reducing both fuel GHG emissions, provided that the biomass is cultivated . orn-based ethanol and soybean-based diesel comprise of the manufactured biofuels. However, their use for tion has led to concerns regarding the impact on the vailability of these feedstocks as sources of food (Lynd . Lignocellulosic plant sources (e.g., corn stover or forest expected to be a more considerable source of biofuels , though an increase in crop production will be required an appropriate amount of sustainable residue for fuels (de Fraiture, Giordano, & Liao, 2008; DOE & USDA, 2005; search Council, 2008). f the aforementioned challenges, many research efforts ed alternative, non-petroleum based processes, includ- chemical liquid fuel production from coal, natural gas, s via synthesis gas (syngas) intermediate and conver- rocarbons through the Fischer–Tropsch (FT) reaction. roduced via natural gas reforming, coal or biomass , and this intermediate opens opportunities of hybrid hat combine syngas from multiple feedstock sources. al, natural gas, and biomass as carbon sources will shift away from petroleum and the produced FT liquids can tegrated with the current fuel market. The abundance bined with recent expansions of natural gas from con- nd unconventional sources, will enhance the security ly, while the incorporation of biomass in the portfo- y processes will help reduce GHG emission figures. In mpete with petroleum-based fuels, many researchers exchan in selli Wit agricul and re for irri to mak ure, pu resour of fres sectors ply thu popula pressu nation (Bgal/d 2030 a (Energ tion of use wi to be e The minim is typi steam rial in to be the tre Energy develo for ind and w tance ( 2010; In a have a identif tical co to and all cos bioma nature to coal in high on the can fu hybrid and/or of deli the loc In t proces from c ies on d efforts to optimize the plant design, technology con- as well as multiple products to increase the profitability ss. It is also important to optimize the utilities usage of .g., heat, power, and water), the investment for the heat intermedia established paper inclu ether (DME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 power recovery network, and recover additional profit ility byproduct. e move towards biomass-based fuels, the demand for l or perennial crops will increase, affecting the national l withdrawal and consumption of freshwater needed n. Moreover, the freshwater input for the biorefineries for process losses will add to the consumption fig- additional stress on water resources. As an essential various ecosystems and human activities, the demand er spans across agricultural, industrial, and residential ile the abundance and distributed nature of the sup- has kept the price of freshwater relatively low, future increases and economic developments will intensify the water resources. In the United States alone, the total shwater in 2005 use was 349 billion gallons per day nny et al., 2009), and is expected to increase by 25% in ing an average population growth scenario of 0.9%/year rmation Administration., 2010). If a large-scale produc- ass for transportation fuels is realized, the freshwater eed the expected 25% and additional measures will have yed to minimize the supply-demand gap. nsifying stress on water resources prompts efforts to eshwater usage in energy conversion processes. Water used for washing operations, separation processes, power generation, cooling systems, or as a raw mate- to the processes. The discharged wastewater needs ed before the final disposal to the environment and nt processes can be energy intensive (Department of 6; Mielke, Anadon, & Narayanamurti, 2010). Thus, the nt of approaches to design efficient water networks al processes, minimizing both freshwater consumption ater discharge from the process, are of major impor- etovic & Grossmann, 2010, 2010a; Grossmann & Martín, ppiah & Grossmann, 2006). ion to process developments for a single plant, studies een done that consider a network based approach to tential locations of these fuel producing plants. Logis- erations in transporting the feedstocks and products the plants are taken into account such that the over- roduction is minimized. The transportation factor for sed systems is especially important due to the diffused iomass resources and its low energy density compared natural gas, which are more centralized and produced ounts. Strategic locations of biorefineries will depend onal production of biomass, whether the availability continuous supply of biomass to the plant or not. In esses where biomass feedstock is combined with coal ral gas, it is important then to investigate the trade-offs g coal, natural gas, and biomass feedstocks in choosing s of the hybrid plants. aper, we review and discuss the thermochemical based igns for the production of liquid transportation fuels biomass, or natural gas feedstocks. Specifically, stud- ndirect liquefaction of the three feedstocks via syngas te are reviewed to elucidate the contributions already in literature. The main products considered in this de gasoline, diesel, kerosene, methanol, and di-methyl ), which can be produced using commercially viable 26 C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 Table 1 List of abbreviations used throughout the review paper. Acronym Description ASU Air separation unit ATR BGTL BIGCC BTL CBGTL CBTL CCS CFD CFMR CGTL CI CNG CSTR CTL DCL DE DME FT FTS GA GHG GIS GTL IGCC LCA LNG LPG MARKAL MILP MINLP OCM PFR SNG SWOT TAPS TDC Units Bgal/d BPD GGE TPD technologie tunities in e engineering appeared. The revi in which on is used as a uids (CTL), n respectively tion of two the strategi stocks (i.e., to liquids, ( coal, bioma lenges and the abbrevi 2. Single fe Table 2 processes, n and biomas contributio energy supp cations that are tabulate we focus on the following contributions: (i) conceptual design, (ii) process simulation, (iii) economic analysis, (iv) heat integration, (v) power integration, (vi) water integration, (vii) process synthesis, (viii) life cycle analysis, (ix) sensitivity analysis, and (x) uncertainty erati e co pro onom i), re hat, a e fra r app nolo entif ork ariat zatio sitiv unce anc ell-to he s ses a al co of th oduc the es w ilitie ill m Autothermal reactor Biomass and natural gas to liquid Biomass integrated gasification combined cycle Biomass to liquid Coal, biomass, and natural gas to liquid Coal and biomass to liquid Carbon capture and sequestration Computational fluid dynamics Cascading fluidized-bed membrane reactor Coal and natural gas to liquid Compression ignition Compressed natural gas Continuous stirred-tank reactor Coal to liquid Direct coal liquefaction Differential evolution Di-methyl ether Fischer–Tropsch Fischer–Tropsch synthesis Genetic Algorithm Greenhouse gas Geographic information system Gas to liquid Integrated gasification combined cycle Life cycle analysis Liquefied natural gas Liquefied petroleum gas Market allocation Mixed-integer linear optimization Mixed-integer nonlinear optimization Oxidative coupling of methane consid propos energy and ec and (ii tions t includ in thei of tech and id framew (vii). V optimi via sen under perform on a w In t proces logistic cesses and pr tion in faciliti availab that w Plug flow reactor Synthetic natural gas Strengths, weaknesses, opportunities, and threats Trans-Alaska Pipeline System Thermal diffusion column Billion gallons per day Barrels per day Gallon of gasoline equivalent Tons per day s. Comprehensive reviews of the challenges and oppor- nergy research and advances in energy process systems (Liu, Georgiadis, & Pistikopoulos, 2011) have recently ew will initially detail the single-type feedstock designs ly one type of feedstock: coal, natural gas, or biomass raw material for liquid fuel production (i.e., coal to liq- atural gas to liquids (GTL), and biomass to liquids (BTL), ). Hybrid feedstock energy processes using a combina- or three feedstocks will then be discussed to highlight c and synergistic benefits of a mixture of different feed- coal and natural gas to liquids (CGTL), coal and biomass CBTL), natural gas and biomass to liquids (BGTL), and ss, and natural gas to liquids (CBGTL)). Finally, key chal- opportunities in the field are outlined. Table 1 lists all ations used in this review paper. edstock energy processes classifies the contributions for single feedstock energy amely coal to liquids (CTL), natural gas to liquids (GTL), s to liquids (BTL) processes into two general categories, ns on (a) stand-alone processes, and (b) network-based ly chain analyses. In the first category, research publi- investigate the development of a single plant process d according to their specific contributions. In this paper, such proble should be b tion betwe facilities. Table 3 the product from the th stocks are g (DME). Oth liquefied pe processes. From Ta recently att source that the three s not been a eration wil production feedstocks gas, reducin However, th CTL and GTL environmen Several cesses to ev performanc technologie (2011). Ert system and ity of the (2009) com Fischer–Tro fuel at the ture and se Ren, Daniel and BTL an ons. For example, research publications listed under (i) nceptual designs of the coal, natural gas, or biomass cesses, and the publications that include simulations ic analysis of the proposed process are noted under (ii) spectively. Categories (iv), (v), and (vi) outline publica- long with the proposed energy conversion process, also meworks to integrate heat, power, or water networks roach. Research works that consider a superstructure gies and conversion routes of feedstock to products, y the optimal plant topology based on an optimization are categorized under the process synthesis category ions of parameter inputs in the design, simulation, and n of energy processes are generally taken into account ity analysis (ix) and design, simulation, and optimization rtainty (x). Finally, the proposed process’ environmental e is measured by the greenhouse gas emissions either -wheel basis or on a single plant basis. econd category, coal, natural gas, or biomass energy re considered in a supply chain network, where the nsiderations for the upstream and/or downstream pro- e plant such as the feedstock acquisition, transportation, t transportation, are taken into account. The key ques- problem formulation is where to locate the plant ith respect to the regional constraints (e.g., feedstock s, demand amount, and transportation infrastructure) inimize the overall cost of fuel production. Solutions of ms include the proposed locations where the facilities uilt and the flow rate and costs of each interconnec- en the feedstock and product nodes with the selected organizes the contributions listed in Table 2 based on s of the energy processes. The five main liquid products ermochemically based, indirect liquefaction of the feed- asoline, diesel, kerosene, methanol, and dimethyl ether er products listed on the table (i.e., electricity, hydrogen, troleum gas (LPG), etc.) are co-products of the reviewed ble 2, it is readily observed that the BTL system has racted much attention as biomass is a renewable energy can potentially play a bigger role in the future. Across ingle feedstock energy systems, water integration has focus of attention, although water resource consid- l have to be taken into account especially if biomass will be significantly expanded in the future. As biomass have lower energy density compared to coal and natural g the cost of biomass energy systems is still a challenge. e GHG emissions figure from BTL systems is lower than systems, elucidating a trade off between economic and tal performances of CTL, GTL, and BTL systems. studies provide comparisons between the three pro- aluate the marketability, economic and environmental es of each system. An overview of GTL, CTL, and BTL s can be found in Gill, Tsolakis, Dearn, and Fernández urk (2011) performed an economic analysis on each identified key factors that determine the profitabil- system under various market conditions. Vliet et al. pared the carbon and energy balances of 14 different psch (FT) fuel production and found that BTL produces highest cost, and CTL and GTL, without carbon cap- questration, will emit GHG more than conventional oil. s, Patel, and Blok (2009) studied 24 routes of GTL, CTL, d estimated the production costs for 2030–2050 using C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 27 Table 2 CTL, GTL, and BTL contributions on stand-alone processes and energy supply chain networks. The numerical figures refer to the reference list at the end of this paper. Component CTL GTL BTL Stand-alone systems Conceptual design Sudiro, Bertucco, Ruggeri, and Fontana (2008), Li, Gao, Chang, Liu, and Pistikopoulos (2010), Mantripragada and Rubin (2009), Mantripragada and Rubin (2011b), Mantripragada and Rubin (2011a), Yu, Zu, Hao, Li, and Liu (2010), Liu, Pistikopoulos, and Li (2009), Lamprecht, Nel, and Leckel (2010), Liu, Pistikopoulos, and Li (2010b), Liu, Pistikopoulos, and Li (2010a), Williams, Larson, Liu, and Kreutz (2009), Kreutz et al. (2008), Liu, Larson, Williams, Kreutz, and Guo (2011), Williams, Liu, Kreutz, and Larson (2011), Chen, Adams, and Barton (2011b), Sudiro and Bertucco (2007), Sudiro and Bertucco (2009), Adams and Barton (2011a), Chen, Adams, and Barton (2011a), Adams and Barton (2011b), Zhou, Hu, Li, and Zhou (2008), Huffman (2011), Zhou et al. (2009), Guang-jian, Zheng, Ming-hua, and Wei-dou (2010), Larson and Tingjin (2003), Han, Zhang, Ying, and Fang (2009), Cocco, Pettinau, and Cau (2006), Jun-ling, Hao, Zharr Cheng, and Zhi hong (2002), Sun, Jin, Gao, and Han (2010), Cau, Carapelluci, and Cocco (1997), Lin, Jin, Gao, and Han (2011), Dillerop, van der Berg, and van der Ham (2010), Gao, Jin, Liu, and Zheng (2004), Lin, Jin, Gao, and Han (2010) Hao et al. (2008), Bao, El-Halwagi, and Elbashir (2010), Iandoli and Kjelstrup (2007), Kim, Jun, Joo, Han, and Song (2009), Hall (2005), Ha, Bae, Woo, and Jun (2010), Lee, Lim, Kim, and Han (2009), Suzuki, Sasaki, and Kojima (1996), Gao et al. (2008), Sudiro and Bertucco (2007), Sudiro and Bertucco (2009), Heimel and Lowe (2009), Zhou et al. (2009), Peng, Wang, Toseland, and Tij (1999), Bin, Hingguang, and Lin (2008), Horstman, Abata, Keith, and Oberto (2005) Larson, Jin, and Celik (2009), Laser, Jin, Jayawardhana, Dale, and Lynd (2009), Tock, Gassner, and Maréchal (2010), Sues, Jurascík, and Ptasinski (2010), Manganaro et al. (2011), Hamelinck, Faaij, Uil, and Boerrigter (2004), Perales, Valle, Ollero, and Barea (2011), Tijmensen, Faaij, Hamelinck, and Hardeveld (2002), Bridgwater and Double (1991), Swanson, Platon, Satrio, and Brown (2010), Clausen, Elmegaard, and Houbak (2010), Sharma, Sarker, and Romagnoli (2011), Renó et al. (2011), Bao, Ng, Tay, Gutiérrez, and El-Halwagi (2011), Sammons, Yuan, Eden, Aksoy, and Cullinan (2008), Williams et al. (2009), Kreutz et al. (2008), Liu, Larson, et al. (2011), Williams et al. (2011), Seiler, Hohwiller, Imbach, and Luciani (2010), Kumabe et al. (2008), Ju et al. (2009), Chang, Fu, and Luo (2012), Hamelinck and Faaij (2002), Clausen, Houbak, and Elmegaard (2010), Van Rens, Huisman, Lathouder, and Cornelissen (2011), Zhang, Solli, Hertwich, Tian, and Zhang (2009), Kim, Han, and Yoon (2010), Zhang, Xiao, and Shen (2009), He and Zhang (2011), Ng and Sadhukkan (2011), Li, Hong, Gao, and Jin (2008), Xiao, Shen, Zhang, and Gu (2009), Mignard and Pritchard (2008), Mignard and Pritchard (2008), Williams, Larson, Katofsky, and Chen (1995), Sarkar, Kumar, and Sultana (2011), De Kam, Morey, and Tiffany (2009), Kou and Zhao (2011), Cucek, Martin, Grossmann, and Kravanja (2011), Zwart and Boerrigter (2005) Process simulation Sudiro et al. (2008), Li, Gao, et al. (2010), Mantripragada and Rubin (2009), Mantripragada and Rubin (2011b), Mantripragada and Rubin (2011a), Yu et al. (2010), Williams et al. (2009), Kreutz et al. (2008), Liu, Larson, et al. (2011), Williams et al. (2011), Chen et al. (2011b), Chen et al. (2011a), Sudiro and Bertucco (2007), Sudiro and Bertucco (2009), Adams and Barton (2011a), Adams and Barton (2011b), Zhou et al. (2008), Zhou et al. (2009), Guang-jian et al. (2010), Larson and Tingjin (2003), Han et al. (2009), Cocco et al. (2006), Sun et al. (2010), Lin et al. (2011), Gao et al. (2004), Lin et al. (2010) Hao et al. (2008), Bao et al. (2010), Iandoli and Kjelstrup (2007), Kim et al. (2009), Ha et al. (2010), Lee et al. (2009), Gao et al. (2008), Dillerop et al. (2010), Sudiro and Bertucco (2007), Sudiro and Bertucco (2009), Heimel and Lowe (2009), Liu, Williams, Larson, and Kreutz (2011b), Zhou et al. (2009), Peng et al. (1999), Bin et al. (2008) Larson, Jin, et al. (2009), Laser, Jin, et al. (2009), Tock et al. (2010), Sues et al. (2010), Manganaro et al. (2011), Hamelinck et al. (2004), Perales et al. (2011), Tijmensen et al. (2002), Bridgwater and Double (1991), Swanson et al. (2010), Clausen, Elmegaard, et al. (2010), Williams et al. (2009), Kreutz et al. (2008), Liu, Larson, et al. (2011), Williams et al. (2011), Kumabe et al. (2008), Ju et al. (2009), Chang et al. (2012), Hamelinck and Faaij (2002), Clausen, Houbak, et al. (2010), Van Rens et al. (2011), Zhang, Solli, et al. (2009), Zhang, Xiao, et al. (2009), He and Zhang (2011), Ng and Sadhukkan (2011), Li et al. (2008), Xiao et al. (2009), Wu, Wu, and Wang (2006), Williams et al. (1995), De Kam et al. (2009), Kou and Zhao (2011), Zwart and Boerrigter (2005) Economic analysis Li, Gao, et al. (2010), Liu, Gerogiorgis, and Pistikopoulos (2007), Mantripragada and Rubin (2009), Mantripragada and Rubin (2011b), Mantripragada and Rubin (2011a), Liu et al. (2009), Liu et al. (2010b), Liu et al. (2010a), Williams et al. (2009), Kreutz et al. (2008), Liu, Larson, et al. (2011), Williams et al. (2011), Chen et al. (2011b), Chen et al. (2011a), Larson et al. (2010), Larson, Fiorese, et al. (2009), Adams and Barton (2011a), Adams and Barton (2011b), Zhou et al. (2008), Erturk (2011), Vliet et al. (2009), Zhou et al. (2009), Larson and Tingjin (2003), Cocco et al. (2006), Jun-ling et al. (2002), Lin et al. (2011), Lin et al. (2010) Bao et al. (2010), Hall (2005), Lee et al. (2009), Suzuki et al. (1996), Dillerop et al. (2010), Heimel and Lowe (2009), Zhou et al. (2009), Erturk (2011), Vliet, Faaij, and Turkenburg (2009) Larson, Jin, et al. (2009), Laser, Jin, et al. (2009), Tock et al. (2010), Hamelinck et al. (2004), Perales et al. (2011), Tijmensen et al. (2002), Bridgwater and Double (1991), Swanson et al. (2010), Clausen, Elmegaard, et al. (2010), Sharma et al. (2011), Henrich, Dahmen, and Dinjus (2009), Sunde, Brekke, and Solberg (2011), Bao et al. (2011), Tay, Ng, Sammons, and Eden (2011), Giarola, Zamboni, and Bezzo (2011), Bai, Hwang, Kang, and Ouyang (2011), Bowling, Ortega, and El-Halwagi (2011), Parker et al. (2010), Huang, Chen, and Fan (2010), Aksoy, Cullinan, Sammons, and Eden (2008), Williams et al. (2009), Kreutz et al. (2008), Liu, Larson, et al. (2011), Williams et al. (2011), Larson et al. (2010), Kumabe et al. (2008), Ju et al. (2009), Hamelinck and Faaij (2002), Clausen, Houbak, et al. (2010), Kim et al. (2010), He and Zhang (2011), Ng and Sadhukkan (2011), Xiao et al. (2009), Erturk (2011), Vliet et al. (2009), Dal-Mas, Giarola, Zamboni, and Bezzo (2011), Amigun, Gorgens, and Knoetze (2010), Leduc, Schwab, Dotzauer, Schmid, and Obersteiner (2008), Leduc, Schmid, Obersteiner, and Riahi (2009), Leduc, Lundgren, Franklin, and Dotzauer (2010), Mignard and Pritchard (2008), Williams et al. (1995), Sarkar et al. (2011), Kou and Zhao (2011), Cucek et al. (2011), 28 C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 Table 2 (Continued ) Component CTL GTL BTL Marvin, Schmidt, Benjaafar, Tiffany, and Daoutidis (2012), Terrados, Almonacid, and Higueras (2009), Akgul, Zamboni, Bezzo, Shah, and Papageorgiou (2011), van Dyken, Bakken, and Skjelbred (2010), Rentizelas and Tatsiopoulos (2010), Rentizelas, Tatsiopoulos, and Tolis (2009), Eksioglu, Acharya, Leightley, and Arora (2009), Eksioglu, Li, Zhang, Sokhansanj, and Petrolia (2010), Zamboni, Shah, and Bezzo (2009), Dunnett, Adjiman, and Shah (2008), Marti and Gonzalez (2010), Panichelli and Gnansounou (2008), Zhang, Johnson, and Sutherland (2011), An, Wilhelm, and Searcy (2011b), Gan and Smith (2011), Leduc, Starfelt, et al. (2010), Hacatoglu, McLellan, and Layzell (2011), Cherubini (2010b), Kocoloski, Griffin, and Matthews (2011), Cucek, Lam, Klemes, Varbanov, and Kravanja (2010), Zwart and Boerrigter (2005) Heat integration Sudiro et al. (2008), Li, Gao, et al. (2010), Mantripragada and Rubin (2011b), Mantripragada and Rubin (2011a), Yu et al. (2010), Liu et al. (2009), Liu et al. (2010b), Liu et al. (2010a), Williams et al. (2009), Kreutz et al. (2008), Liu, Larson, et al. (2011), Williams et al. (2011), Chen et al. (2011b), Chen et al. (2011a), Adams and Barton (2011a), Adams and Barton (2011b), Zhou et al. (2009), Guang-jian et al. (2010), Larson and Tingjin (2003), Cocco et al. (2006), Sun et al. (2010), Lin et al. (2011), Gao et al. (2004), Lin et al. (2010) Bao et al. (2010), Iandoli and Kjelstrup (2007), Gao et al. (2008), Dillerop et al. (2010), Zhou et al. (2009), Bin et al. (2008) Larson, Jin, et al. (2009), Laser, Jin, et al. (2009), Tock et al. (2010), Sues et al. (2010), Hamelinck et al. (2004), Perales et al. (2011), Tijmensen et al. (2002), Swanson et al. (2010), Clausen, Elmegaard, et al. (2010), Sharma et al. (2011), Renó et al. (2011), Tay et al. (2011), Williams et al. (2009), Kreutz et al. (2008), Liu, Larson, et al. (2011), Williams et al. (2011), Ju et al. (2009), Hamelinck and Faaij (2002), Clausen, Houbak, et al. (2010), Van Rens et al. (2011), Zhang, Solli, et al. (2009), Kim et al. (2010), He and Zhang (2011), Ng and Sadhukkan (2011), Li et al. (2008), Xiao et al. (2009), Leduc et al. (2008), Leduc, Lundgren, et al. (2010), Williams et al. (1995), De Kam et al. (2009), Cucek et al. (2011), Rentizelas and Tatsiopoulos (2010), Rentizelas, Tatsiopoulos, et al. (2009), Marti and Gonzalez (2010), Leduc, Starfelt, et al. (2010), Power integration Sudiro et al. (2008), Mantripragada and Rubin (2009), Mantripragada and Rubin (2011b), Mantripragada and Rubin (2011a), Yu et al. (2010), Liu et al. (2009), Liu et al. (2010b), Liu et al. (2010a), Williams et al. (2009), Kreutz et al. (2008), Liu, Larson, et al. (2011), Williams et al. (2011), Adams and Barton (2011a), Adams and Barton (2011b), Zhou et al. (2009), Guang-jian et al. (2010), Larson and Tingjin (2003), Cocco et al. (2006), Sun et al. (2010), Lin et al. (2011), Gao et al. (2004), Lin et al. (2010) Bao et al. (2010), Iandoli and Kjelstrup (2007), Gao et al. (2008), Zhou et al. (2009), Bin et al. (2008) Larson, Jin, et al. (2009), Laser, Jin, et al. (2009), Tock et al. (2010), Sues et al. (2010), Hamelinck et al. (2004), Perales et al. (2011), Tijmensen et al. (2002), Swanson et al. (2010), Clausen, Elmegaard, et al. (2010), Sharma et al. (2011), Renó et al. (2011), Tay et al. (2011), Williams et al. (2009), Kreutz et al. (2008), Liu, Larson, et al. (2011), Williams et al. (2011), Kumabe et al. (2008), Ju et al. (2009), Hamelinck and Faaij (2002), Van Rens et al. (2011), Zhang, Solli, et al. (2009), Kim et al. (2010), He and Zhang (2011), Ng and Sadhukkan (2011), Li et al. (2008), Williams et al. (1995), De Kam et al. (2009), Rentizelas and Tatsiopoulos (2010), Rentizelas, Tatsiopoulos, et al. (2009), Marti and Gonzalez (2010), Leduc, Starfelt, et al. (2010) Water integration Bao et al. (2010) Cucek et al. (2011) Process synthesis Li, Gao, et al. (2010), Liu et al. (2009), Liu et al. (2010b), Liu et al. (2010a), Chen et al. (2011b), Chen et al. (2011a) Sharma et al. (2011), Bao et al. (2011), Sammons et al. (2008), Tay et al. (2011), Giarola et al. (2011), Kim et al. (2010), Cucek et al. (2011) Life cycle analysis Li, Gao, et al. (2010), Liu et al. (2010b), Jaramillo, Griffin, and Matthews (2008), Jaramillo, Samaras, Wakeley, and Meisterling (2009), Dooley, Dahowski, and Davidson (2009), Xunmin, Yan, and Xiliang (2010), Williams et al. (2009), Kreutz et al. (2008), Liu, Larson, et al. (2011), Williams et al. (2011), Larson et al. (2010), Larson, Fiorese, et al. (2009), Vliet et al. (2009), Larson and Tingjin (2003), Zhang and Huang (2007) Hao, Wang, Song, Li, and Ouyang (2010), Jaramillo et al. (2008), Jaramillo et al. (2009), Vliet et al. (2009) Sharma et al. (2011), Sunde et al. (2011), Renó et al. (2011), Giarola et al. (2011), Williams et al. (2009), Kreutz et al. (2008), Liu, Larson, et al. (2011), Williams et al. (2011), Larson et al. (2010), Xiao et al. (2009), Vliet et al. (2009), Ahlgren et al. (2008), Dowaki and Genchi (2009), Wu et al. (2006), Joelsson and Gustavsson (2010), Higo and Dowaki (2010), Cherubini and Jungmeier (2010), Williams et al. (1995), Kou and Zhao (2011), Wahlund, Yan, and Westermark (2004), Hoefnagels, Smeets, and Faaij (2010), Cucek et al. (2010) Sensitivity analysis Sudiro et al. (2008), Li, Gao, et al. (2010), Mantripragada and Rubin (2009), Mantripragada and Rubin (2011b), Mantripragada and Rubin (2011a), Yu et al. (2010), Liu et al. (2009), Williams et al. (2009), Kreutz et al. (2008), Liu, Larson, et al. (2011), Williams et al. (2011), Sudiro and Bertucco (2007), Chen et al. (2011b), Chen et al. (2011a), Zhou et al. (2009), Adams and Barton (2011a), Adams and Barton (2011b), Zhou et al. (2008), Hao et al. (2008), Bao et al. (2010), Kim et al. (2009), Ha et al. (2010), Lee et al. (2009), Suzuki et al. (1996), Sudiro and Bertucco (2007), Zhou et al. (2009), Peng et al. (1999), Dillerop et al. (2010), Liu et al. (2011b), Vliet et al. (2009) Larson, Jin, et al. (2009), Laser, Jin, et al. (2009), Tock et al. (2010), Sues et al. (2010), Manganaro et al. (2011), Hamelinck et al. (2004), Tijmensen et al. (2002), Williams et al. (2009), Kreutz et al. (2008), Clausen, Elmegaard, et al. (2010), Sharma et al. (2011), Liu, Larson, et al. (2011), Williams et al. (2011), Seiler et al. (2010), Kumabe et al. (2008), Ju et al. (2009), Chang et al. (2012), Hamelinck and Faaij (2002), Clausen, Houbak, et al. (2010), Zhang, Solli, et al. (2009), Zhang, Xiao, et al. (2009), He and Zhang (2011), Ng and Sadhukkan (2011), Li et al. (2008), Mignard and Pritchard (2008), Vliet et al. (2009), Larson et al. (2010), C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 29 Table 2 (Continued ) Component CTL GTL BTL Larson and Tingjin (2003), Han et al. (2009), Lin et al. (2011), Dillerop et al. Bai et al. (2011), Parker et al. (2010), Huang et al. (2010), Amigun et al. (2010), Leduc et al. (2008), Leduc Uncertainty i (201 Network-bas Supply chain strategic planning i (201 Note: The list the C etc.). These stu a Monte Ca calculations processes. The dist ural gas, an contrast be framework produced in tructures (e logistics of CTL and GTL ever, is mor The distanc mostly by t facility loca to the suppl to CTL and B In additi several repo (Bechtel, 19 2009; NAS, tricity (Nat fuels and e Nexant Inc. Energy Tec Phillips, Tar ity (Bechtel processes w researches. 2.1. Coal to Due to it tive to petro fuels can ta uefa Fisch echn per. Proce pica In th (2010), Lin et al. (2010), Liu et al. (2007), Vliet et al. (2009), Larson et al. (2010), Larson, Fiorese, et al. (2009) Mantripragada and Rubin (2011b), Liu et al. (2010a), Khalilpour and Karim ed analyses and Li, Gao, et al. (2010), Liu et al. (2007) Khalilpour and Karim of references included in this table does not include studies on subcomponents on dies, however, are mentioned within the text. rlo method. Finally, Ren and Patel (2009) compared the on energy use and CO2 emissions of GTL, CTL, and BTL inct difference of the distribution between coal, nat- d biomass feedstock production poses an interesting tween CTL, GTL, and BTL systems in a supply chain . Since large quantities of coal and natural gas are a centralized manner and the transportation infras- coal liq based, more t this pa 2.1.1. A ty 2008). .g., railways, pipelines, etc.) are largely in place, the coal and natural gas delivery to potential locations of plants are well established. Biomass production, how- e diffused in nature and is done in smaller quantities. e limitation of biomass transportation, which is done ruck, adds another restriction to the selection of BTL tions. As a result, much more attention has been given y chain and strategic planning of BTL systems compared TL systems. on to contributions of academic papers, we highlight rts that outlined detailed information on coal to fuels 92; National Energy Technology Laboratory, 2007a, NAE and NRC, 2009), coal and natural gas to elec- ional Energy Technology Laboratory, 2007b), coal to lectricity (Bechtel Corp and Global Energy Inc. and , 2003), biomass to fuels (Jones & Zhu, 2009; National hnology Laboratory, 2009; NAS, NAE and NRC, 2009; ud, Biddy, & Dutta, 2011), biomass to fuels and electric- , 1998), and biomass to hydrogen (Spath et al., 2005) hose components can be incorporated into academic Note that these reports are not reviewed in this paper. liquids (CTL) s abundant known reserves, coal is an attractive alterna- leum as energy source. The conversion of coal to liquid ke place via two main routes, namely direct or indirect temperatur H2, CO, CO2 shown that that are clo water–gas- Yuehong, H depending 2008) and a fier types is four specie (e.g., NH3, H above their acidic speci ing of the F physical or subsequent recover the separated u capture” of these two g capture the when a pur (ii) sequest A 2:1 ra to maximiz compositio that is less et al. (2009), Leduc, Lundgren, et al. (2010), Ahlgren et al. (2008), Joelsson and Gustavsson (2010), Kim, Realff, Lee, Whittaker, and Furtner (2011), Williams et al. (1995), Sarkar et al. (2011), Kou and Zhao (2011), Cucek et al. (2011), Marvin et al. (2012), Panichelli and Gnansounou (2008), Kocoloski et al. (2011), Cucek et al. (2010), Zwart and Boerrigter (2005) 1) Dal-Mas et al. (2011), Kim, Realff, and Lee (2011) 1) Sharma et al. (2011), Henrich et al. (2009), Sammons et al. (2008), Giarola et al. (2011), Meehan and McDonnell (2010), Bai et al. (2011), Bowling et al. (2011), Ravula, Grisso, and Cundiff (2008), Parker et al. (2010), Huang et al. (2010), Aksoy et al. (2008), Dal-Mas et al. (2011), Amigun et al. (2010), Leduc et al. (2008), Leduc et al. (2009), Leduc, Lundgren, et al. (2010), Kim, Realff, Lee, Whittaker, et al. (2011), Kim, Realff, and Lee (2011), Marvin et al. (2012), Terrados et al. (2009), Akgul et al. (2011), van Dyken et al. (2010), Rentizelas and Tatsiopoulos (2010), Rentizelas, Tatsiopoulos, et al. (2009), Eksioglu et al. (2009), Eksioglu et al. (2010), Zamboni, Shah, et al. (2009), Dunnett et al. (2008), Marti and Gonzalez (2010), Panichelli and Gnansounou (2008), Zhang et al. (2011), An et al. (2011b), Gan and Smith (2011), Leduc, Starfelt, et al. (2010), Hacatoglu et al. (2011), Cherubini (2010b), Kocoloski et al. (2011), Cucek et al. (2010) TL, GTL, and BTL processes (e.g., gasifier operation, catalyst systems, ction. The latter option, more specifically gasification- er–Tropsch (FT) conversion to liquid fuels, is currently ically and commercially established, and is reviewed in ss description l CTL plant is shown in Fig. 1 (reprinted from Kreutz et al., e CTL plant, a coal feedstock is initially gasified at high es to produce a raw synthesis gas (syngas) containing , and H2O as the four major components. It has been these four species will exist in relative concentrations se to the thermodynamic equilibrium dictated by the shift reaction (Li, Grace, Watkinson, & Ergüdenler, 2001; ao, & Zhihong, 2006). These compositions will also vary on the coal gasifier type (Jarungthammachote & Dutta, model on the coal gasifier based on a variety of gasi- included in Baliban et al. (2010). In addition to these s, light C1 and C2 hydrocarbons and acidic gas species 2S, HCl) will generally be found in concentrations far equilibrium values. Prior to entering the FT units, these es must be stripped from the syngas to prevent poison- T catalyst. This cleaning unit generally utilizes either chemical absorption to remove the acid gas which is ly directed to a treating facility (e.g., a Claus plant) to sulfur. If desired, the CO2 in the syngas can also be sing physical or chemical absorption using either “co- the CO2 and other acid gases or separate capture of as streams. A two-stage absorption framework that can sulfur-rich gases and the CO2 separately is beneficial e CO2 stream is required for either (i) process recycle or ration. tio of H2 to CO in the synthesis gas is generally desired e the conversion of CO in the FT reactor. However, the n of the syngas exiting the gasifier will have a ratio than one due to the low hydrogen content in coal. The 30 C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 Table 3 Single feedstock energy process systems organized by product type. Products CTL GTL BTL Gasoline Sudiro et al. (2008), Mantripragada and Rubin (2009), Mantripragada and Rubin (2011b), Mantripragada and Rubin (2011a), Yu et al. (2010), Lamprecht et al. (2010), Huffman (2011), Bechtel (1992), Bechtel Corp and Global Energy Inc. and Nexant Inc. (2003) Bao et al. (2010), Heimel and Lowe (2009), Hall (2005), Dillerop et al. (2010), Suzuki et al. (1996) Larson, Jin, et al. (2009), Laser, Jin, et al. (2009), Tock et al. (2010), Sues et al. (2010), Tijmensen et al. (2002), Bridgwater and Double (1991), Swanson et al. (2010), Bao et al. (2011) Kim, Realff, Lee, Whittaker, et al. (2011), Kim, Realff, and Lee (2011), Joelsson and Gustavsson (2010), Sarkar et al. (2011), Bechtel (1998), Zwart and Boerrigter (2005) Diesel Sudiro et al. (2008), Mantripragada and Rubin (2009), Mantripragada and Rubin (2011b), Mantripragada and Rubin (2011a), Yu et al. (2010), Lamprecht et al. (2010), Huffman (2011), Bechtel (1992), Bechtel Corp and Global Energy Inc. and Nexant Inc. (2003) Bao et al. (2010), Heimel and Lowe (2009), Hall (2005), Lee et al. (2009) Larson, Jin, et al. (2009), Laser, Jin, et al. (2009), Tock et al. (2010), Sues et al. (2010), Hamelinck et al. (2004), Tijmensen et al. (2002), Bridgwater and Double (1991), Swanson et al. (2010), Sharma et al. (2011), Bowling et al. (2011), Parker et al. (2010), Ahlgren et al. (2008) Kim, Realff, Lee, Whittaker, et al. (2011), Kim, Realff, and Lee (2011), Joelsson and Gustavsson (2010), Wu et al. (2006), Sarkar et al. (2011), Hacatoglu et al. (2011), Bechtel (1998), Zwart and Boerrigter (2005) Kerosene Tijmensen et al. (2002) Methanol Li, Gao, et al. (2010), Liu et al. (2009), Liu et al. (2010b), Liu et al. (2007), Cau et al. (1997), Lin et al. (2011), Gao et al. (2004) Guang-jian et al. (2010), Liu et al. (2010a), Larson and Tingjin (2003), Sun et al. (2010), Lee et al. (2009), Gao et al. (2008) Tock et al. (2010), Sues et al. (2010), Bridgwater and Double (1991), Renó et al. (2011), Kumabe et al. (2008), Clausen, Houbak, et al. (2010), Amigun et al. (2010), Leduc et al. (2009), Van Rens et al. (2011), Zhang, Xiao, et al. (2009), Ng and Sadhukkan (2011), Li et al. (2008), Leduc et al. (2008), Dowaki and Genchi (2009), Leduc, Lundgren, et al. (2010), Xiao et al. (2009), Liu et al. (2007), Joelsson and Gustavsson (2010), Williams et al. (1995), Sarkar et al. (2011) Dimethyl ether Zhang and Huang (2007), Han et al. (2009), Larson and Tingjin (2003), Cocco et al. (2006), Jun-ling et al. (2002) Lee et al. (2009), Peng et al. (1999), Bin et al. (2008), Horstman et al. (2005), Zhou et al. (2009) Larson, Jin, et al. (2009), Tock et al. (2010), Clausen, Elmegaard, et al. (2010), Tay et al. (2011), Ahlgren et al. (2008), Ju et al. (2009), Chang et al. (2012), Van Rens et al. (2011), Zhang, Solli, et al. (2009), Kim et al. (2010), Dowaki and Genchi (2009), Joelsson and Gustavsson (2010) Wu et al. (2006), Sarkar et al. (2011) Electricitya Mantripragada and Rubin (2009), Mantripragada and Rubin (2011b), Mantripragada and Rubin (2011a), Yu et al. (2010), Liu et al. (2009), Liu et al. (2010b), Liu et al. (2007), Guang-jian et al. (2010), Liu et al. (2010a), Larson and Tingjin (2003), Cocco et al. (2006), Sun et al. (2010), Cau et al. (1997), Lin et al. (2011), Gao et al. (2004) Gao et al. (2008), Bin et al. (2008), Zhou et al. (2009) Larson, Jin, et al. (2009), Laser, Jin, et al. (2009), Sues et al. (2010), Hamelinck et al. (2004), Swanson et al. (2010), Clausen, Elmegaard, et al. (2010), Sharma et al. (2011), Tay et al. (2011), Kim et al. (2010), Ng and Sadhukkan (2011), Li et al. (2008), Liu et al. (2007), Hacatoglu et al. (2011), De Kam et al. (2009), Rentizelas and Tatsiopoulos (2010), Rentizelas, Tatsiopoulos, et al. (2009), Marti and Gonzalez (2010), Leduc, Starfelt, et al. (2010), Cucek et al. (2010), Bechtel (1998) Ethanola Laser, Jin, et al. (2009), Perales et al. (2011), Bridgwater and Double (1991), Sharma et al. (2011), Bai et al. (2011), Giarola et al. (2011), Huang et al. (2010), Parker et al. (2010), Dal-Mas et al. (2011), He and Zhang (2011), Cherubini and Jungmeier (2010), De Kam et al. (2009), Kou and Zhao (2011), Cucek et al. (2011), Marvin et al. (2012), Akgul et al. (2011), Eksioglu et al. (2009), Eksioglu et al. (2010), Zamboni, Shah, et al. (2009), Dunnett et al. (2008), An et al. (2011b), Gan and Smith (2011), Leduc, Starfelt, et al. (2010), Kocoloski et al. (2011), Cucek et al. (2010) LPGa Sudiro et al. (2008), Yu et al. (2010), Lamprecht et al. (2010) Bao et al. (2010) Hydrogena Larson, Jin, et al. (2009), Laser, Jin, et al. (2009), Sues et al. (2010), Van Rens et al. (2011), Williams et al. (1995) SNGa Sudiro et al. (2008) Laser, Jin, et al. (2009), Sues et al. (2010), Zwart and Boerrigter (2005) Crude productsa Hao et al. (2008), Iandoli and Kjelstrup (2007), Kim et al. (2009), Ha et al. (2010) Tay et al. (2011) Othera Heat (Sun et al., 2010), Carbon nanotubes (Huffman, 2011) Ethylene (Hall, 2005) Heat (De Kam et al., 2009; Leduc et al., 2008; Leduc, Lundgren, et al., 2010; Ng & Sadhukkan, 2011; Sues et al., 2010), Rentizelas and Tatsiopoulos (2010), Rentizelas, Tatsiopoulos, et al. (2009), Marti and Gonzalez (2010), Leduc, Starfelt, et al. (2010)), Animal feed protein (Dale, Allen, Laser, & Lynd, 2009; Laser, Jin, et al., 2009), Succinic acid, propanediol (Sharma et al., 2011), Ammonia (Sarkar et al., 2011) a Dimethyl carbonate, LPG (liquefied petroleum gas), electricity, and hydrogen are byproducts of the processes in the cited literatures and are not reviewed in this paper. C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 31 reutz, water–gas- by either (i reaction or reaction. Th erally tolera occur upst The produc of hydrocar stream. The of hydrocar temperatur from the F using a dis isomerizati 2.1.2. Litera The follo eral variatio Jianguo et a its develop be stand-al bined cycle effect of H2 CTL and GT ceptual des for CTL app experiment showing th on the first on high-tem incorporati cess that co to hydrogen to reduce C based on an which show water usag CTL fuel pro market. Expandi tems have performanc native fuels simultaneo , 20 n ov o, Zh f coa u, an ch an stud with con hane prio tic n com pled issio anc e th ound econ d em ses w quid -pro reas gate s and Fig. 1. Coal to liquids process flowsheet (reprinted from K shift reaction is employed to adjust the H2:CO ratio ) adding steam and using the forward water–gas-shift (ii) adding H2 and using the reverse water–gas-shift e catalyst used in the water–gas-shift reactor is gen- nt to the presence of sour gases, so this reaction may ream of the cleaning units at higher temperatures. ts from the FT reactor will be (1) a synthetic blend bons ranging from C1 to C30+ and (2) a heavy wax composition of the hydrocarbon blend and the ratio bon blend to heavy wax is dependent on the choice of e, catalyst, and design of the FT reactor. The effluent T reactor can be upgraded to fuel-quality products tillation column, a wax hydrocracker, hydrotreaters, on units, and an alkylation unit (Bechtel., 1992, 1998). ture review wing studies outline the fundamental concepts and sev- ns of the CTL technologies. Liu, Shi, and Li (2010) and l. (2009) provided a general introduction of CTL and ment in China, specifying that CTL facilities can either one or coupled with an integrated gasification com- (IGCC) power plants. Lu and Lee (2007) investigated the /CO ratio in the syngas feed stream for FT synthesis for L applications. Lamprecht et al. (2010) described a con- ign of a fixed-bed dry bottom coal gasification system lications with low-rank coals. Leckel (2007) reported Barreto vided a Hongta study o Yang, X in whi (2008) system ing the A met design synthe When the cou CO2 em perform and on They f better ture an proces both li The co the inc investi to fuel al results on low-temperature FT wax derived from coal, at CTL diesel product is of high quality. Following up study, Leckel (2011) presented experimental results perature FT distillates. Huffman (2011) proposed the on of a catalytic dehydrogenation step into a CTL pro- nverts the light C1–C4 products from the FT synthesis , which is recycled to the syngas feed stream in order O2 emissions. The concept is illustrated by calculations experimental Fischer–Tropsch Synthesis (FTS) system, s a potentially drastic reduction in CO2 emissions and e of the FTS process. These experiments suggest that ducts can compete and be fully integrated in the fuel ng on the CTL process, coal-based polygeneration sys- also been studied due to the increased economic e when other products, such as electricity and alter- (e.g., methanol, dimethyl ether (DME), hydrogen), are usly produced along with the FT fuels (Yamashita & varied syng were perfor achieved w with electri the syngas for electrici Other st alternative either meth that these tors. Cau et for the inte CO2 remov the integra enhance th developed faction to m recycle pla Larson, Liu, & Williams, 2008). 05). Zheng, Weidou, Hongtao, and Linwei (2003) pro- erview of benefits for polygeneration systems in China. eng, Weidou, Larson, and Tingjin (2003) reported a case l gasification based energy system in China. Hao, Dong, d Li (2007) gave an overview of a co-generation system, IGCC system is coupled with FT synthesis. Sudiro et al. ied the coupling of an air-blown coal gasifier-combustor a CTL process to improve process performances, reduc- sumption of high-purity oxygen, and CO2 emissions. removal technology is incorporated into the process r to the FT conversion, resulting in a co-production of atural gas (SNG) along with gasoline, diesel, and LPG. pared to the conventional CTL system, they found that system has a higher yield, plant efficiency and reduced ns. Mantripragada and Rubin (2009, 2011b) studied the e of two CTL systems, one that produces only liquid fuels at co-produces electricity, simulated with Aspen Plus. that the co-production of fuels and electricity yields omic performance with the given prices of CO2 cap- issions. Mantripragada and Rubin (2011a) simulated ith the GE (slurry) and Shell (dry-feed) gasifiers for fuels only and co-production of fuels and electricity. duction scheme yielded lower liquid fuel cost due to ed profit from electricity production. Yu et al. (2010) d polygeneration processes in which coal is converted electricity with carbon capture. Four case studies with as portions directed to fuels and electricity production med and it was found that the best overall efficiency is hen the syngas is sent for maximum FT fuel production city generation from tail gas, instead of the cases where stream is split and sent directly to the combined cycle ty generation. udies have focused on the production of liquids using non-FT routes. These studies have strongly focused on anol or DME production due to the potential impact two liquids can have in the chemical and energy sec- al. (1997) explored and developed a conceptual design gration of IGCC system with methanol synthesis and al. Carapellucci, Cau, and Cocco (2001) suggested that tion of the IGCC system with methanol synthesis can e overall plant performance. Larson and Tingjin (2003) conceptual process designs for the indirect coal lique- ethanol and DME in both once through structure and nt configurations. Cocco et al. (2006) studied an IGCC 32 C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 power plant configuration that is integrated with DME synthesis, with comparisons for two types of gasifiers, namely the dry-feed and slurry bed entrained flow gasifier. Zhang and Huang (2007) conducted a life cycle analysis (LCA) for coal-based DME as vehicle fuels in Chi compared t bility of pro Han et al. ( slurry bed r results. Shim model for D operating c coal gasifica to H ratio fo shift reacto the DME sy Brown, Bha Exergy a several stud formed an e and methan a system th ing heat, m to achieve t and the unr et al. (2010) of methano techno-eco CO2 recove In additi on an optim liquids syst generation that produc ematical m the feedsto quent study to determin plant desig of the poly ing in a m with an ec sidered a co and used a the produc with the ob tal impact, multi-objec co-generati economic a for multipl coal-based et al. (2010 problem an solutions. The CTL (Braithwait 2008a, 2008 & Gallagher fuel-produc vide impro petroleum- the total em Sovacool, C study (Balib cess design used to produce a near-zero emissions of CO2 without the need for CO2 sequestration. If a carbon-based source of H2 is utilized (e.g., steam reforming of methane), then the liquid fuels can be produced at prices that are competitive with petroleum based fuels and the s can um lysis ero, b ost-c k an de th m be uct s gh ce ed g uct hnol 010; t con chea o be natio nd re the Asses l gas one indu r from n 198 of 24 trole indu mino 00 dr te, 1 noco hem na ha d 16 t Wa e TP ing in PD (E logy rs w per f 33% ed a , a c nda rs to fuels 0% o pes and hem a com in Ch lique ith L pla en f f oil p her, na and suggested that DME has less overall emissions o CTL diesel. Jun-ling et al. (2002) explored the feasi- ducing DME to utilize excess coal gas in steel works. 2009) modeled the conversion of syngas to DME in a eactor and compared the prediction with experimental , Lee, Yoo, Yun, and Kim (2009) developed a kinetics ME synthesis based on Aspen Plus to determine optimal onditions. Zhou et al. (2009) combined the syngas from tion and natural gas reforming to achieve the correct C r DME synthesis, eliminating the need for a water gas r to adjust the syngas composition. For an overview of nthesis process from syngas, the reader is directed to tt, Hsiung, Lewnard, and Waller (1991). nalyses for coal to liquid systems have been shown in ies. Gao et al. (2004) and Guang-jian et al. (2010) per- xergy analysis for a system that co-produces electricity ol. Sun et al. (2010) completed an exergy analysis for at consumes coke oven gas and coal, producing cok- ethanol, and electricity. The two gas sources are mixed he correct syngas composition for methanol synthesis, eacted syngas is sent to a power generation section. Lin performed an economic analysis for the co-production l and electricity, and Lin et al. (2011) completed a nomic evaluation for the polygeneration system with ry. on to the process designs, several studies have focused ization-based process synthesis methods for coal to ems. Liu et al. (2007) developed an approach for poly- energy systems, and applied their model to a system es methanol and electricity. The multi-period math- odel allows for capacity expansion, and determines ck and technology chosen for the process. In a subse- , Liu et al. (2009) employed optimization approaches e the optimal methanol and electricity polygeneration n under user-determined scenarios. A superstructure generation system is modeled mathematically, result- ixed-integer nonlinear optimization (MINLP) model onomic objective function. Li, Gao, et al. (2010) con- al-derived methanol production for hydrogen vehicles n optimization approach to simultaneously optimize tion, product distribution, and hydrogen dispensing, jective of improving energy efficiency, environmen- and economic behavior. Liu et al. (2010b) developed a tive optimization approach for a methanol and power on system where the objective function includes both nd environmental measures. The framework allows e feedstocks, but the case study in the paper is a polygeneration system. The work is extended in Liu a) to incorporate uncertainties in a multiperiod MINLP d a decomposition algorithm is used to obtain the technologies have grown to attract policy discussions e, Horst, & Iacobucci, 2010; Fischedick, 2010; Vallentin, b; Yingyue, Liping, Changqing, & Hongmei, 2011; Zhao , 2007). However, while showing promise as a viable ing technology, the standard CTL design does not pro- vements on the overall GHG emissions compared to based technologies. In fact, CTL is estimated to increase issions (Dooley et al., 2009; Jaramillo et al., 2008, 2009; ooper, & Parenteau, 2011; Xunmin et al., 2010). A recent an et al., 2010; Elia et al., 2010) has developed a CTL pro- where recycle of the CO2 and reaction with input H2 is proces petrole electro near-z fuels c Höö conclu CTL fro to prod althou sustain to prod the tec et al., 2 produc gas or can als combi ratio a cost of 2.1.3. Coa almost power gasifie plant i a total and pe power of bitu ing 25 Institu The Co Dow C Louisa coal an ating a ash-fre operat 1800 T Techno gasifie barrels ratio o describ eration at Secu gasifie liquid vides 4 two ty diesel value c route, Group which alyst w The DC and wh year o & Fletc have GHG emissions that are reduced with respect to fuels. If a non-carbon based source of H2 is utilized (i.e., of water), then the emissions from the process will be ut a low cost of electricity is required to make the liquid ompetitive. d Aleklett (2010) has reviewed the CTL technologies and at the feedstock to product conversion ratio prevents ing a global liquid fuel supplier. The ratio from feedstock uggests that some CTL scenarios are too optimistic, and rtain countries can pursue this option, CTL cannot be lobally. However, it is possible to raise the feedstock conversion ratio to allow for a more widespread use of ogy. The near-zero emissions CTL plant designs (Baliban Elia et al., 2010) are capable of pushing the feedstock to version ratio to levels above 95% if a source of natural p electricity can be obtained. The low conversion ratio mitigated by using hybrid energy processes where a n of feedstocks are input to help increase the conversion duce GHG emissions while taking advantage of the low coal feedstock. sment of state of the art approaches ification has existed commercially around the world for hundred years, and has been utilized by the electric stry for almost four decades. Texaco developed the GEE the 1940s and started the first commercial gasification 3 (Electric Power Research Institute, 1993). Currently, gasifiers have been built in 12 locations for both coal um coke. The first commercial-scale unit for the electric stry gasified a total of 1000 dry short tons per day (TPD) us coal and the largest unit to date is capable of gasify- y TPD of coal in a single unit (Electric Power Research 993; National Energy Technology Laboratory, 2007b). Phillips E-GasTM gasifier was originally developed by ical beginning in 1976. A unit operating in Plaquemine, s a capacity of 1400 dry, ash-free TPD for bituminous 50 dry, ash free TPD for lignite coal and the unit oper- bush River, Indiana is able to handle up to 1850 dry, D of high-sulfur bituminous coal. Shell also has a gasifier The Netherlands that has a bituminous coal capacity of lectric Power Research Institute, 1993; National Energy Laboratory, 2007b). The operating capacities of these ould be able to produce approximately 4–5 thousand day of liquid product, if a feedstock carbon conversion is assumed (Kreutz et al., 2008). Though the gasifiers bove have been used extensively for electric power gen- ommercial-scale CTL plant has been operating by Sasol in South Africa. The project utilized approximately 100 convert over 40 million metric tonnes of coal per year to . The production capacity of 150,000 barrels per day pro- f the liquid fuels for South Africa. Sasol operates using of FT technology: a low-temperature unit to produce a high-temperature unit to produce a variety of high- icals (Sasol., 2009). As an alternative to the gasification mercial CTL plant has also been built by the Shenhua ina using the direct coal liquefaction (DCL) technology, fies coal at high temperature and pressure over a cat- an input of hydrogen, to product transportation fuels. nt at Erdos, Inner Mongolia started operating in 2009 ully operated, is expected to produce 1 megatonnes per roducts, or about 25,000 barrel/day (BPD) of fuels (Su 2010). C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 33 ed fro The cost to $1.6/gall carbon capt for processe 2011a, 201 Jun-ling et a Fiorese, et a et al., 2010, et al., 2011; 2009; Willi 2009; Willi emissions f of a petrole sions that a et al., 2008) plant has b for plants th duce additio with a “onc is used for e NRC, 2009) will be high can be obta the overall assumed ca the potenti generate. The amo final liquid p 2011b; Che Guang-jian Tingjin, 200 et al., 2011; Bertucco, 20 et al., 2009 highly depe to make th generally b of unreacte Process con conversion cost of capit tal (i.e., $/bp cle. Carbon carbon base n is , & D l, the ge o 201 011; 009, of ei utiliz s. s to Proce pica arkin hat i n th m th r tha of su via s herm mer activ :CO r exit ver a Fig. 2. Gas to liquids process flowsheet (reprint of CTL plants have been estimated to range from $1.1 on of gasoline equivalent (GGE) for processes without ure and sequestration (CCS) and from $1.5 to $1.8/GGE s with CCS (Adams & Barton, 2011a, 2011b; Chen et al., 1b; Cocco et al., 2006; Elia et al., 2010; Erturk, 2011; l., 2002; Kreutz et al., 2008; Larson et al., 2010; Larson, l., 2009; Larson & Tingjin, 2003; Li, Gao, et al., 2010; Lin 2011; Liu et al., 2007, 2009, 2010a, 2010b; Liu, Larson, Mantripragada & Rubin, 2009, 2011a, 2011b; NAS et al., ams et al., 2009; NAS, NAE and NRC, 2009; Vliet et al., ams et al., 2011; Zhou et al., 2008, 2009), though the rom the processes without CCS can be over twice that um refinery while the processes with CCS have emis- re comparable with petroleum-based processes (Kreutz . The capital investment estimated for a 50,000 BPD CTL een estimated to be around $100,000/BPD of product at utilize recycle of the unreacted synthesis gas to pro- nal liquid product and around $120,000/BPD for plants e-through” configuration where the unreacted syngas lectricity production (Kreutz et al., 2008; NAS, NAE and . Note that the effects of scaling in the investment cost ly dependent on the maximum possible capacity that ined within specific units in the process. Additionally, cost will depend greatly on (i) the cost of coal, (ii) the pital investment and levelized annuity factors, and (iii) al to sell any byproduct electricity that the plant will unt of carbon in the coal feedstock that is converted to reactio Ribeiro genera the ran 2011a, et al., 2 et al., 2 choice cesses proces 2.2. Ga 2.2.1. A ty from P units t betwee gas fro cleane degree either (auto-t are com an attr the H2 stream to reco roduct ranges from 20% to 35% (Adams & Barton, 2011a, n et al., 2011a, 2011b; Cocco et al., 2006; Gao et al., 2004; et al., 2010; Han et al., 2009; Kreutz et al., 2008; Larson & 3; Li, Gao, et al., 2010; Lin et al., 2010, 2011; Liu, Larson, Mantripragada & Rubin, 2009, 2011a, 2011b; Sudiro & 07, 2009; Sudiro et al., 2008; Sun et al., 2010; Williams , 2011; Yu et al., 2010; Zhou et al., 2008, 2009), and is ndent on the topology of the process flowsheet used e liquid fuels. The “once-through” configurations will e on the lower end of that range, due to the conversion d syngas to CO2 via combustion/electricity generation. figurations with recycle of the CO2 can achieve higher rates of carbon, though this generally comes at a higher al for the process. However, the normalized cost of capi- d) will be lower for the processes including syngas recy- conversion ratios of near 100% can be achieved if a non- d source of H2 is input and the reverse water–gas-shift 2.2.2. Litera Out of t nology is th Overviews Karp, and D existing GT Iandoli a ciency of a unit. Hao et and cobalt tions, includ flow reacto determined the product which did n conversion m Parkinson, 2005). used to consume the process CO2 (Agrawal, Singh, elgass, 2007; Baliban et al., 2010; Elia et al., 2010). In lower-heating value efficiency for a CTL process is in f 45–50% (Adams & Barton, 2011a, 2011b; Chen et al., 1b; Kreutz et al., 2008; Li, Gao, et al., 2010; Liu, Larson, Mantripragada & Rubin, 2009, 2011a, 2011b; Williams 2011; Yu et al., 2010), and does not vary widely with the ther “once-through” or recycle configurations. For pro- ing CCS, the loss of efficiency is generally 1–2% for the liquids (GTL) ss description l gas to liquids (GTL) process is shown in Fig. 2 (reprinted son, 2005) and has a “downstream” series of process s very similar to the CTL process. The key difference ese two technologies is the generation of the synthesis e initial feedstock. The natural gas feedstock is generally n coal feedstocks, and will therefore require a smaller lfur removal. The natural gas can be converted to syngas team reforming, partial oxidation, or both in sequence al reforming). Though all three of these technologies cially mature, oxygen-blown auto-thermal reforming is e option because the process can be tailored to produce atio that is needed for FT synthesis. The synthesis gas ing the reactor may be directed to a CO2 removal unit nd sequester the CO2 prior to entry into the FT units. ture review he three single-feed alternative processes, GTL tech- e most technologically and commercially developed. of the GTL process can be found in Wilhelm, Simbeck, ickenson (2001) and Basini (2005), and a review of L technologies can be found in Eliseev (2009). nd Kjelstrup (2007) analyzed the thermodynamic effi- GTL system and conducted an exergy analysis for each al. (2008) simulated GTL process alternatives with iron catalyzed FT reactors. Based on several process simula- ing the continuous stirred-tank reactor (CSTR) and plug r (PFR) reactor configurations, flowsheet structures are from the thermal and carbon efficiencies, as well as distributions of the process. The cobalt-based system, ot require CO2 removal from the FT tail gas, achieved full and is more efficient than the once-through iron-based 34 C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 system, which includes CO2 removal from raw syngas. Hall (2005) gave an overview of a technology that involves three reaction steps and two separation steps to produce hydrocarbon liquids. Kim et al. (2009) simulated a GTL system for maximum fuel pro- duction by reactor. Arz the integrat a catalytic m ics (CFD). Be plant in FTS mize the pla a comprehe including th analysis of With th studies are of a GTL p compared post-combu respect to th (1995) repo gen produc (TDC) react and Bakker mal reactor investigated gen or air i (2005) dem CO2 reform temperatur Dillerop the reverse within the et al. (2011) thermal pla Ha et al. (2 bon dioxide recycling un recycling, th achieved an (2011) perfo two reform A series with hydro (2009a) ou and simulat bined with hydrogen a is higher an Rahimpour gasoline pro was optimi line blend Mostafazad counter-cur that the co vorable yie (2011) inve (CFMR) con (2011d) sh higher gas (2010) opt tion of cy (DE) metho Mirvakili, a and Paymo cyclohexane) with hydrogen production are optimized using the DE method. Methanol and DME production from natural gas have been stud- ied. Steinberg (1998) performed a kinetic study for methanol and en p rodu by r g po l gas d co nce t ed h aren ko, P the sis re g et sis st the f s we ation 005 “on- e hig and form stud ente pow h the inally n and E syn nd c duct tion rmin L pro ios. I t of natu that rice (200 n, an stion sel en eschi Kim & Sh Wu, H 07; X rio, Lee, amp ski, ly to ted p imp to b Slop its an h the bet mmi changing variables such as the temperature of the FT amendi et al. (2009) performed simulation studies of ion of steam reforming and combustion of methane in icrochannel reactor using computational fluid dynam- hroozsarand and Zamaniyan (2011) studied the amine application simulated in HYSYS and MATLAB to opti- nt’s operational parameters. Bao et al. (2010) presented nsive thermo-economic study of a typical GTL system, e design, simulation, process integration, and economic the process. e generally established process configurations, many dedicated to the improvement of certain segments lant on a per unit basis. Heimel and Lowe (2009) two configurations for CO2 capture for a GTL plant: stion CO2 capture and oxy-fired CO2 capture. With e reforming segment of the plant, Shigapov and Gesser rted experimental studies on liquid fuels and hydro- tion from natural gas in a thermal diffusion column or. Petersen, Christensen, Nielsen, and Dybkjaer (2003) ud (2005) discussed the development of the autother- (ATR) and reforming technologies. Breed et al. (2005) a zone, natural gas reforming reactor in which oxy- s first contacted with solid metal bromide. Yagi et al. onstrated the performance of catalysts that facilitate ing, and Rabe, Truong, and Vogel (2005) studied low e partial oxidation of methane for GTL application. et al. (2010) proposed a novel design of the ATR, namely flow catalytic membrane reactors for heat integration reactor and with the air separation unit (ASU). Agiral demonstrated a GTL process in a multi-phase flow, non- sma microreactor based on dielectric barrier discharge. 010) studied two reforming units, the steam and car- reforming of methane to form syngas, and the effect of reacted syngas mixture to the process efficiency. With ey found that zero emissions from the process can be d a reduction of natural gas used. Lee, Hong, and Moon rmed the simulation and experimental results from the ing methods. of papers investigated a novel FTS reactor design gen permselective membrane. Rahimpour and Elekaei tlined a FTS reactor design, modeled mathematically ed using MATLAB, in which a fixed-bed reactor is com- a membrane assisted fluidized-bed reactor to control ddition. The results showed that the yield in gasoline d CO2 formation is decreased. Forghani, Elekaei, and (2009) reported that the co-current mode can enhance duction. In Rahimpour and Elekaei (2009b) the reactor zed using a Genetic Algorithm (GA) to maximize gaso- and minimize CO2 production. Rahimpour, Forghani, eh, and Shariati (2010) compared the co-current and rent modes for the dual-bed FTS reactor, and found unter-current produces more gasoline and less unfa- lds. Rahimpour, Mirvakili, Paymooni, and Moghtaderi stigated a cascading fluidized-bed membrane reactor figuration for FTS. Rahimpour, Mirvakili, and Paymooni owed that a double membrane in the reactor yielded oline yield. Rahimpour, Khademi, and Bahmanpour imized the thermally coupled FTS an dehydrogena- clohexane reactors using the differential evolution d. In Rahimpour and Bahmanpour (2011), Rahimpour, nd Paymooni (2011b, 2011c), and Rahimpour, Mirvakili, oni (2011e) the thermally coupled reactors (FTS and hydrog that p thesis burnin natura tion an and o achiev sis. Pis Pisaren els for synthe Pen synthe CO in used, a gasific et al. (2 ral gas achiev (2003) a tri-re (2008) syngas to the for bot case. F ficatio for DM study a Pro applica to dete the GT scenar uct ou when found stock p Zhang bustio combu for die Kim, B Uhm, & Ishida, 2007; Yin, 20 & Di Io Moon, 2002; L Sankow similar regula The tinues North due to formed throug parison oil (co roduction, using experimental results from a reactor ces hydrogen and applying them for methanol syn- eacting the hydrogen with CO2 recovered from coal wer plant stack gases. Gao et al. (2008) studied a polygeneration system for methanol and power produc- mpared the polygeneration system with full-reforming hrough methanol synthesis. The new configuration igher efficiency, determined from an exergy analy- ko, Pisarenko, Minigulov, and Abaskuliev (2008) and isarenko, and Sarkisov (2009) developed kinetic mod- steam-CO2 reforming of methane and the methanol actors. al. (1999) studied the optimal operation of the DME ep from syngas, in particular the ratio between H2 and eed composition. Natural gas and CO2 reforming were ll as combining both reforming technologies with coal to achieve the correct syngas composition. Horstman ) studied the feasibility of synthesizing DME from natu- board” for dual fuel compression ignition (CI) engines to her thermal efficiency and reduce emissions. Lee et al. Cho et al. (2009) optimized the design and operation of ing reactor of natural gas for DME synthesis. Bin et al. ied the polygeneration of DME and electricity, in which rs the DME synthesis step and the residual gas is sent er generation unit, and performed an exergy analysis individual product generation and the polygeneration , Zhou et al. (2009) combined the syngas from coal gasi- natural gas reforming to achieve the correct C to H ratio thesis. The coal-only system was also evaluated in this ompared to the hybrid feedstock process. s from the GTL process have been tested for various s. Lee et al. (2009) presented an economic assessment e which product (i.e., FT diesel, DME, or methanol) from cess would be the most profitable under various price t was found that FT diesel is the most profitable prod- the GTL process under current natural gas prices, but ral gas is cheaper, DME is the product of choice. They the product selection is more a function of the feed- s instead of the product prices. Li, Huang, Wang, and 7) examined the particle distribution after fuel com- d found that GTL-fuels are cleaner. Other tests, such as characteristics, have been applied to GTL fuel products gine (Azimov, Kim, Jeong, & Lee, 2011; Jrai et al., 2009; ero, Jeong, & Lee, 2007; Krahl et al., 2009; Lee, Kim, Ryu, , 2007; Mancaruso, Sequino, & Valieco, 2011; Nguyen, ioji, 2010, 2011; Ogawa, Ibuki, Minematsu, & Miyamoto, uang, Zhang, & Fang, 2007; Wu, Huang, Zhang, Fang, & inling & Zhen, 2009), passenger cars (Beatrice, Guido, 2010; Lapuerta, Armas, Hernández, & Tsolakis, 2010; Choi, & Jeong, 2010), heavy duty engine (Knottenbelt, recht, 2007; Soltic, Edenhauser, Thurnheer, Schreiber, & 2009). These tests agree that GTL diesel blends perform petrodiesel with lower GHG emissions and reduced ollutants (Fernández, Tsolakis, Cracknell, & Clark, 2009). lementation of GTL technologies has been and con- e pursued worldwide. In the United States, the Alaska e is a promising candidate location for a GTL project rich natural gas resources. Ejiofor et al. (2008) per- economic analysis to transport gas-to-liquids products existing Trans-Alaska Pipeline System (TAPS). A com- ween the option of blending GTL products with crude ngled) or as alternate slugs (batching) was completed, C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 35 and they determined that batching is the more economic option. Ibironke et al. (2011) performed a probabilistic economic analysis to transport GTL products as blends with crude oil. For this appli- cation, several studies were completed to characterize both the GTL fuels an properties 2007; Rama studies and in a demon Bolivia (Uda India (Kesh Kobrosly, & 2010; Szklo GTL tech text due to associated which, in c respectively transportat atively sma of pipeline technologie can potenti transportat the oil prod nology that (OCM) met discussed t stranded ga Liquefied N gases. Rahim FT reactor, facilitate ad Finally, planning pr mixed integ to determin for either LN given the fl 2.2.3. Asses Natural cial scale fo and the che of hydrogen reforming o the detailed it less pract does gain be H2 from the operation o Council, 20 nology due gas resourc ORYX GTL p rels/day of d that time, S duce up to 1 in the Escra launched th ducing a tot 2011). A major the capital approximat estimates fo to be higher (Davies, 2003; Sasol., 2006). In a recent study, the capi- tal cost for an 8,760 BPD GTL plant was estimated to be $60,020/BPD with the cost scaling to approximately $44,000/BPD if the plant capacity increased to 34,000 BPD (Liu, Williams, Larson, & Kreutz, . How f $18 may cost e of proc ole i s can ing f rel o Hall, Suzu te is s fro feed from and ral g on a carb than f app a H2 t the ratio hich on th tios unr 010; , 200 Liu e 2009 s ca ties o rat heat ses, p et 2009 Sud s wi ses a cy o omas Proce rmoc n in mila bio y cle nces as-re t. Th old ( . Thu e bio s in th syng tors d petroleum to determine the compatibility and flow through the pipeline system (Das, Nerella, & Kilkarni, krishnan et al., 2003). Other countries with reported demonstration pertaining to GTL fuels include China, stration program for transit buses (Hao et al., 2010), eta, Burani, Maure, & Oliva, 2007; Velasco et al., 2010), av & Basu, 2007), Nigeria (Stanley, 2009), Qatar (Chedid, Ghajar, 2007), and Brazil (Branco, Szklo, & Schaeffer, et al., 2005). nologies provide an additional niche in the energy con- their capability to monetize stranded gases, as well as gases that are the by-products from the oil industry, urrent common practices, are unexploited and flared, . Associated gases are flared because the collection and ion to the market are uneconomical due to their rel- ll amount of production, remote locations, and lack infrastructure. The development of smaller scale GTL s that can be operated in remote locations, however, ally convert these gases into valuable and marketable ion fuel products, which then can be transported via ucts pipeline. Suzuki et al. (1996) introduced a tech- converts methane via oxidative coupling of methane hod for remote natural gas. Olsen and Gobina (2004) he options provided by GTL technologies to monetize ses, and Dong, Wei, Tan, and Zhang (2008) compared atural Gas (LNG) and GTL technologies to exploit these pour, Ghorbani, Asiaee, and Shariati (2011) assessed a mathematically modeled, for gas flaring application to ditional gasoline product from refineries. an optimization-based approach for the natural gas oblem is proposed in Khalilpour and Karimi (2011). A er linear programming (MILP) problem is formulated e the investment and field development of natural gas G, Compressed Natural Gas (CNG), or GTL applications, uctuating market demands. sment of state of the art approaches gas reforming has existed for decades on a commer- r the production of hydrogen for petroleum refining mical industries. Of the approximately 41 million tons produced per year, 80% is accomplished using steam f methane (National Research Council, 2004). However, tubular structure of the steam reforming unit makes ical for scale-up. Conversely, the auto-thermal reactor nefits from scale-up and can produce a larger amount of natural gas conversion. Thus, the ATR has been the unit f choice in commercial GTL plants (National Research 04). GTL has been the most commercialized FTL tech- largely in part to the need to utilize stranded natural es throughout the world. Sasol has been operating the roject in Qatar since June 2006 to produce 34,000 bar- iesel, naphtha, and LPG products (Davies, 2003). Since asol has looked to both expand the ORYX project to pro- 00,000 barrels/day of product and develop a new plant vos region of Nigeria (Sasol., 2006). Recently, Shell has e Pearl GTL project in Qatar, which is capable of pro- al of 260,000 barrels/day of oil-equivalent fuels (Shell., concern with GTL production is the uncertainty behind costs of the plant. Sasol initially reported costs of ely $25,000/BPD for the 34,000 BPD ORYX project, but r the expansion of the facility to 100,000 BPD are slated 2011c) costs o which capital only on of the large r proces produc per bar 2011; 2011c; estima saving higher syngas fication of natu higher The higher ratio o obtain withou H2/CO used w of carb sion ra recycle et al., 2 & Lowe 2009; 2007, uration capaci version lower- proces Dillero Lowe, 2011c; uration proces efficien 2.3. Bi 2.3.1. The is show very si the raw quentl differe tent in effluen thresh tained over th the tar of the FT reac ever, the Shell Pearl GTL project has reported capital –19 billion dollars for their production, or $74,000/BPD, increase to near $24 billion (Shell., 2011). Though the estimates contain a large degree of uncertainty, it is two major components that influence the profitability ess. Specifically, the cost of natural gas will also play a n determining the cost of crude oil for which the GTL be competitive. In general, the cost associated with uels using the GTL process has ranged from $40 to $70 f crude oil (Bao et al., 2010; Dillerop et al., 2010; Erturk, 2005; Heimel & Lowe, 2009; Lee et al., 2009; Liu et al., ki et al., 1996; Vliet et al., 2009; Zhou et al., 2009). This similar to the cost of CTL processes because the cost m capital investment in GTL processes is offset by the stock costs. That is, the capital cost of producing a clean coal is generally higher due to the high costs of gasi- syngas cleaning compared to auto-thermal reforming as. However, natural gas costs per unit energy will be verage than the corresponding costs for coal. on conversion ratio in GTL processes is generally much either CTL or BTL processes. The feedstock has a H/C roximately 4, which allows the resulting synthesis gas to /CO ratio more appropriate for hydrocarbon formation need for additional carbon losses. That is, to obtain the in gasification processes, a water–gas-shift unit is often converts some of the CO to CO2, and reduces the amount at can be converted to liquid fuels. Thus, carbon conver- of 65–75% can be readily obtained from processes that eacted syngas (Bao et al., 2010; Bin et al., 2008; Dillerop Gao et al., 2008; Ha et al., 2010; Hao et al., 2008; Heimel 9; Iandoli & Kjelstrup, 2007; Kim et al., 2009; Lee et al., t al., 2011b, 2011c; Peng et al., 1999; Sudiro & Bertucco, ; Zhou et al., 2009). Note that “once-through” config- n be utilized to provide approximately equal energy f electricity and liquid fuels, though the carbon con- io will be reduced to near 40% (Liu et al., 2011c). The ing value efficiency of GTL processes is higher than CTL with ratios of 55–65% being typical (Bao et al., 2010; al., 2010; Gao et al., 2008; Ha et al., 2010; Heimel & ; Kim et al., 2009; Lee et al., 2009; Liu et al., 2011b, iro & Bertucco, 2009; Zhou et al., 2009). Recycle config- ll have slightly higher efficiencies than “once-through” nd that the implementation of CCS will penalize the f a process by about 1–3% (Liu et al., 2011b, 2011c). s to liquids (BTL) ss description hemical conversion of biomass to liquid fuels (BTL) Fig. 3 (reprinted from Rudloff, 2007) and follows a r pathway to CTL. A gasification train is used to convert mass feedstock to synthesis gas which must be subse- aned up before being passed to the FT reactors. The key with biomass gasification are the high moisture con- ceived biomass and the formation of tars in the gasifier e moisture level of the biomass should be under a certain e.g., 20%) for the efficiency of the gasifer to be main- s, a dryer may be required to pass a hot vapor stream mass to evaporate some of the moisture. Additionally, e gasifier effluent must also be removed prior to cooling as to prevent fouling of the syngas cleanup units or the . Typically, the gasifier effluent can be passed through 36 C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 rinted a tar cracke syngas. 2.3.2. Litera Numero conversion role in the Somerville, generally c from cellul include the cal convers gasification critical wat Several for second Sunde et al. of hydrotre Balat, Balat that yield c systems an syngas in th 2009b). Dem thesis from reviewed th First genera stock availa a focus on ing. Laser, biomass refi ment impac and Zabanio second gen Bulushev an pyrolysis an the product using a tech Tijmens from bioma discussed t ments. Ham production be profitabl fits of clean that large B cost estima analyzed a l diesel, gaso valu igh e price er et ocess tion anal nol, a s and wo t anal ss to on et n sto rgy eth ent sion te is as la liam nol a plica hnol ing s anc tives etha con ed th ectric and Comp s sho Fig. 3. Biomass to liquids process flowsheet (rep r to convert the long chain tar molecules to additional ture review—stand alone processes us studies have investigated various routes of biomass to liquid fuels, as biomass is expected to play a major energy sector in the near future (Lynd et al., 2009; Youngs, Taylor, Davis, & Long, 2010). Biofuels are ategorized as first generation biofuels (e.g., ethanol osic biomass) and second generation biofuels, which rmochemical conversion of biomass. Thermochemi- ions also have several alternatives, including pyrolysis, , hydrothermal, direct combustion, liquefaction, super- er extraction, and air-steam gasification. reviews exist. Zhang (2010) reviewed technologies generation bio-automotive fuels from forest residues. (2011) reviewed the environmental impacts and costs ated vegetable oils, transesterified lipids and woody BTL. , Kirtay, and Balat (2009a) reviewed pyrolysis systems harcoal, bio-oil, and fuel gas, and discuss gasification d their potential products that can be generated from e second part of the review (Balat, Balat, Kirtay, & Balat, irbas (2007) reviewed methanol synthesis and FT syn- biomass syngas. Naik, Goud, Rout, and Dalai (2010) e production of first and second generation biofuels. tion biofuels appear unsustainable due to limited feed- bility and competition with the food market. Thus, second generation lignocellulosic biofuels is increas- Larson, et al. (2009) evaluated 14 routes of mature ning scenarios and calculated the efficiency, environ- t, and economic performance of each route. Damartzis tou (2011) reviewed the thermochemical conversion to eration biofuels through an integrated process design. d Ross (2011) reviewed the conversion of biomass via were e have h tricity Seil BTL pr produc nomic metha of fuel from t exergy bioma Swans on cor an ene sheet m assessm conver cal rou route h Wil metha fuel ap ent tec tion us perform alterna of biom ied the analyz and el sibility fuels. system d gasification. Bridgwater and Double (1991) reviewed ion costs of liquid fuels from biomass via various routes no-economic simulation. en et al. (2002) explored FT liquid and power production ss, postulated that the process can be competitive, and he potential of economic and technological improve- elinck et al. (2004) performed a simulation on FT from biomass and suggested that the process will only e when oil price increases and/or environmental bene- er FT diesel are valued. Henrich et al. (2009) determined TL plant is more profitable than small ones through tes for biosynfuel production. Larson, Jin, et al. (2009) arge-scale gasification system that produces electricity, line, DME, or hydrogen from switchgrass. Five processes Mignard biomass syn production duction pro Amigun et production Economic biomass av will achiev input, incre Clausen production from post-c ural gas or b from Rudloff, 2007). ated, and they found that the once-through systems lectricity output, which are favorable when the elec- is high. al. (2010) discussed a design concept of an enhanced that includes an external energy input to increase fuel . Tock et al. (2010) performed thermodynamic and eco- ysis of an integrated process that produces FT fuels, nd DME. Sarkar et al. (2011) compared the production chemicals (i.e., methanol, DME, FT fuels, and ammonia) ypes of biomass gasifiers. Sues et al. (2010) conducted yses on 5 biowastes to fuels routes via gasification (i.e., SNG, methanol, FT fuels, hydrogen, heat and electricity). al. (2010) compared the costs of two BTL plants based ver gasification. Manganaro et al. (2011) completed analysis of residual biomass to fuels with a spread- od. Perales et al. (2011) developed a technoeconomic of ethanol production via thermochemical and catalytic . They found that the capital cost for the thermochemi- higher than the biochemical route, but the biochemical rger operating cost. s et al. (1995) investigated the co-production of nd hydrogen from gasified biomass for low-emission tions. Hamelinck and Faaij (2002) evaluated the differ- ogies associated with methanol and hydrogen produc- imulation and analyzed for their economic and energy es, showing that methanol and hydrogen are viable fuel for the future. Demirbas (2008) provided an overview nol production from waste. Kumabe et al. (2008) stud- version of woody biomass to methanol. Li et al. (2008) e performance of a polygeneration system for methanol ity. Zwart and Boerrigter (2005) investigated the fea- economic performance of co-producing SNG with FT ared to the liquids-only process, the polygeneration ws that synergy can reduce energy consumption. and Pritchard (2008) explored the enhancement of gas with hydrogen input from electrolysis for methanol . Zhang, Xiao, et al. (2009) simulated a methanol pro- cess from biomass in interconnected fluidized beds. al. (2010) evaluated the potential of biomethanol from non-woody plant gasification in South Africa. analysis suggested the right plant scale, taking the ailability and supply chain into account. Larger plants e economies of scale, but will require more biomass asing the transportation cost. , Houbak, et al. (2010) analyzed a catalytic methanol from biomass gasification, electrolysis of water, CO2 ombustion capture and autothermal reforming of nat- iogas. Six plant configurations were compared and the C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 37 lowest cost was achieved by the plant with electrolysis of water, gasification of biomass, and autothermal reforming of natural gas. Van Rens et al. (2011) performed an exergy analysis for a BTL plant producing methanol, DME, or hydrogen. Renó et al. (2011) studied the Sadhukkan platform fo thermo-eco presented i cation, alco that the cap with the bio Ahlgren biomass to tial of DME a conceptua that better biomass co analysis for Li et al. demonstrat Clausen, Elm plants, incl the catalyti Kim et al. electricity p also include biomass is s experiment Several chemical ro gasification and power p (2011) eval of a thermo weather co process tha Hess, Wrigh cellulosic b Brown ( (2009) prop combines sy Cucek et al. tem with fe corn stover In additi including th grated into biorefinery can be con ing a flexib the constra Sammons, E and Peratho biofuels bio Cherubini e fuels in his the process developme Chew a biorefinery and FTS in process sys modeling fo (2009) dev FT fuels, h co-producing animal feed protein from switchgrass. Dale et al. (2009) investigated the possibility of producing protein feeds with fuels and chemicals from biomass. Pinatti et al. (2010) discussed a biorefinery concept with eleven thermochemical routes and ologi timi logic twor for a syn ic p y ma ting Litera ike c entr sup Tats ces w tech ratu in G ion, s (201 h, cl ain ( annid s of chno ula e on co pro pret nd py ergy wang ener Yu, al m s in W nell ivers um ken e cha con 010 ls sup faci et al loca Dal- apac who 008 tion chai on m plant ood cost, in t ered eden ker, methanol production from sugarcane bagasse. Ng and (2011) studied the economics of integrating a bio-oil r producing methanol and combined heat and power. A nomic study on thermochemical ethanol production is n He and Zhang (2011). Ethanol is produced via gasifi- hol synthesis and alcohol separation. The study showed ital cost of this process is high, and not able to compete chemical route of ethanol production. et al. (2008) studied the conversion of farm-grown FT diesel and DME. Chohfi (2008) discussed the poten- in the sugarcane value chain. Ju et al. (2009) simulated l design of biomass to DME and their results suggested environmental performance is achieved compared to mbustion. Zhang, Solli, et al. (2009) performed exergy the biomass steam gasification to DME process. (2009), Li, Wang, et al. (2010) reported a pilot scale ion of corncob-derived syngas conversion to DME. egaard, et al. (2010) modeled and simulated two DME uding the once through and recycle configurations of c conversion of syngas from torrefied woody biomass. (2010) developed a MINLP to synthesize a DME and roduction process from biomass. The superstructure s natural gas and coal as feedstock options, but only elected in the case study. Chang et al. (2012) developed al and kinetic simulation of biomass to DME. studies investigated ethanol production via thermo- utes. De Kam et al. (2009) studied a biomass integrated combined cycle (BIGCC) system to incorporate heat roduction in dry-grind ethanol facilities. Kou and Zhao uated the environmental and economic performance chemical ethanol production process under extreme nditions. Cherubini and Jungmeier (2010) developed a t produces ethanol, electricity, heat, and chemicals and t, and Kenney (2007) highlighted the logistics of using iomass feedstocks for ethanol production. 2007) and Agrawal, Singh, Ribeiro, Delgass, and Perkis osed a hybrid thermochemical/biological process that ngas production and fermentation to produce biofuels. (2011) developed an integrated dry-grind ethanol sys- rmentation or catalytic mixed alcohol synthesis using as feedstock. on to a single BTL process, all biomass conversion routes, ermochemical, catalytic, and biochemical, can be inte- the biorefineries concept. The idea of an integrated concept is that multiple types of biomass feedstocks verted into various biofuels and biochemicals, allow- le approach to allocate the products depending on ints of the system (Cherubini, 2010a; Demirbas, 2010; den, Yuan, Cullinan, & Aksoy, 2007). Centi, Lanzafame, ner (2011) gave an overview of a second generation refinery and the socio-political impact it will have. t al. (2009) combined first and second generation bio- discussion. Kokossis and Yang (2010) highlighted that systems engineering approach plays a key role in the nt of efficient biorefineries. nd Bhatia (2008) discussed a catalytic technology based on palm oil, which includes biomass gasification its technology portfolio. Sammons et al. (2008) used a tems approach and combined process and economic r optimal biorefinery product allocation. Laser, Jin, et al. eloped seven process designs co-producing ethanol, ydrogen, methane, and power, and three processes one bi and op techno the ne model (2011) econom a fuzz conflic 2.3.3. Unl scale, c and its Tolis, & resour of BTL the lite found collect Searcy researc ply ch Karagi feature sion te Rav based ulation role of tion, a the en and Al ing bio States. ematic logistic McDon the Un maxim van Dy supply energy et al. (2 biofue The Parker facility States. ment c chain, et al. (2 ity loca supply portati to the from w wood factors consid ern Sw Whitta cal route. Bao et al. (2011) used a short-cut method zation-based approaches to synthesize and screen the al pathways and inefficient distribution flows over k. Sharma et al. (2011) used a MILP financial planning multi-product, multi-platform biorefinery. Tay et al. thesized a sustainable biorefinery with maximum erformance and minimal environmental impact via thematical programming model to account for the nature of the two main objectives. ture review—supply chain networks oal or natural gas, which are produced in a large- alized manner, biomass production is more distributed ply chain involves more logistical issues (Rentizelas, iopoulos, 2009). Thus, an optimal allocation of biomass ith minimum transportation cost is a crucial aspect nologies and studies on biomass supply chain exist in re. A review on biomass supply chain studies can be old and Seuring (2011), taking into consideration the torage, and distribution of biomass. An, Wilhelm, and 1a) reviewed the literature on biofuel supply chain assified in decision time frame and level in the sup- i.e., upstream, midstream, or downstream). Iakovou, is, Vlachos, Toka, and Malamakis (2010) reviewed the waste biomass supply chains and the potential conver- logies for heat and power production. t al. (2008) developed a biomass transportation system tton logistics and simulated using a discrete event sim- cedure. Uslu, Faaij, and Bergman (2008) evaluated the reatment technologies, such as torrefaction, pelletiza- rolysis, on bioenergy chains, particularly in increasing density of transported materials. Cundiff, Fike, Parrish, (2009) discussed the logistics constraints in develop- gy systems in the southeastern region of the United Bartle, Li, and Wu (2009) developed a discrete math- odel for mallee biomass production and transportation estern Australia. transportation logistics. Meehan and (2010) analyzed biomass availability and delivery for ity College Dublin under minimum land use impact, energy scenario, and minimum environmental impact. t al. (2010) developed a MILP approach for the biomass in, highlighting the change in biomass moisture and tent during storage for long-term processing. Huang ) formulated a multi-period optimization model for the ply chain. lity location problem is investigated for BTL plants. . (2010) performed resource assessment and biorefinery tion optimization for the western region of the United Mas et al. (2011) studied strategic planning and invest- ity planning under price uncertainty for ethanol supply se framework can be applied to other systems. Leduc ) developed a MILP problem to solve the methanol facil- problem for a case study in Austria, taking the biomass n into account. Leduc et al. (2009) considered a trans- odel to estimate the logistic demands of biomass supply and the supply to the gas station for methanol product gasification. Cost analysis, plant location and efficiency, and operating hours are among the most influential he fuel production cost. Leduc, Lundgren, et al. (2010) locations of methanol production plants for north- with demand scenarios up to 2025. Kim, Realff, Lee, et al. (2011) developed a mixed integer optimization 38 C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 model for the biomass supply chain network for the Southeastern region of the United States. The system consists of two conversion plants, the first one being a pyrolysis plant that produces biooil, char, and fuel gas, and a second gasification-based plant that pro- duces gasol and Lee (20 stochastic p using Mont Aksoy et on poultry l Tittmann, P ger linear, b size, and ty tem (GIS) t California. B chain plann cost. Bowlin chain optim and costs o ational cost planning of via first and echelon, sp environmen Addition thermochem and Naka ( planning pr bioethanol 2010) stud ethanol pro Bezzo, and a spatially cost and en (Zamboni, S ronmental o (2012) stud supply chai problem. Th residues is Leduc, S lignocellulo production regional re four-layer s processing performed Lakes base impacts on a MILP mod ply chain to The mathem sentation, w (2011b) ev fuel supply oped a gene facilities th et al. (2011 location and formulated Rentizel Tatsiopoulo an optimal generation (2009) eva Wales. Mar with linear optimisation for the design of a bioenergy chain. Panichelli and Gnansounou (2008) used a GIS-based decision support system to allocate forest wood residues to torrefaction plants for a gasification unit in a power plant. Zhang et al. (2011) ned biom mass large dolo tunit ds, a , Jab s, Tu , 201 ed in ques at m poli env gate red t ss pe e., m issi grass ss to ls. Xi tion the proc ainti ortat sive ted city, the e s, me for pua Asses mass GTL, const t pro mate o de rs (R nd 1 els (H g to ited $15 ume ove ted c to th ed re 00 ba litera ange type eet f ve h pro cost ine and biodiesel. The work is extended in Kim, Realff, 11) to consider uncertainty in a two stage mixed integer rogram, and the robustness of the solution is analyzed e Carlo simulation. al. (2008) determined the biorefinery locations based itter in Alabama so as to minimize transportation cost. arker, Hart, and Jenkins (2010) developed a mixed inte- iorefinery citing model that determines the location, pe of the biorefinery using geographic information sys- echnology for the spatial configurations in the state of ai et al. (2011) studied biorefinery location and supply ing under traffic congestion, minimizing transportation g et al. (2011) developed a facility location and supply ization for biorefineries, taking into account the sales f feedstocks, transportation cost, capital cost, and oper- . Giarola et al. (2011) studied the strategic design and corn grain and stover-based bioethanol supply chains second generation technologies. A multi-period, multi- atially explicit MILP model is developed to optimize the tal and financial performance simultaneously. ally, similar methods have been applied for non- ical conversion facilities. Ayoub, Martins, Wang, Seki, 2007) studied a two-level bioenergy decision system oblem. Dunnett et al. (2008) studied a lignocellulosic supply chain with pretreatment. Eksioglu et al. (2009, ied the design of a biomass supply chain system for duction from corn stover and woody biomass. Zamboni, Shah (2009), Zamboni, Shah, et al. (2009) investigated explicit approach formulated as a MILP to minimize vironmental impacts for corn based ethanol networks hah, et al., 2009) and formulated a multi-objective envi- ptimization (Zamboni, Bezzo, et al., 2009). Marvin et al. ied a biochemical-based cellulosic biomass-to-ethanol n for the Midwestern United States by solving a MILP e conversion technology used to convert agricultural dilute acid pretreatment and enzymatic hydrolysis. tarfelt, et al. (2010) addressed the optimal locations for sic ethanol facilities with combined heat and power . Cucek et al. (2010) developed a MILP approach for newable energy supply chains, taking into account a uperstructure, starting from harvesting, preparation, to the distribution of products. Hacatoglu et al. (2011) a feasibility study on bioenergy system for the Great d on lignocellulosic biomass with minimal adverse food and fiber production. Akgul et al. (2011) developed el for the optimal design of a corn-based bioethanol sup- minimize its total cost, applied to Northern Italy region. atical model incorporated a neighborhood flow repre- hich governed the delivery route of materials. An et al. aluated a lignocellulosic biofuel and petroleum-based chain for Central Texas. Gan and Smith (2011) devel- ric framework for the supply of biomass to conversion at produce electricity and cellulosic ethanol. Kocoloski ) discussed the impact of cellulosic ethanol refineries size on the price of product ethanol. The problem was as a MIP with an economic objective function. as, Tatsiopoulos, et al. (2009) and Rentizelas and s (2010) studied a hybrid optimization method to solve location problem for bioenergy (i.e., heat and power) facilities. Charlton, Elias, Fish, Fowler, and Gallagher luated the potential of biomass energy systems in ti and Gonzalez (2010) combined GIS spatial studies combi forest Bio into a metho Oppor metho Clarke Kypreo Bauen review techni tives th energy The investi compa bioma tion (i. GHG em switch bioma biofue produc studied tower uncert transp prehen calcula electri pared FT fuel the LCA and Pa 2.3.4. Bio CTL or under ity tha an esti plans t ing yea to spe BTL fu lookin the Un mately are ass The estima apply assum of 10,0 in the in the r on the flowsh will ha oxygen capital GIS-based method to identify feasible locations for ass to fuel facilities with transportation cost model. supply chain networks can also be incorporated r scale, regional, renewable energy planning. Several gies exist, such as the SWOT (Strengths, Weaknesses, ies, and Threats) analysis, expert opinion “Delphi” nd the MARKAL model (Borjesson & Ahlgren, 2010; lonski, Moran, Anandarajah, & Taylor, 2009; Gul, rton, & Barreto, 2009; Jablonski, Strachan, Brand, & 0; Terrados et al., 2009). These models, which are not this paper, incorporate the multiple decision analysis for multiple time horizons, as well as competing objec- ay exist within a region, to assist decisions on regional cies. ironmental benefits of biomass-based processes are d via life cycle analysis studies. Wahlund et al. (2004) he life cycle analysis for different processes, including lletization for electricity production and fuels produc- ethanol, DME, ethanol). Wu et al. (2006) analyzed the ons for different fuels, including FT diesel and DME from . Larson (2006) reviewed studies on LCA for different liquids systems, including first and second generation ao et al. (2009) studied the performance of biomethanol from rice straw in China. Dowaki and Genchi (2009) LCA of DME and methanol production via the BLUE ess. Monte carlo simulation is used to account for the es in the biomass preprocessing, moisture content, and ion distance. Hoefnagels et al. (2010) presented a com- LCA calculation for biofuel systems. Cherubini (2010b) GHG balances for a bioenergy system that produces heat, and biofuels. Joelsson and Gustavsson (2010) com- missions and oil use for the polygeneration system of thanol, and DME. Higo and Dowaki (2010) investigated DME production from various biomass species in Japan New Guinea. sment of state of the art approaches to liquids is not nearly as commercially developed as though a large amount of small facilities are currently ruction. For instance, Choren has developed a beta facil- duces 18 million liters of diesel per year (310 BPD) using d capital cost of 50 million euro (160,965 euro/BPD), and velop a 200 million litter plant (3450 BPD) in the com- udloff, 2007). Udhe is organizing the BioTFuel project 13 million euro over the next seven years to produce einritz-Adrian, 2010). In India, Bioleum Resources is convert multiple biomass feedstocks to liquid fuels. In States, BTL plants have been estimated to cost approxi- 0,000/BPD due largely to the small scale that the plants d to be operated (near 5000 BPD) (Kreutz et al., 2008). rall cost for a BTL plant is strongly dependent on the apacity of the plant. Note that the following conclusions e references under the BTL category in Table 2. Due to strictions in feedstock flow rate, small plant capacities rrels/day and lower have been thoroughly investigated ture. The break-even oil prices for these processes are of $100–150 per barrel of oil, and are highly dependent and price of biomass used in the study. A typical process or a BTL refinery is very similar to a CTL flowsheet, and igh costs of capital for syngas generation and cleaning, duction, and the FT reactors. This implies that the overall s for a BTL process may only be slightly higher than a C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 39 CTL process if biomass gasification can be proved commercially on a large scale and the necessary supplies of biomass feedstock are readily available. The other major component of the overall cost of a BTL process will be the purchase price of the biomass feedstock, which will h Note that th cost is asso storage in th has a near-z will emit 0. so a tax on fuels produ processes m The carb very similar Note that ca non-carbon shift reactio 2007; Balib have a high in the biom either leave can be utiliz bon conver BTL process of about 45 “once-throu implemente 3. Hybrid f The deve opens up op energy proc of energy r ing multipl products. M effects due For exampl stocks can r BTL process reduce the gas-based p natural gas, ity location centralized production Table 4 energy proc coal and bio uids (BGTL) processes, b and (b) ene contributio Section 2. Ta on the prod ucts from th feedstocks ether (DME hydrogen, L 3.1. Coal an The inte manifested combined CTL and GTL by injecting methane to the gasification reactor and reported a synergistic effect in producing syngas with H2:CO ratio close to 2, which is the stoichiometric requirement of the FT process. Sudiro and Bertucco (2007) coupled steam- ing o es s cien l gas use cycle issio nd e s. dies clud gate s fue ined . (20 with and d u et tura ynth t the e co perfo rly, o ion 2 logy the ms a a sy nol, a ions. to ove ed to gene e use al an Singl easin s, si ons c pro oduc lid w ratio prod o (20 nol s reut s is TL p on to here that lant, wer onfig fuel p sized ave a higher cost per unit energy than coal feedstocks. e economics of a BTL process are strongly effected if a ciated with any CO2 that is avoided. Due to the carbon e biomass, BTL processes will have a GHG lifecycle that ero net emissions. Typical petroleum-based processes 5 tons of CO2 per barrel of product (Kreutz et al., 2008), life-cycle GHG emissions could raise the cost of liquid ction from petroleum to levels that would make BTL ore competitive. on in the biomass that is converted to the liquid fuels is to coal processes, with a range of 25–35% being typical. rbon conversion ratios of near 100% can be achieved if a based source of H2 is input and the reverse water–gas- n is used to consume the process CO2 (Agrawal et al., an et al., 2010; Elia et al., 2010). Though the biomass will er H/C ratio than the coal feedstocks, the oxygen present ass will hinder liquid fuels production because it will the process as H2O or CO2. “Once-through” processes ed for electricity generation, though the fraction of car- ted will be lower. The lower-heating value efficiency of es is also very similar to the CTL figures, with a range –55%. The exact value will be dependent on whether a gh” or recycle configuration is used or whether CCS is d. eedstock energy processes lopment of the three single feedstock energy processes portunities to develop hybrid energy processes. Hybrid esses provide a compelling alternative in the portfolio esource production, due to their flexibility in convert- e types of feedstock inputs into a consistent range of oreover, hybrid energy systems can have synergistic to the combined advantages of each individual system. e, combining coal or natural gas with biomass as feed- educe the cost of fuel production compared to a pure . On the other hand, using biomass as a feedstock can GHG emissions compared to either a coal and natural rocess. In the supply chain analyses, combining coal, and biomass feedstocks will have an effect in the facil- s of hybrid energy facilities and trade offs between the production of coal and natural gas and the distributed of biomass can be investigated. summarizes the contributions for hybrid feedstock esses, namely the coal and natural gas to liquids (CGTL), mass to liquids (CBTL), natural gas and biomass to liq- , and coal, biomass, and natural gas to liquids (CBGTL) ased on a classification in (a) stand-alone processes, rgy supply chain system. Further classifications of the ns of each publication follow the description outlined in ble 5 organizes the contributions listed in Table 4 based ucts of the energy processes. The five main liquid prod- e thermochemically based, indirect liquefaction of the are gasoline, diesel, kerosene, methanol, and dimethyl ). Other products listed on the table (i.e., electricity, PG, etc.) are co-products of the reviewed processes. d natural gas to liquids (CGTL) gration of the CTL and GTL conceptual designs can be in several different configurations. Cao et al. (2008) reform that us mal effi natura (2009) CO2 re CO2 em sions a system Stu tems in investi DME a is obta Li et al system ether, Zho and na DME s to shif with th nomic Simila in Sect techno ratio in Ada ysis of metha condit is used for the extend tricity cells ar (CCS). 3.2. Co 3.2.1. Incr system emissi (1996) that pr ipal so configu either Sciazk metha In K proces and CB emissi atmosp found tion p to a po plant c tional empha f natural gas and steam-gasification of coal in a reactor olar energy as heat source. They found that the ther- cy of the hybrid process is higher than the coal-only or -only systems. In another process, Sudiro and Bertucco d separate gasification and reforming processes with to the gas reforming block and observed reduction in n from the CTL case. The hybrid energy system’s emis- conomic performances are in between the CTL and GTL that performed exergy analysis over hybrid CGTL sys- e Zhou et al. (2008) and Li et al. (2011). Zhou et al. (2008) d the co-feeding of coal and natural gas to co-produce l replacement and electricity. Higher exergy efficiency by the hybrid feeding and co-production of products. 11) conducted an exergoeconomic of a polygeneration coal and coke oven gas, producing methanol, dimethyl imethyl carbonate. al. (2009) combined the syngas from coal gasification l gas reforming to achieve the correct C to H ratio for esis, eliminating the need for a water gas shift reactor syngas composition. The hybrid process is compared al-only and natural gas-only systems in terms of eco- rmance, energy efficiency, and environmental impact. ne of the case studies in Peng et al. (1999), reviewed .2, involved the combination of natural gas reforming with coal gasification to achieve the correct H2 to CO feed composition. nd Barton (2011a) performed a techno-economic anal- stem that converts coal and natural gas to electricity, nd FT fuels under various carbon policies and market They proposed a strategy where natural gas reforming cool the gasifier, showing increased energy efficiency rall process. In Adams and Barton (2011b) the work is incorporate solid oxide fuel cells as the primary elec- rator. They found that it is most beneficial when the fuel d in conjunction with carbon capture and sequestration d biomass to liquids (CBTL) e stand-alone processes g attention is given to hybrid coal and biomass energy nce biomass incorporation can reduce the life cycle ompared to coal-only systems. Warren and El-Halwagi posed two configurations for a co-generation system es FT liquid fuel and hydrogen from coal and munic- aste, including cellulosic and plastic wastes. The two ns vary in the production route of hydrogen, which is uced from coal or from cellulosic waste. Chmielniak and 03) suggested co-gasification of biomass and coal for ynthesis. z et al. (2008), the combined coal and biomass (CBTL) highlighted out of the 16 configurations of CTL, BTL, rocesses because of its potential to have net zero GHG the atmosphere (i.e., when the release of CO2 to the is equal to CO2 in-take during photosynthesis). They the “once-through” configuration for the polygenera- where residual syngas from the FT synthesis is sent generating unit, is more profitable than the “recycle” uration, where the residual syngas is recycled for addi- roduction. Based on this report, Williams et al. (2009) the benefits of the polygeneration of liquid fuels and 40 C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 Table 4 Hybrid energy systems contributions on stand-alone processes and energy supply chain networks. The numerical figures refer to the reference list at the end of this paper. Component CGTL CBTL BGTL CBGTL Stand-alone systems Conceptual d Process simu Economic an Heat integra Power integ Water integ Process synt Life cycle an Sensitivity a Uncertainty Network-bas Supply chain electricity f In addition tem can po The work o (Liu, Larson of the poly that low-em be more pr would requ Larson e systems th biomass wi CBTL-CCS c taking a mix inputs, seve their CTL a esign Li, Liu, He, Wang, and Pistikopoulos (2011), Sudiro and Bertucco (2007), Sudiro and Bertucco (2009), Adams and Barton (2011a), Adams and Barton (2011b), Zhou et al. (2008), Zhou et al. (2009), Peng et al. (1999) Larson et al. (2010), Chen et al. (2011b), Chen et al. (2011a), Warren and El-Halwagi (1996), Liu, Larson, et al. (2011), Kreutz et al. (2008), Williams et al. (2011) lation Li et al. (2011), Sudiro and Bertucco (2007), Sudiro and Bertucco (2009), Adams and Barton (2011a), Adams and Barton (2011b), Zhou et al. (2008), Zhou et al. (2009), Peng et al. (1999) Larson et al. (2010), Chen et al. (2011b), Chen et al. (2011a), Warren and El-Halwagi (1996), Liu, Larson, et al. (2011), Kreutz et al. (2008), Williams et al. (2011) alysis Adams and Barton (2011a), Larson et al. (2010), Chen Adams and Barton (2011b), Zhou et al. (2008), Zhou et al. (2009) et al. (2011b), Chen et al. (2011a), Liu, Whitaker, Pistikopoulos, and Li (2011), Warren and El-Halwagi (1996), Liu, Larson, et al. (2011), Kreutz et al. (2008), Williams et al. (2011) tion Sudiro and Bertucco (2009), Adams and Barton (2011a), Adams and Barton (2011b), Zhou et al. (2009) Larson et al. (2010), Chen et al. (2011b), Chen et al. (2011a), Liu, Larson, et al. (2011), Kreutz et al. (2008), Williams et al. (2011) ration Adams and Barton (2011a), Adams and Barton (2011b), Zhou et al. (2009) Liu, Larson, et al. (2011), Kreutz et al. (2008), Williams et al. (2011) ration hesis Chen et al. (2011b), Chen et al. (2011a) alysis Larson et al. (2010), Liu, Larson, et al. (2011), Kreutz et al. (2008), Williams et al. (2011) nalysis Sudiro and Bertucco (2007), Zhou et al. (2009), Peng et al. (1999), Adams and Barton (2011a), Adams and Barton (2011b), Zhou et al. (2008), Liu, Whitaker, et al. (2011) Kreutz et al. (2008), Liu et al. (2011b), Williams et al. (2011), Chen et al. (2011b), Chen et al. (2011a), Larson et al. (2010) ed analyses Liu, Whitaker, et al. (2011) rom coal and biomass with carbon capture and storage. to reduced GHG emissions from biomass, the CCS sys- tentially result in a net-negative emission for the plant. f Kreutz et al. (2008) is followed up in a recent paper , et al., 2011) that highlights the economic advantage generation system. Additionally, their results suggest itting synfuels produced by the hybrid system would ofitable than the production of cellulosic ethanol and ire half as much lignocellulosic biomass. t al. (2010) and Larson, Fiorese, et al. (2009) studied at produce liquid fuels and electricity from coal and th CCS for an Illinois case study. Using the once-through onceptual design developed in Kreutz et al. (2008) and ture of switchgrass and mixed prairie grass as biomass ral case studies were completed and compared with nd BTL counterparts. Life cycle analysis and economic analysis we economical The CBTL performanc between fu tem is able high. On th but the fue et al. (2011 is an attrac Chen et tem that us chemicals, w ent scenari by solving uct prices, Liu et al. (2011b), Borgwardt (1997), Li, Hong, Jin, and Cai (2010), Dong and Steinberg (1997) Baliban et al. (2010), Baliban, Elia, and Floudas (2011), Baliban, Elia, and Floudas (2012) Liu et al. (2011b), Borgwardt (1997), Li, Hong, et al. (2010), Dong and Steinberg (1997) Baliban et al. (2010) Liu et al. (2011b), Baliban et al. (2010), Borgwardt (1997), Dong and Steinberg (1997) Baliban et al. (2011), Baliban et al. (2012) Liu et al. (2011b), Dong and Steinberg (1997) Elia, Baliban, and Floudas (2010), Baliban et al. (2011), Baliban et al. (2012) Liu et al. (2011b), Li, Hong, et al. (2010) Elia et al. (2010), Baliban et al. (2011), Baliban et al. (2012) Baliban et al. (2012) Baliban et al. (2011), Baliban et al. (2012) Liu et al. (2011b) Elia, Baliban, Xiao, and Floudas (2011), Baliban et al. (2012) Liu et al. (2011b), Li, Hong, et al. (2010) Baliban et al. (2010), Baliban et al. (2011), Baliban et al. (2012), Elia et al. (2011) Elia et al. (2011) re completed, and the CBTL system was able to compete ly with the CTL and BTL systems with zero emissions. system balances the economic and environmental es, serving as the middle ground in the trade-off el prices and environmental impact. While the CTL sys- to generate fuels at lower costs, its emission values are e contrary, the BTL system has net-negative emissions, l cost is high due to higher feedstock prices. Williams ) suggested that the CBTL-CCS polygeneration system tive alternative to decarbonize coal power plants. al. (2011b) developed an energy polygeneration sys- es coal and biomass to produce power, liquid fuels, and hose performances were studied in detail under differ- os of market prices. The optimal design is determined a MINLP problem under different feedstock and prod- and potential carbon policy scenarios. To address the C.A . Floudas et al. / Com puters and Chem ical Engineering 41 (2012) 24– 51 41 Table 5 Hybrid energy process systems organized by product type. Products CGTL CBTL BGTL CBGTL Gasoline Sudiro and Bertucco (2007), Sudiro and Bertucco (2009), Adams and Barton (2011a), Adams and Barton (2011b) Williams et al. (2009), Larson et al. (2010), Larson, Fiorese, et al. (2009), Chen et al. (2011b), Liu et al. (2011b) Baliban et al. (2010), Elia et al. (2010), Baliban et al. (2011), Baliban et al. (2012), Chen et al. (2011a), Warren and El-Halwagi (1996), Liu, Whitaker, et al. (2011), Liu, Larson, et al. (2011), Elia et al. (2011) Kreutz et al. (2008), Williams et al. (2011) Diesel Sudiro and Bertucco (2007), Sudiro and Bertucco (2009), Adams and Barton (2011a), Adams and Barton (2011b) Williams et al. (2009), Larson et al. (2010), Larson, Fiorese, et al. (2009), Chen et al. (2011b), Liu et al. (2011b) Baliban et al. (2010), Elia et al. (2010), Baliban et al. (2011), Baliban et al. (2012), Chen et al. (2011a), Warren and El-Halwagi (1996), Liu, Whitaker, et al. (2011), Liu, Larson, et al. (2011), Elia et al. (2011) Kreutz et al. (2008), Williams et al. (2011) Kerosene Warren and El-Halwagi (1996), Liu, Larson, et al. (2011), Kreutz et al. (2008), Folkedahl, Snyder, Strege, and Bjorgaard (2011) Baliban et al. (2010), Elia et al. (2010), Baliban et al. (2011), Baliban et al. (2012), Elia et al. (2011) Methanol Li et al. (2011), Adams and Barton (2011a), Adams and Barton (2011b) Chen et al. (2011b), Chen et al. (2011a), Chmielniak and Sciazko (2003) Borgwardt (1997), Li, Hong, et al. (2010), Dong and Steinberg (1997), Clausen, Houbak, et al. (2010) Dimethyl ether Li et al. (2011), Zhou et al. (2008), Zhou et al. (2009), Peng et al. (1999) Dimethyl carbonatea Li et al. (2011) LPGa Sudiro and Bertucco (2007), Sudiro and Bertucco (2009) Electricitya Adams and Barton (2011a), Adams and Barton (2011b), Zhou et al. (2008), Zhou et al. (2009) Williams et al. (2009), Larson et al. (2010), Larson, Fiorese, et al. (2009), Chen et al. (2011b), Liu et al. (2011b), Li, Hong, et al. (2010) Baliban et al. (2011), Baliban et al. (2012) Chen et al. (2011a), Liu, Whitaker, et al. (2011), Liu, Larson, et al. (2011), Elia et al. (2011) Kreutz et al. (2008), Williams et al. (2011), Chmielniak and Sciazko (2003) Hydrogena Warren and El-Halwagi (1996) a Dimethyl carbonate, LPG (liquefied petroleum gas), electricity, and hydrogen are byproducts of the processes in the cited literatures and are not reviewed in this paper. 42 C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 changes in the study is et al. (2011 solving a tw flexible poly that their st Experim process ha (2008) repo petroleum (2011) dev an iron-bas thetic isopa of military- 3.2.2. Supp An energ is studied in of certain ch mixed integ tigated the UK, identify high profita 3.3. Natura Hybrid b studied as e Fig. 4. Coal, biomass, and natural gas to liquids process flowsheet (reprin prices and fluctuations throughout the plant lifetime, extended to a flexible polygeneration system in Chen a). Design and operational decisions are obtained by o-stage programing problem, and it is found that the generation systems generate higher net present values atic counterparts. ental works that demonstrate the viability of the CBTL ve also been pursued. Ahmaruzzaman and Sharma rted experimental results of liquid fuels derived from vacuum residues, coal, and biomass. Folkedahl et al. eloped and demonstrated the CBTL technology with ed FT catalyst, whose products are upgraded to syn- raffinic kerosene that closely meets the specifications grade jet fuel. ly chain networks y supply chain network consideration for CBTL systems Liu, Whitaker, et al. (2011), where the optimal locations emical production centers are determined via solving a er optimization problem. The outlined case study inves- optimal strategic planning for CBTL technologies in the ing the optimal locations for these plants that result in bility over the entire planning horizon. l gas and biomass to liquids (BGTL) iomass and natural gas energy systems have not been xtensively as the aforementioned systems. Borgwardt (1997) prop methanol p gration of b the produc a method reduced CO of biomass, feedstock, a recycle of th so that no o Li, Hong ral gas and that produc ratio, the g shift reacto feeding bio system. In C 2.3, the pla gasification for catalyti cost. Liu et al biomass sy which CO2 the develop compared t found that other syste ted from Baliban, Elia, & Floudas, 2010). osed a natural gas and biomass co-feeding system for roduction, intended for fuel cell applications. The inte- iomass can reduce pollution in both the process and ts consumption. Dong and Steinberg (1997) proposed called the Hynol process to produce methanol with 2 emission. The process consists of hydrogasification steam reforming of the produced gas and natural gas nd methanol synthesis. Hydrogasification refers to the e H2 rich gas after methanol synthesis into the gasifier xygen is needed to maintain the reactor temperature. , et al. (2010) took advantage of hydrogen-rich natu- carbon-rich biomass to design a polygeneration system es methanol and electricity. By adjusting the feedstock as-shifting process that usually is done by a water gas r can be eliminated without cost. They found that co- mass and natural gas can reduce material inputs into the lausen, Houbak, et al. (2010), also reviewed in Section nt configuration that combines electrolysis of water, of biomass, and autothermal reforming of natural gas c methanol synthesis yielded the lowest production . (2011b) investigated two designs of natural gas and stems that co-produce electricity and FT fuels, one in is vented and the other captured. The design follows ed processes in Kreutz et al. (2008) closely, and when o the plant configurations in Kreutz et al. (2008) they the performance of the BGTL system out-competes all ms, except for the CBTL case. C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 43 3.4. Coal, biomass, and natural gas to liquids (CBGTL) 3.4.1. Single stand-alone processes Floudas and co-workers have proposed in a series of papers novel hybr systems (Fi duce gasol ratio for th The studies 2010; Elia in Aspen P mathematic taneous he (Baliban et expanded tive conver inputs to t et al., 2011 tive process operating c process syn plant topol structure is heat and p waste heat A MINLP m tion is perfo step. This MIN also include tion of an e freshwater structure o biological d process wa gases and or for the hea this superst taneous he on a total o coal feedsto and two pl sequestrati 3.4.2. Supp In Elia et tigated to the entire coal, bioma work was facilities in duction. Th and ends a designs wer ses based on combinatio compositio the layout o locations, ex ciated with that the CB demand wit cesses and et al., 2011) 4. Research groups contributions Table 6 presents a representative list of the research contribu- tions on single feedstock and hybrid feedstock energy processes zed b ure c ed o and brid id f id fe d on ess s ent h Addi red, ces. pro hav m ca port on a r in on ap hybr tion m de ase i estic r usa ation vatio lem v ly ch sis a gy pr ral ga Table requ e fee luci and Simi offs esou ity l poin , res syst on. S sis f gies r ma n tha affec egic e fee rtun ch sy idere hybr re sp id coal, biomass, and natural gas to liquid (CBGTL) g. 4 reprinted from Baliban et al. (2010)) that pro- ine, diesel, and kerosene according to the demand e US, motivated by the work of Agrawal et al. (2007). on this new hybrid CBGTL process (Baliban et al., et al., 2010) outline the initial process development lus, along with a novel gasifier stoichiometric-based al model, a detailed economic analysis, and simul- at and power integration for 7 different case studies al., 2010; Elia et al., 2010). The initial design is into a CBGTL superstructure that includes alterna- sion routes from the coal, biomass, and natural gas he gasoline, diesel, and kerosene products (Baliban ). The superstructure incorporates multiple alterna- units, stream interconnectivities, and a variety of unit onditions. Mixed integer nonlinear optimization-based thesis methods are employed to identify the optimal ogy under different scenarios. The complete super- modeled mathematically, including a simultaneous ower integration that utilize heat engines to recover from the process and produce steam and electricity. odel is formulated and the heat and power integra- rmed simultaneously along with the process synthesis LP model is further extended in Baliban et al. (2012) to a complete wastewater network and the determina- fficient wastewater treatment network that minimizes consumption and wastewater discharge. A super- f a wastewater network is postulated that includes a igester and a sour stripper as options for treatment of ter streams that can have high concentrations of acidic ganic species and a cooling water and steam cycles uses t and power integration. The mathematical model for ructure forms a process synthesis problem with simul- at, power, and water integration. The model is tested f 108 case studies which consist of combinations of six cks, three biomass feedstocks, three plant capacities, ant superstructures, namely with and without carbon on. ly chain networks al. (2011), the supply chain network problem is inves- explore the potential of the CBGTL process to fulfill United States transportation demand using domestic ss, and natural gas sources. An optimization frame- proposed to identify the optimal locations of CBGTL the United States that yield the lowest cost of fuel pro- e supply chain starts at the feedstock source locations t the demand locations. The considered CBGTL plant e obtained from the Aspen Plus simulations and analy- Baliban et al. (2010) and Elia et al. (2010), using distinct ns of 6 coal species, 15 biomass species, 1 natural gas n, and 3 plant capacities. The model takes into account f feedstock availabilities in the United States, demand isting transportation infrastructure, and the costs asso- each segment of the energy supply chain. It was shown GTL process can fulfill the United States transportation h costs that are competitive with petroleum-based pro- significant reduction in greenhouse gas emissions (Elia . organi 5. Fut Bas lenges and hy • Hybr hybr Base proc effici gies. explo sour • BGTL tems syste trans lizati • Wate grati and sump syste incre dom wate utiliz culti prob • Supp analy ener natu (see then of th will e gas, mal. trade gas r • Facil have tems such erati analy nolo othe ratio will • Strat singl oppo of ea cons and Whe y research groups worldwide. hallenges and opportunities n the studies present in literature, the following chal- opportunities are highlighted for the single feedstock feedstocks energy processes: eedstock energy processes. Further development of edstock energy processes can be pursued in the future. the current simulation studies, optimization based ynthesis approaches are important tools in designing ybrid systems with multiple feedstocks and technolo- tional integration with other renewable sources can be such as electricity or hydrogen from wind and solar cess. The combined natural gas and biomass energy sys- e not been extensively studied. This particular hybrid n potentially be useful, especially for the United States ation sector, due to the growing interest in biomass uti- nd abundant natural gas reserves in the country. tegration. Few works have considered the water inte- proach in the development of both the single feedstock id feedstocks energy processes to minimize the con- of freshwater. This area is an important aspect of energy signs since pressure on water resources is projected to n the future as population and the consumption from , agricultural, and industrial usage increase. Efficient ge in energy processes is necessary, especially since the biomass will also increase water consumption for its n. Process systems engineering is able to address this ia optimization-based wastewater network synthesis. ain and facility location problem. The supply chain nd the facility location problem for the hybrid feedstock ocesses need to be further developed. Although coal and s supply chain problems are less relevant than biomass 2), the incorporation of biomass in energy systems will ire a consideration of the availability and distribution dstock. The CBTL and BGTL facility location problems date the areas where the plant distances to coal, natural biomass sources, as well as the fuel market are opti- larly for the CGTL process, insights can be gained on the between placing facilities close to the coal or natural rces. ocation of polygeneration systems. As many studies ted out the economic benefit of polygeneration sys- earchers can explore the facility location problem for ems with multiple market sectors taken into consid- o far, most studies have performed the supply chain or the fuel market. However, when the conversion tech- also co-produce electricity, heat, hydrogen, etc., these rkets, which may have different geographical configu- n the fuel market, need to be taken into account and t the optimal locations of the polygeneration plants. planning. The strategic planning problem for both the dstock and the hybrid feedstocks energy processes is an ity for researchers to investigate the long-term viability stem. Either one type or multiple type of plants can be d (i.e., a network in which a combination of the single id systems is included) for a certain region or country. ecific geographical locations are considered, different 44 C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 Table 6 Hybrid energy systems contributions on complete stand-alone processes and energy supply chain networks organized by research groups. Groups Affiliations Country CTL GTL BTL CGTL CBTL BGTL CBGTL Agrawal et al. Purdue University USA X X Barton et al. Massachusetts Institute USA X X X of Technology Bertucco et al. University of Padova Italy X X X Bezzo et al. University of Padova Italy X Borgwards US Environmental USA X Protection Agency Bridgwater et al. Aston University UK X Brown et al. Iowa State University USA X Chang et al. South China University China X of Technology Cherubini, F. Norwegian University of Norway X Science and Technology Clausen et al. The Technical University Denmark X of Denmark Cocco et al. University of Cagliari Italy X Cundiff et al. Virginia Polytechnic Institute USA X and State University Daoutidis et al. University of Minnesota USA X Dooley et al. Pacific Northwest USA X National Laboratory Dowaki et al. Mid-Sweden University Sweden X Eden et al. Auburn University USA X Eksioglu et al. Mississippi State University USA X El-Halwagi et al. Texas A&M University USA X X X Faaij et al. Utrecht University The Netherlands X X X Fan et al. University of California, Davis USA X Floudas et al. Princeton University USA X X X Gao et al. Chinese Academy of Sciences China X X Gorgens et al. Stellenbosch University South Africa X Grossmann et al. Carnegie Mellon University USA X Gustavsson et al. Mid-Sweden University Sweden X Hall Texas A&M University USA X Han et al. Seoul National University South Korea X Hansson et al. Swedish University of Sweden X Agricultural Sciences Hao et al. Chinese Academy of Sciences China X Henrich et al. Karlsruhe Institute Germany X of Technology Hu et al. Tsinghua University China X X X Huffman University of Kentucky USA X Jaramillo et al. Carnegie Mellon University USA X X Jin et al. Chinese Academy of Sciences China X X Jun et al. Korea Institute South Korea X of Energy Research Karimi et al. National University of Singapore Singapore X Kjelstrup et al. Norwegian University of Norway X Science and Technology Kokossis et al. National Technical University Greece X of Athens Kreutz et al. Princeton University USA X X X X X Kumar et al. University of Alberta Canada X Larson et al. Princeton University USA X X X X X Leduc et al. Lulea University of Technology Sweden X Li et al. Chinese Academy of Sciences China X Liu et al. Tsinghua University China X X X Lynd et al. Dartmouth College USA X Manganaro et al. Stevens Institute of Technology USA X Marechal et al. Ecole Polytechnique Fédérale Switzerland X de Lausanne Minowa et al. National Institute Japan X at Advanced Industrial Science and Technology Ouyang et al. University of Illinois USA X at Urbana-Champaign Papageorgiou et al. University College London UK X Parker et al. University of California, Davis USA X Perales et al. Universidad de Sevilla Spain X Pistikopoulos et al. Imperial College London UK X X X Ptasinski et al. Eindhover University The Netherlands X of Technology Realff et al. Georgia Institute USA X of Technology Renó et al. Federal University of Itajubá Brazil X Romagnoli et al. Louisiana State University USA X Rubin et al. Carnegie Mellon University USA X C.A. Floudas et al. / Computers and Chemical Engineering 41 (2012) 24– 51 45 Table 6 (Continued ) Groups Affiliations Country CTL GTL BTL CGTL CBTL BGTL CBGTL Sadhukkan et al. University of Manchester UK X Shah et al. Imperial College London UK X Song et al. X Steinberg et Tatsiopoulos Tiffany et al. van der Berg X Weidou et a Wilhelm et a Williams et X Wu et al. Wu et al. Xiao et al. Ying et al. Yoon et al. Zhao et al. Zheng et al. Zwart et al. Government Black et al. X Tarka et al. Reed et al. Phillips et al Jones et al. – sites will energy sy from the the proble and suppl • Uncertain take unce account t the future processes stand-alo chain netw opportun cesses. 6. Conclus This revi stock and h liquefaction mediates, a network-ba main produ and DME, w LPG. Future ment of sta supply chai the future e Acknowled The auth National Sc nces T. A., I rming T. A., rming Seoul National University South Korea al. Brookhaven National USA Laboratory et al. National Technical University Greece of Athens University of Minnesota USA et al. University of Twente The Netherlands X l. Tsinghua University China X l. Texas A&M University USA al. Princeton University USA X Argonne National Laboratory USA Curtin University Australia of Technology Southeast University China East China University of China X Science and Technology Seoul National University South Korea Purdue University USA Tsinghua University China X Energy Research Centre The Netherlands of The Netherlands (ECN) reports National Energy USA X Technology Laboratory National Energy USA X Technology Laboratory National Energy USA X Technology Laboratory . National Renewable USA Energy Laboratory Pacific Northwest USA National Laboratory Bechtel USA X have different resource profiles. Thus, the design of the stem suitable for a particular location will be distinct others. Uncertainties can further be incorporated into m to account for variety and fluctuations in any process y chain parameters. Refere Adams, refo Adams, refo ty. For long-term energy planning, it is important to rtainties in demand, supply, and price parameters into o assure the robustness of the planning solutions for . Uncertainty considerations for both single feedstock and hybrid feedstock processes, for (i) the design of ne processes, and (ii) the design of an energy supply orks have not been investigated extensively, providing ities for the advancement of research in energy pro- ions ew paper has outlined the contributions for single feed- ybrid feedstocks energy systems, based on indirect of coal, natural gas, and biomass via syngas inter- nd includes studies on the stand-alone systems and sed supply chain and facility location analyses. 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Hybrid and single feedstock energy processes for liquid transportation fuels: A critical review 1 Introduction 2 Single feedstock energy processes 2.1 Coal to liquids (CTL) 2.1.1 Process description 2.1.2 Literature review 2.1.3 Assessment of state of the art approaches 2.2 Gas to liquids (GTL) 2.2.1 Process description 2.2.2 Literature review 2.2.3 Assessment of state of the art approaches 2.3 Biomass to liquids (BTL) 2.3.1 Process description 2.3.2 Literature review—stand alone processes 2.3.3 Literature review—supply chain networks 2.3.4 Assessment of state of the art approaches 3 Hybrid feedstock energy processes 3.1 Coal and natural gas to liquids (CGTL) 3.2 Coal and biomass to liquids (CBTL) 3.2.1 Single stand-alone processes 3.2.2 Supply chain networks 3.3 Natural gas and biomass to liquids (BGTL) 3.4 Coal, biomass, and natural gas to liquids (CBGTL) 3.4.1 Single stand-alone processes 3.4.2 Supply chain networks 4 Research groups contributions 5 Future challenges and opportunities 6 Conclusions Acknowledgement References