Evaluation of Techno-Economic Studies on the Bioliq® Process for Synthetic Fuels Production from Biomass

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Article Evaluation of Techno-Economic Studies on the bioliq® Process for Synthetic Fuels Production from Biomass

Nicolaus Dahmen * and Jörg Sauer

Karlsruhe Institute of Technology, Institute of Catalysis Research and Technology (IKFT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany; [email protected] * Correspondence: [email protected]

Abstract: Techno-economic studies by various research institutions on the costs for the production of biomass to liquid (BtL) fuels using the bioliq® process were analyzed and evaluated. The bioliq® process consists of decentralized pretreatment by fast pyrolysis plants for biomass energy densification, and of a central gasification and synthesis step for synthesis of gas and synthetic fuel production. For comparison, specific material and energy flows were worked out for both process steps, and conversion efficiencies were calculated for the conversion of straw to diesel fuel via the Fischer-Tropsch synthesis. A significant variation of the overall process efficiency in the range of 33–46% was mainly a result of the different assumptions made for electricity generation at the central location. After breaking down the individual cost items to either fixed or variable costs, it turned out that the largest cost items in the production of BtL fuels were attributable to feedstock and capital costs. Comparison of the specific investments showed that, in addition to economies of scale, other factors had a significant influence leading to values between 1000 and 5000 EUR/kW. This,

particularly, included the origin of the equipment purchase costs and the factors applied to them. Fuel production costs were found to range between 0.8 and 2.6 EUR/L. Possible cost reduction by

Citation: Dahmen, N.; Sauer, J. learning potential was investigated, leading to an improvement by a few percent of production costs. Evaluation of Techno-Economic A sensitivity analysis of the individual cost items by up to 30%, for “investments” and “biomass Studies on the bioliq® Process for and transport” cost increases, led to higher manufacturing costs of up to 17% in both cases. By Synthetic Fuels Production from harmonizing the depreciation period and the chosen interest rate, the production costs changed Biomass. Processes 2021, 9, 684. from −16% to +17%. Similarly, effects could be shown by adjusting the costs for maintenance and https://doi.org/10.3390/pr9040684 servicing, and the plant operation time. A superposition of these effects in a best-case scenario led to cost reductions of 21%. The most expensive variant in the opposing worst-case scenario raised costs Academic Editor: Juan by up to 27%. This uncertainty contributed already ﬁfty percent to a preliminary cost estimate based Francisco García Martín on a conceptual design.

Received: 31 January 2021 Keywords: technoeconomic evaluation; bioliq® process; biomass-to-liquids; efﬁciency; produc- Accepted: 7 April 2021 tion cost Published: 13 April 2021

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in 1. Introduction published maps and institutional affil- iations. Biomass-to-liquid (BtL) processes for the production of synthetic fuels are relatively recent technologies that are still under development. In these processes, lignocellulosic biomass, organic residues and waste are converted by thermochemical processes into synthesis gas, which is catalytically reacted to form different types of synthetic fuels as the main products [1–4]. Compared to the commercially established gas-to-liquids and coal- Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. to-liquid processes, the challenges for BtL development address mostly to the upstream This article is an open access article part, converting a variety of biomass feedstock to clean syngas considering the specifics of distributed under the terms and feedstock availability and supply. These fuels produced are similar to those in use today, conditions of the Creative Commons and it is expected that they can replace a part of the fossil fuels required for existing vehicle Attribution (CC BY) license (https:// fleets in the short term. Easier adjustment to new developments in internal combustion creativecommons.org/licenses/by/ engines and compliance with new regulations are expected as a result of modified processes 4.0/). or new synthetic products. Essentially, many different carbon sources may be converted

Processes 2021, 9, 684. https://doi.org/10.3390/pr9040684 https://www.mdpi.com/journal/processes Processes 2021, 9, 684 2 of 23

into syngas mixtures of carbon monoxide, hydrogen and methane. Purpose-grown biomass, biomass by-products from agriculture, forestry and food processing, as well as organic waste (including plastics) can be used with different technology pathways and process configurations. Also, carbon dioxide together with hydrogen, produced from electrolysis of water using renewable electrical power, can be converted into syngas and to synthetic fuels (PtL, power-to-liquids). Even a combination of BtL and PtL is possible, making use of the CO2 by-produced in thermochemical processes. Thus, nearly complete use of the feedstock carbon in synthetic processes is possible [5]. The potential of sustainable biomass residues is limited, and its use for bioenergy is subject to controversial discussion. However, synthetic BtL fuels may have significant potential as renewable fuels for early implementation on the way to defossilized transportation. The development of BtL processes has advanced significantly in the last decades. Commercial application has failed, so far, because of missing competitiveness to conven- tional fuels. In Table1, a selection of pilot and demonstration plants with Technology Readiness Levels (TRL) between 6 and 8 for the conversion of lignocellulosic biomass into different types of synthetic fuels via gasification pathways are listed, providing evidence as to the range of technologies, conversion capacities and state of development. Due to the importance of Scandinavian wood industries, the earliest efforts can be observed in this region. Many more examples of synthesis gas trains are given in the global database of biomass conversion facilities of IEA Bioenergy (www.ieabioenergy.com/installations, accessed on 12 April 2021), including advanced biofuels, combustion, gasification and pyrolysis plants. The examples given in the table show the multitude of options to set up a process chain regarding feedstock, pretreatment, gasification and synthesis technology. In recent years, therefore, a large number of studies and reports of the various process developers and researchers relating to BtL, and other advanced biofuel production processes, have emerged. In addition to the possible advances of the various novel technology approaches for processing biomass, entire process chains with their economic and environmental aspects have been assessed [6–9]. These studies include various gasification- based fuels derived from chemical-catalytic and biocatalytic syngas conversion as well as products from pyrolytic and solvolytic processes, which are catalytically upgraded directly to fuels or to intermediates for refinery coprocessing. Technoeconomic studies are a valuable tool to estimate the economic performance of a process in the early state of its development and to compare it to established processes and products, as well as to alternative developments. A number of comparative studies on alternative BtL pathways exist. They mainly differ in gasification technology and syngas product. Stahlschmidt in 2010 compared different technologies for the production of methanol, one and two-step production of dimethyl ether, as well as of gasoline, Fischer-Tropsch hydrocarbons, and SNG (substitute natural gas) products [10]. Based on AspenPlus simulations for a gasifier with a thermal fuel capacity of 200 MW, the overall conversion efficiencies varied between 18.4 and 65.5% with specific production costs ranging between 15.5 and 40.6 ctEUR/kWh. The most sensitive parameter were the fuel supply costs. The technoeconomic evaluation by Dimitriou et al., in particular, examined and quantified the effect of uncertainty of key parameters on the fuel production costs when using Monte Carlo methods [7]. Produc- tion costs were found to be within 6.4–9.1 ctEUR/kWh of produced fuel. Interestingly, the uncertainty analysis indicated that deterministic estimates or sensitivity analyses of production costs may systematically underestimate the production cost of biofuels, as they do not account for the effect of simultaneous variations of parameters. Considering this effect, one of the six investigated scenarios even turned out to be economic within a certain probability. Processes 2021, 9, 684 3 of 23

Table 1. Selected pilot or demo projects for biomass to liquid (BtL) processes.

No. Project Feedstock Key Technologies Products Black liquor, Chemrec process with EFG, 2 MW, 30 bar, A LTU Green Fuels, S DME, methanol pyrolysis oil 4 t/d production Güssing Renewable Forest Repotec fast internally circulating CHP, SNG (1 MW), FT plant (slip B Energy Multifuel biomass and fluidized bed (FICFB), 8 MW, atm. stream) Gasification, A others pressure CFB, 12 MW Foster Wheeler, hot filtration, NSE Biofuels Oy, Forest Heat for a lime kiln, FT-products C catalytic tar reforming, (5 MW for synfuel Varkaus, S biomass (slip stream) application) Lignocellulose Fast pyrolysis 2 MW + 5 MW entrained D bioliq®, KIT, D MeOH/DME, gasoline bio-slurries flow gasification, hot gas cleaning, 80 bar Goteborg Energie AB, Forest metso/repotec Dual bed, 20 MW E Biomethane GoBiGas, S biomass (50.000 t/y SNG) Forest Torrefaction + Uhde Prenflow EF, 15 MW, F Total, BioTfuel demo, F FT-products biomass 8000 t/d FT product Forest G Värmlandsmetanol, S Uhde-HTW gasifier, 111 MW Methanol biomass Växjö Värnamo Forest Foster Wheeler pressurized CFB + hot gas H Biomass Gasification Heat & power, clean syngas biomass filtering Centre, S Woodland Biomass Research Center, LLC Thermal Forest Dual fluidized bed gasifier, 5 t/d waste I FT products Reformer Synthesis biomass wood, West BiofuelsWoodland, CA Red Rock Biofuels, Forest J Gasification, 44.0000 t/y FT liquids FT products Oregon, US biomass

The influence of plant size and constellation was also investigated. Regarding biomass, economies of scale always compete with biomass transportation costs, i.e., economies of logistics, which consequently lead to an optimum in the specific production cost vs. conversion capacity curve for biomass conversion plants [11]. To avoid long transportation distances, particularly for biomass with low energy density, decentralized pretreatment plants could be located in regions with large feedstock potentials. The so-produced intermediate energy carriers are more transport-worthy concerning their energy density and handling, enabling cheaper transport over long distances to a central plant for efficient further conversion and upgrading to desired transportation fuel. Depending on the desired conversion capacities, type of feedstock and supply chain, different pretreatment processes may be recommended with increasing draw areas to provide low feedstock supply costs. This was, for example, validated in simulations utilizing forest materials and switch grass both by thermochemical and mechanical pretreatment in North American regions [9,12]. In addition, mixed feedstock would provide further potential of reduced supply costs [13,14]. Dedicated studies on the effect of conversion capacity on the productions costs of BtL fuels were also conducted e.g., by R. Stahlschmidt et al. [10] or by L. Leible in [15], who investigated biomass residue potential in one of the federal states in Germany and derived and compared different plant configurations and sizes. In the FP7 BioBoost project, opti- mized production costs of a plant network of decentralized fast pyrolysis (FP) and central gasification/synthesis plants were calculated including a model for biomass logistics [16]. The EU-wide simulation of the FP pathway implementation showed a straw utilization of 52 Mt/a for biofuel production, meaning an average utilization share per region of 35%. This resulted in the production of 5.5 Mt of biofuel per year. The regions with the Processes 2021, 9, 684 4 of 23

highest straw production were found in France, Spain and in the East of Europe. In the optimum simulation scenario, 137 straw FP plants were erected, the capacities of which varied between “small” (<200,000 t/a) and “very large” (>350,000 t/a). Approximately 10 times less biosyncrude plants were utilized with correspondingly 5–10 times larger conversion capacities when compared to the FP plants. These results confirm the possible benefits of the local-central principle, where an intermediate is locally produced and centrally converted to final products. Gunukula et al. investigated the effect of different pretreatment technologies for biomass densification by pelletization and torrefaction, including the necessary grinding and drying steps, for both wood and switch grass prior to FP with further upgrade to hydrocarbon fuels by hydrotreating [17]. The 500 t/d pretreatment plants each supplied a four-times larger fast pyrolysis plant. Generally, minimum fuel selling prices increased when thermochemical pretreatment was implemented. The most sensitive parameter was the product yield, making it obvious that, in the case of fast pyrolysis, not only bio-oil but also the char product should be utilized for fuel production within a gasification pathway. This was confirmed in the study of Brown et al. [18] where biofuel production via pyrolysis bio-oil/char slurry showed better performance than only the use of bio-oil. In that study, woody material was processed in mobile pretreatment units, further reducing biomass transportation costs. Optimum conversion capacities were found in the range of 2250–2750 t/d, for which specific production costs of 1.68, 1.55 and 1.35 EUR/L were estimated to convert bio-oil, bioslurry and torrefied wood into Fischer- Tropsch fuels. Alternative to the gasification pathway, bio-oil may also processed to fuels or fuel components by conversion via catalytic deoxygenation, hydrogenation, cracking and related reactions. Here, TRL is less than that of BtL processes today and early-stage technoeconomic evaluations reveal specific production costs in the same range as for BtL fuels, either as stand-alone plants or in coprocessing in a refinery. Instead of using the solid pyrolysis product for heat generation, it may be utilized for hydrogen production in self-sustaining stand-alone plant concepts [19–21]. However, as most of the BtL processes are still in the pilot or demo phase (TRL 6-8), and not much experience from a large-scale industrial plant is available for the key technologies, these studies are based on numerous assumptions to enable calculation of the specific cost of fuel production. That way, differences and uncertainties in selection and pricing of equipment, estimation of the material and energy flows, as well as the economic model and parameters, may occur. Consequently, this means that divergent results may be obtained if the economic performance of a process is investigated by different investigators. Therefore, in this work, different studies on the same process were conducted in order to evaluate the type, extension and sensitivity of uncertainties which may arise using the example of the bioliq® process developed at KIT. This particular process appeared as a useful example, because it was examined in several studies. Since nearly all of them made use of the same technical information, it could be expected that differences in the estimated production costs were mainly due to cost estimates and economic factors. With one exception, biomass logistics were not taken into account, reducing complexity for upstream costing by simply assuming feedstock costs at the plant gate. From the results obtained in this study, it was expected that they could be transferred to other process developments and some general conclusions may, therefore, be drawn in view of more reliable technoeconomic evaluations. For the bioliq® process, fuel production costs (minimum selling price) between 0.80 and 2.50 EUR/L could be found (for comparison: 1 L diesel fuel is equivalent to 10.0 kWh or 36.0 MJ). This range of calculated production costs per liter of fuel does not allow a direct comparison of the cost-effectiveness of the different process constellations, and the question arises how the results are influenced by plant size as well as technical and economic parameters. At the same time, nontransparency of the procedure and a lack of information prevented a simple follow-up of the studies. To elaborate in detail the premises and methodology used in the studies, the material and energy flows as well as method and parameters of production cost estimates in dependence on conversion capacity were determined. A sensitivity analysis was carried out to evaluate the impact Processes 2021, 9, 684 5 of 23

of different economic parameters on the assessment results. Finally, a harmonized set of economic parameters was applied to the different process conﬁgurations in order to compare the different evaluations of the bioliq® method on a common basis, achieving direct comparability among the studies.

2. Methods For more than 15 years, research has been carried out on the development of a sustainable process for the production of second-generation synthetic fuels from biomass using the bioliq® process of the Karlsruhe Institute of Technology (KIT) [11]. Biogenic residues, such as straw or residual forest wood, were used as raw materials for this process. Agricultural biomass needs to be sourced from particularly large areas, and its transport may be unfavorable for the supply of large-scale plants. According to the bioliq® concept, biomass is converted into an energy-dense liquid-like product for easier and cheaper transportation by decentralized fast pyrolysis plants. From a number of such plants, the intermediate bioenergy slurry, consisting of liquid fast pyrolysis oil and char containing solid, also referred to as biosyncrude, would be collected and further conversted in a large, central gasification and synthesis plant profiting from economies of scale. Due to this combination of local and centralized conversion plants, more favorable economic and environmental performance was expected for certain types of biomass. Another advantage of upstream pyrolysis is the homogeneity of the slurry. Its formulation enables the material properties to remain constant within a certain range of specifications, and it can be implemented more easily in the subsequent gasification process. In the following, first the original concept of the bioliq® process is described. However, in the technoeconomic studies evaluated here, certain deviations from this concept have been made [22]. Then, the methodology for comparison of the studies is explained. The first process step for the production of synthetic fuel according to the bioliq® concept is the pyrolysis of the biomass [10] as depiced in Figure1. The air-dry biomass is initially shredded and then delivered to the reactor, where it is reacted within a few seconds of gas retention time at approx. 500 ◦C in an anoxic atmosphere. Rapid heating is provided by mixing the biomass particulates with an excess of hot sand in a continuously operated twin screw reactor. The resulting hot pyrolysis vapors and main fraction of solid char product are separated in a hot gas cyclone. Instant quench condensation causes a condensate rich in organics to condensate first (fast pyrolysis bio-oil), and an aqueous condensate is obtained in a second condensation step. The pyrolysis bio-oil and the char containing solid formed during the pyrolysis then form the slurry. The high yield of liquid condensates is a prerequisite for the production of slurries with desirable material properties, such as particle size distribution, viscosity, and heating value. The production of biosyncrude suitable for gasification was investigated at KIT and still requires further research and development [13]. In the technoeconomic studies considered in this meta- analysis, biosyncrude was always considered as the sum of liquid condensates and solid char product without specifying its fuel characteristics, except for the heating value. The noncondensable gas, mainly consisting of CO and CO2, can be used as process energy to reheat the sand as heat carrier medium. Another central element of the bioliq® process is the gasification process, based on the Lurgi Multi-Purpose-Gasification (MPG) concept [14]. The biomass-based slurries from pyrolysis are gasified with pure oxygen in a high-pressure entrained flow gasifier at temperatures above 1200 ◦C and a pressure of up to 80 bar. The high temperatures ensure almost complete carbon conversion. The reaction chamber of the gasifier is surrounded by a water-cooled membrane wall, on which a protective layer of a centimeter-thick slag layer is formed. This prevents possible erosion and corrosion processes. At temperatures above the melting point of the ash, all inorganic material recovered from the gasifier is as a liquid slag by a lock hopper system. In the bioliq® concept, cleaning and conditioning of the syngas obtained is performed by high-temperature, high pressure gas cleaning after hot gas extraction from the gasifier. This helps to avoid heat losses and the installation of Processes 2021, 9, 684 6 of 23

Processes 2021, 9, 684 6 of 23

The bioliq® pilot plant that as shown schematically in Figure 2 is operated at KIT alongblowers the whole that compress process chain the synthesis since 2014 gas diff toers the from pressure the original required concept for the to synthesis.a certain The extenthigh pressure[22]. For ensuressyngas ause, rapid gasoline carbon type conversion of fuel with is produced relatively lowafter oxygen single consumptionreactor MeOH/DMEand a short synthesis. residence time.

Biomass

Shredder Pyrolysis Auger reactor gas Quench medium Sand heat carrier cycle Carbonisate + Condensate(s)

Biosyncrude Processes 2021, 9, 684 6 of 23

FigureFigure 1. Schematic 1. Schematic of the of thede-centralized de-centralized fast fastpyrolysis pyrolysis process. process. Due to the high reaction temperature, a tar-free synthesis gas (with a very low methane The bioliq® pilot plant that as shown schematically in Figure 2 is operated at KIT contentalong the below whole 1 vol.%) process is chain formed. since The 2014 dry diff gasers mainly from the consists original of carbonconcept monoxide to a certain (CO) andextent hydrogen [22]. For (H 2syngas) (approx. use, a gasoline 1:1 ratio) type as well of asfuel carbon is produced dioxide (COafter2 ).single Sour gasesreactor like COMeOH/DME2, HCl, and synthesis. H2S, as well as small amounts of other impurities such as ammonia (NH3), cyanide (HCN), and carbonyl sulﬁde (COS), are removed in the subsequent gas cleaning process up to the required limit value [15]. Once clean syngas is obtained, a variety of Biomass processes can be added to produce any type of hydrocarbon fuel (gasoline, diesel, jet fuel), oxygenate products (methanol (MeOH, dimethyl ether (DME)), and also hydrogen and methane (SNG,Shredder substitute natural gas). The bioliq® pilot plant that as shown schematically inPyrolysis Figure2 is operated at KIT along the whole processAuger reactor chain since 2014 differs from the original conceptgas to a certain extent [22]. For syngas use, gasoline type of fuel is produced after singleQuench reactor MeOH/DME synthesis. ® Nine studies on the bioliq process, which were performedmedium by different institutions withinSand eight heat years from 2006 to 2014, were evaluated, and are compiled in Table2. In principle,carrier they cycle consist of two groups of studies. First, technoeconomic evaluations were Figureperformed 2. Schematic within of the dissertations central part atof KITthe bioliq dedicated® concept. to the investigation of the bioliq® concept, Carbonisate + Condensate(s) evaluating the production costs utilizing different feedstock and products. The second ® groupNine consisted studies on of the studies bioliq performed process, onwhich the comparisonwere performed of different by different BtL pathways,institutions includ- withining, buteight not years exclusively from 2006 dedicated to 2014, to were theBiosyncrude bioliqevaluated,® process. and are In allcompiled cases, thein Table primary 2. In reports principle,were used they as consist data sources, of two whichgroups were of stud mostlyies. First, published technoeconomic in Germany. evaluations However, were reviewed performed within dissertations at KIT dedicated to the investigation of the bioliq® concept, papersFigure were1. Schematic also published of the de-centralized in a number fast pyrolysis of cases. process. These are also given in the references. evaluating the production costs utilizing different feedstock and products. The second group consisted of studies performed on the comparison of different BtL pathways, including, but not exclusively dedicated to the bioliq® process. In all cases, the primary reports were used as data sources, which were mostly published in Germany. However, reviewed papers were also published in a number of cases. These are also given in the references. Essential technical criteria taken into account were the plant configurations (number of pretreatment plants, conversion capacity of pretreatment and central plants), and the mass and energy input and output of local and central processing. In most cases, straw was selected as feedstock and Fischer-Tropsch (FT) hydrocarbons as the product. Straw

® FigureFigure 2. 2.SchematicSchematic of ofthe the central central part part of the of thebioliq bioliq® concept.concept.

Nine studies on the bioliq® process, which were performed by different institutions within eight years from 2006 to 2014, were evaluated, and are compiled in Table 2. In principle, they consist of two groups of studies. First, technoeconomic evaluations were performed within dissertations at KIT dedicated to the investigation of the bioliq® concept, evaluating the production costs utilizing different feedstock and products. The second group consisted of studies performed on the comparison of different BtL pathways, including, but not exclusively dedicated to the bioliq® process. In all cases, the primary reports were used as data sources, which were mostly published in Germany. However, reviewed papers were also published in a number of cases. These are also given in the references. Essential technical criteria taken into account were the plant configurations (number of pretreatment plants, conversion capacity of pretreatment and central plants), and the mass and energy input and output of local and central processing. In most cases, straw was selected as feedstock and Fischer-Tropsch (FT) hydrocarbons as the product. Straw

Processes 2021, 9, 684 7 of 23

Table 2. Overview on the techno-economic studies investigated in this work.

No. Study Type Author Institution Year Ref Institut für Technikfol- Kraftstoff, Strom und Wärme aus L. Leible genabschätzung und 1 Stroh und Waldrestholz—eine Report 2007 [23] et al. Systemanalyse systemanalytische Untersuchung (ITAS)—KIT Systemanalytische Untersuchung Institut für Technikfol- zur Schnellpyrolyse als genabschätzung und 2 Prozessschritt bei der Produktion PhD thesis S. Lange 2008 [24] Systemanalyse von Synthesekraftstoffen aus (ITAS)—KIT Stroh und Waldrestholz Modellierung und Bewertung von Institut für Prozessketten zur Herstellung von Industriebetriebslehre 3 PhD thesis P. Kerdoncuff 2008 [25] Biokraftstoffen der zweiten und industrielle Generation Produktion (IIP)—KIT Cost estimate for biosynfuel Institut für E. Henrich 4 production via biosyncrude Report Katalyseforschung und 2009 [26] et al. gasification -technik (IKFT)—KIT Techno-ökonomische Bewertung alternativer Institut für Verfahrenskonfigurationen zur Industriebetriebslehre 5 PhD thesis F. Trippe 2013 [27–29] Herstellung von und industrielle Biomass-to-Liquid (BtL) Produktion (IIP)—KIT Kraftstoffen und Chemikalien Biomass-to-Liquid“ BtL, eine Deutsche 6 Report - 2006 [30] Realisierungsstudie Energie-Agentur GmbH Institut für Energiever- Ermittlung spezifizierter Kosten fahrenstechnik und und ökologischer Auswirkungen B. Meyer 7 Report Chemieingenieurwesen 2009 [10,31] der Erzeugung von BtL et al. (IEC)—Technische Kraftstoffen und Biogas Universität Freiberg Institut für Feuerungs- Analyse von thermochemischen und Kraftwerkstechnik 8 Konversionsverfahren zur Report D. Beiermann 2010 [32] (IKF)— Herstellung von BtL-Kraftstoffen Universität Stuttgart Energy Carrier Chain LCA, Deliverable 9 Sustainability assessment of Report EU FP7 BioBoost project 2014 [33] D6.4 energy carriers

Essential technical criteria taken into account were the plant configurations (number of pretreatment plants, conversion capacity of pretreatment and central plants), and the mass and energy input and output of local and central processing. In most cases, straw was selected as feedstock and Fischer-Tropsch (FT) hydrocarbons as the product. Straw was selected because it can be expected that the decentralized bioliq® concept is particularly suitable for ash-rich, low-energy types of biomass. FT synthesis was chosen because for this process it was found to be the most common synthesis process used in all the studies. That way, fuel production costs are related to one liter of diesel-like fuel with lower heating values ranging from 43.2 to 44.2 MJ/kg. From the mass and energy flows, yields and conversion efficiencies were calculated, respectively. They are provided as the ratio of the energy contained in the product (either biosyncrude in the local or FT hydrocarbons in central processing plant) to the energy supplied to the process in form of feedstock, electricity and other fuels. Processes 2021, 9, 684 8 of 23

On the basis of these results, it was possible to examine the cost models for estimating investment and production costs. In the studies, missing information on parameters was calculated from given values, if possible, or queried directly by the authors. Each of the studies followed its own approach to calculating the different cost contributions and total costs. For a uniform treatment of the cost information with different degrees of detailing given in the studies, all expenses were allocated to “ﬁxed costs” and “variable costs”. Fixed costs are payments of a company that occur even when the plant is not in operation. They include capital costs (depreciation, interest, and construction costs), maintenance, repairs and servicing costs, insurance & tax payments, as well as personnel costs and overhead (administration, etc.). The annuity payments of the cost of capital are calculated using the following formula: (i + 1)n·i Annuity = I0· (1) (i + 1)n − 1

with n: depreciation period, i: interest rate, I0: total investment. Variable costs are only incurred during the operation of the plant, and consist mainly of costs for raw materials, transport, operating resources and energy. Annual variable costs were calculated from feedstock cost (straw at gate of the local conversion sites) and transportation of biosyncrude. In cases where electricity was by-produced, revenues were included in the production cost estimate. Finally, the economic models for estimating production costs were related to their date of origin by a plant cost index, investigated in terms of learning effects as well as for sensitivity with regard to investment and biomass supply costs. Harmonization of the cost models made use of the highest values of economic parameters (worst case scenario) or lowest values of economic parameters (best-case scenario) includingthe percentages used to calculate capital cost and those for maintenance and repair, as well as plant operation time.

3. Results and Discussion 3.1. Mass and Energy Balance, Conversion Efficiency First, the selected studies were analyzed with regard to their material and energy flows. The process chain for the production of BtL fuel was divided into its three main process steps. In addition to the fast pyrolysis process, the first process step, the local “pyrolysis,” also includes biomass preparation, such as crushing and drying. The second, central part of the process is collectively referred to as “gasification and synthesis,” and includes the process steps of slurry preparation, gasification and subsequent gas conditioning as well as the entire synthesis process including downstream product treatment as well as the energetic integration of residual gas and by-produced steam. This separation allowed for a modular analysis of the various studies and enabled comparison of the different approaches. Table3 provides a comprehensive overview of the numbers related to the material and energy flows in the various studies. For studies no. 2 and 5, adjustments became necessary because outputs of local and input of central sites were not consistent. The values of the input and output flows of the pyrolysis were then adjusted in both studies to the amount of biosyncrude required for gasification. The studies analyzed differed greatly in terms of their specified conversion capacity. While the smallest capacity only envisaged an annual biomass throughput of below 0.4 Mt, that of the largest assumed capacity of more than 9 Mt was roughly twenty times as much. Other studies mostly required biomass amounts between 1 and 2 Mt/a. Six of the nine studies used straw as biomass with a residual moisture of approx. 14 wt.% in their plant concepts. In Study 2, wood was used in addition to the straw and Study 6 did not denote a specific type of biomass, but only provided a calorific value and a moisture content of 30 wt.%, suggesting the use of wood as raw material. In one study, straw and wood were separately investigated as feedstock [10], and the resulting difference in the overall conversion efficiency along the whole process chain turned out to be only 0.5%. Processes 2021, 9, 684 9 of 23

Table 3. Overview of feedstock and process characteristics of the investigated studies.

Study 1 2 3 4 5 6 7 8 9 Biomass Straw Straw/wood Straw Straw Straw - Straw Straw Straw feedstock Water 14 14/35 15 15 15 30 14 15 15 content/wt.% Water content 7 7 15 - 8 15 7 10 - dry/wt.%

LHVBiomass/MJ/kg 14.6 11.9 14.9 14.4 14.5 12.6 14.2 14.0 13.4 Decentralized pyrolysis plants Thermal fuel capacity per 100 200 55 100 100 98 50 100 117 plant/MW Number of plants 50 4 10 35 11 5 5 5 4 Total biomass conversion 9247 1785 1000 6944 1989 1000 413 1027 2190 capacity/kt/a Total biosyncrude 5902 1182 680 4618 1347 503 306 724 1480 capacity/kt/a

LHVBiosyncrude/MJ/kg 18.7 16.6 19.9 19.1 17.6 22.0 17.3 17.2 16.9 Conversion 82% 76% 85% 88% 78% 84% 72% 82% 84% efﬁciency Central gasiﬁcation + synthesis plant Biosyncrude conversion 4089 680 500 3056 941 439 200 425 999 capacity/MW Biosyncrude conversion 5902 1182 680 4618 1347 503 306 724 1480 capacity/kt/a Hydrocarbon product 1716 326 187 1500 375 186 115 207 404 capacity/MW Product 1069 201 115 1000 216 106 66 136 238 capacity/kt/a

LHVproduct/MJ/kg 43.32 43.83 43.90 43.20 43.86 44.05 44.15 44.00 42.8 Electricity 0 0 74 0 146 22 0 0 85.5 export/MW Conversion 42% 48% 37 + 22% 1 40 + 22% 1 42 + 11% 1 43% 43% 48% 39.4 + 8.2% 1 efﬁciency Overall energetic 34% 34% 34 + 14% 1 43% 33 + 13% 1 37 + 4% 1 33% 39% 33.0 + 6.9% 1 efﬁciency 1: Related to fuel plus electricity production.

Table3 contains the conversion efficiencies, also depicted in Figure3. Those estimated for the local pyrolysis process varied between 72 and 88%. While comparatively high conversion losses occurred in studies 2, 5 and 7, the efficiency of the other studies was over 80%. Study 7 calculated the lowest efficiency, which was due to the high specific power consumption and the need for extra fuel. In Study 4, the consumption of electrical energy was not taken into account, as no value was given for it. This resulted in the highest efficiency of the pyrolysis process at 88%. The high efficiency in Study 6 can be explained by the above-average calorific value of the slurry, see Table3. In all studies, Study 7 excluded, no additional fuel gas was required to heat-up and pyrolyze the biomass. Processes 2021, 9, 684 10 of 23 Processes 2021, 9, 684 10 of 23

100

40 Efficiency / %

0 123456789 Study number Pyrolysis process Gasification/synthesis process Overall efficiency

Figure 3. Conversion efficiencies of the local pyrolysis and the central gasification/synthesis processes together with those of the overall process in the investigated studies. Figure 3. Conversion efficiencies of the local pyrolysis and the central gasification/synthesis processes together with those of the overall process in the investigatedThe amount studies. of biosyncrude obtained from the biomass was, except of the higher value in Study 6, nearly the same across all studies, most likely because the authors made use The amount of biosyncrude obtained from the biomass was, except of the higher of the same data published on the bioliq® process. It is interesting to see that notable value in Study 6, nearly the same across all studies, most likely because the authors made differences in the composition of the biosyncrude occurred particularly with regard to use of the same data published on the bioliq® process. It is interesting to see that notable the water content (Table4). The water content of the dried biomass varied due to various differencesassumptions in the for composition the intensity of ofthe the biosyncrude drying step occurred and, consequently, particularly with differing regard amounts to the waterof water content were (Table found 4). in The the water summed-up content condensates. of the dried biomass Surprisingly, varied the due varying to various water assumptionscontent did for not the significantly intensity of affect the thedrying calorific step valueand, consequently, of the biosyncrude, differing which amounts may beof a waterconsequence were found of the in the heating summed-up values assumed condensate fors. its Surprisingly, different components. the varying water content did not significantly affect the calorific value of the biosyncrude, which may be a conse- quenceTable 4.ofProduct the heating yields values by straw assumed fast pyrolysis for its (data different given incomponents. wt.%).

TableStudy 4. Product yields by straw 1 fast pyrolysis 2 (data given 3 in wt.%). 4 5 8

StudyGas 1 23.10 2 18.30 3 20.00 4 21.40 24.805 21.208 GasBiosyncrude 23.10 76.90 18.30 81.70 20. 80.0000 21.40 78.60 24.80 75.20 21.20 78.80 Biosyncrude thereof: 76.90 81.70 80.00 78.60 75.20 78.80 Condensatesthereof: 54.98 51.39 60.00 30.97 27.52 47.91 CondensatesChar solid 54.98 21.92 51.39 23.86 60.00 20.00 30.97 29.79 27.52 24.97 47.91 21.83 Char solid 21.92 23,86 20.00 29.79 24.97 21.83 Water 6.45 0.00 17.84 22.71 9.06 Water 6.45 0.00 17.84 22.71 9.06 LHV/MJ·kg−1 18.7 16.6 19.9 19.1 17.6 17.2 LHV/MJ·kg−1 18.7 16.6 19.9 19.1 17.6 17.2

WhenWhen looking looking for for the the conversion conversion efficiencies efficiencies of of the the central central processing processing steps, steps, signifi- signifi- cantlycantly greater greater differences differences were were found found compared compared to to those those of of the the pyrolysis pyrolysis process. process. Devia- Devia- tionstions of of up up to to 20% 20% were were seen seen between between the the st studies.udies. Comparatively Comparatively high high efficiencies efficiencies were were achievedachieved in in the the studies studies no. no. 3, 3, 5 5and and 6 6that that co considerednsidered the the by-production by-production of of electricity electricity (see (see TableTable 3).3). In In these these studies, studies, however, however, the the ener energygy content content in in the the fuel fuel was was reduced. reduced. The The high high efficiencyefficiency in in Study Study 4 4(49% (49% even even without without elec electricitytricity production) production) can can be be explained explained by by the the procedureprocedure used used to to estimate estimate the the efficiency, efficiency, which which was was drawn drawn up up on on the the basis basis of of purely purely

Processes 2021, 9, 684 11 of 23

stoichiometric considerations of the overall chemical reactions taking place. Thermal losses were also estimated, but were comparatively low A more detailed consideration of the differences was difficult because the data from the internal evaluation sheets or simulation models was not available. Regarding overall conversion efficiency, it is interesting to see that five out of the eight studies showed nearly identical efficiencies of 33% or 34% related to the hydrocarbon fuel production, only differing by whether or not electrical energy could be by-produced. The highest efficiency of 43% was again found in Study 4. There, the low thermal losses raised the efficiency to a comparatively high value. Based on the energy balance of the sole stoichiometric chemical reactions, a theoretical maximum conversion efficiency of around 55% can be assumed for the bioliq® process when pure oxygen is used for gasification. In average, the studies examined estimated an efficiency of 36% (± 3%) (leaving out Study 4) of the bioliq® process, based on the energy content of the fuel. This is a comparatively small deviation for an emerging technology, which, however, can be partially explained by the fact that the same sources were referred to. However, there were some significant differences between the studies in the assumption of electricity production. By-production of electricity was most relevant, particularly regarding the high gasification temperatures occurring and the possible heat use of several hundred degrees Celsius. In Study 5, the overall process was designed to be self-sufficient in terms of heat and electricity supply. Only excess amounts were exported into the electricity grid. The same was true for another studied carried out within the EU project RENEW, where around 45% conversion efficiency was estimated within a self-sufficient process [34]. Conversion efficiencies for the bioliq® concept appeared lower compared to those of other BtL technologies. In the IE study, efficiencies were reported to range between 40 and 55% [21]. Dimitriou et al. found 38 to 48% comparing different entrained flow and circulating fluid bed reactor technologies to produce Fischer-Tropsch hydrocarbon fuel from wood chips [7].

3.2. BtL Production Cost Estimation 3.2.1. BtL Production Costs The allocation of the different cost contributions to fixed and variable costs enabled a fair comparison between the studies and clearly showed their distinguishing features. An analysis of the costs was not possible for Study 7 due to the lack of information on the total investment costs. Table5 provides an overview on the cost models and contributions used in the investigated studies. Figure4 displays the specific production costs determined per liter of hydrocarbon fuel and the associated contribution of fixed and variable costs. For Study 7 only the total production costs are shown. It can be seen that the specific production costs differed significantly both in total and in the distribution of fixed and variable costs. In total, the costs varied between 0.80 EUR/L and 2.37 EUR/L. In addition to the previously discussed conversion efficiencies estimated in the studies, this was also a result of differences in the methodology used to set up cost models. Any revenue from electricity production was deducted from the variable costs and thus their share was reduced. While fixed and variable costs made almost balanced contributions to the total production costs in studies 1, 2 and 4, the fixed costs were significantly higher in the other studies. In Study 9, costs incurred by research and development were also considered. However, there was a trend towards a decreasing proportion of fixed costs with increasing plant size, as could be expected. ProcessesProcessesProcessesProcesses 2021 2021 2021 2021, ,9 9, ,,684 ,9684 9, ,684 684 1212 of12 of12 23 of23 of 23 23

Processes 2021, 9, 684 12 of 23 FigureFigureFigureFigure 4. 4. Specific 4.Specific 4. Specific Specific production production production production costs costs costs costs and and and and their their their their comp comp comp compositionositionositionosition by by by fixedbyfixed fixed fixed and and and and variable variable variable variable costs. costs. costs. costs.

TableTableTableTable 5. 5. Economic 5.Economic 5. Economic Economic parameters parameters parameters parameters and and and and cost cost cost cost contributions contributions contributions contributions of of the of theof the theBtL BtL BtL BtLcost cost cost cost evaluation; evaluation; evaluation; evaluation; Colors Colors Colors Colors of of theof theof the thepie pie pie piechart: chart: chart: chart: light light light light blue: blue: blue: blue: capital capital capitalcapital capital cost;cost;cost;cost;cost; orange: orange: orange: orange:orange: maintenance; maintenance; maintenance; maintenance;maintenance; grey: grey: grey: grey:grey: tax tax tax taxtax& & insurance; & &insurance;& insurance; insurance;insurance; yellow: yellow: yellow: yellow:yellow: personnel; personnel; personnel; personnel;personnel; blue: blue: blue: blue:blue: overhead; overhead; overhead; overhead;overhead; green: green: green: green:green: biomass; biomass; biomass; biomass;biomass; dark dark dark darkdark blue: blue: blue: blue:blue: materials; materials; materials; materials;materials; brown:brown:brown:brown:brown: utilities. utilities. utilities. utilities.utilities.

Study Study Study Study 11 1 1 1 Study Study Study Study Study 2 2 2 2 Study Study Study Study Study 3 3 33 3 Study Study Study Study Study 4 4 4 4 InvestInvestInvestInvestInvest local locallocal local local plantsplants plants plants 1,177,415 1,177,415 1,177,415 1,177,415 kEURkEUR kEUR kEUR kEUR 341,598 341,598 341,598 341,598 341,598 kEUR kEUR kEUR kEUR kEUR 130,920 130,920 130,920 130,920 130,920 kEUR kEUR kEURkEUR kEUR 700,000 700,000 700,000 700,000 700,000 kEUR kEUR kEUR kEUR InvestInvestInvestInvestInvest central centralcentral central central plantplant plant plant plant 2,011,136 2,011,136 2,011,136 2,011,136 kEURkEUR kEUR kEUR kEUR 439,000 439,000 439,000 439,000 439,000 kEUR kEUR kEUR kEUR kEUR 401,490 401,490 401,490 401,490 401,490 kEUR kEUR kEURkEUR kEUR 750,000 750,000 750,000 750,000 750,000 kEUR kEUR kEUR kEUR ProductionProductionProductionProductionProduction costscosts costs costs 1.01 1.01 1.01 1.01 EUR/LEUR/L EUR/L EUR/L EUR/L 1.12 1.12 1.12 1.12 1.12 EUR/L EUR/L EUR/L EUR/L 1.02 1.02 1.02 1.02 EUR/L EUR/L EUR/LEUR/L EUR/L 0.8 0.8 0.8 0.8EUR/L 0.8EUR/L EUR/L EUR/L

Capital cost 11.4% of TCI 9.4% 10.2% 14.2% CapitalCapitalCapitalCapital cost cost cost cost 11.4% 11.4% 11.4% 11.4% of of ofTCI TCIof TCI TCI 9.4% 9.4% 9.4% 9.4% 10.2% 10.2% 10.2% 10.2% 14.2% 14.2% 14.2% 14.2% InterestInterestInterestInterestInterest rate raterate rate rate 7% 7% 7% 7% 7% 7% 7% 7% 7% 8% 8% 8% 8% 7% 7% 7% 7% 7% DepreciationDepreciationDepreciationDepreciationDepreciation periodperiod period period period 18 18 18 years 18years years years 20 20 20 20years 20years years years years 20 20 20 20years 20years years years 10 10 10years 10years 10 years years years MaintenanceMaintenanceMaintenanceMaintenanceMaintenance 4% 4% 4% 4% 4% ofof TCIofTCI TCIof TCI TCI 5/3% 5/3%5/3% 5/3% 5/3% 1 1 of1 of 1of 1ofTCI TCIof TCI TCI TCI 4% 4% 4%4% 4%of of ofTCI TCIof TCITCI TCI 6% 6% 6% 6%of 6%of ofTCI TCIof of TCI TCI BiomassBiomassBiomassBiomassBiomass 72 72 72 EUR/tEUR/t 72EUR/t EUR/t EUR/t 68 68 68 68EU 68EU EUR/t EU R/tEUR/tR/t R/t 63 63 63EUR/t 63EUR/t EUR/tEUR/t EUR/t 64 64 64EUR/t 64EUR/t 64 EUR/t EUR/t Electrical energy price - 61 EUR/MWh 76 EUR/MWh - ElectricalElectricalElectricalElectrical energyenergy energy energy price price price price - - - - 61 61 61 61EUR/MWh EUR/MWh EUR/MWh EUR/MWh 76 76 76 76EUR/MWh EUR/MWh EUR/MWh - - - ElectricityElectricityElectricityElectricity demand demand demand demand - - - - 2.6 2.6 2.6 2.6kW/L kW/L kW/L kW/L 1.9 1.9 1.9 1.9kW/L kW/L kW/L kW/L - - - - Electricity demand - 2.6 kW/L 1.9 kW/L - ShareShareShareShare of of ofelectricity electricityof electricity electricity Share of electricity costs --- - - 0.06 0.06 0.06 0.06 0.06 EUR/L EUR/L EUR/L EUR/L 0.05 0.05 0.05 0.05 EUR/L EUR/L EUR/LEUR/L EUR/L - - - - - costscostscostscosts PersonnelPersonnelPersonnelPersonnelPersonnel 1287 128712871287 306306 306306306 5050 50 50 1168116811681168 1168 AnnualAnnualAnnualAnnualAnnual operation operationoperation operation operation timetime time time time 7500 7500 7500 7500 hh h h h 7500/8000 7500/8000 7500/8000 7500/8000 7500/8000 h h h h 7502 7502 7502 7502 h h hh h 8000 8000 8000 8000 8000 h h h h StudyStudyStudyStudy 55 5 5 5 Study Study Study Study Study 6 6 6 6 Study Study Study Study Study 8 8 88 8 Study Study Study Study Study 9 9 9 9 ProcessesProcessesProcessesProcessesInvest 2021 2021 2021 local 2021, 9, ,9, 684,9, plants 684,9 684, 684 374,880 kEUR 245,160 kEUR 343,295 kEUR 640,722 kEUR1313 13of 13of 23of 23of 23 23 InvestInvest InvestInvest local local local local plants plants plants plants 374,880 374,880 374,880 374,880 kEUR kEUR kEUR kEUR 245,160 245,160 245,160 245,160 kEUR kEUR kEUR kEUR 343,295 343,295 343,295 343,295 kEUR kEUR kEUR kEUR 640,722 640,722 640,722 640,722 kEUR kEUR kEUR kEUR InvestInvestInvestInvestInvest central centralcentral central central plantplant plant plant plant 693,200 693,200 693,200 693,200 693,200 kEURkEUR kEUR kEUR kEUR 414,440 414,440 414,440 414,440 414,440 kEUR kEUR kEUR kEUR 691,526 691,526 691,526 691,526 691,526 kEUR kEUR kEURkEUR kEUR 864,734 864,734 864,734 864,734 864,734 kEUR kEUR kEUR kEUR ProductionProductionProductionProductionProduction costscosts costs costs 1.20 1.20 1.20 1.20 EUR/LEUR/L EUR/L EUR/L EUR/L 1.45 1.45 1.45 1.45 1.45 EUR/L EUR/L EUR/L EUR/L 1.90 1.90 1.90 1.90 EUR/L EUR/L EUR/LEUR/L EUR/L 2.57 2.57 2.57 2.57 2.57 EUR/L EUR/L EUR/L EUR/L

CapitalCapitalCapitalCapital cost costcost cost cost 8.3% 8.3% 8.3% 8.3% 8.3% of of TCIofTCI ofTCI TCI TCI 13.6% 13.6% 13.6% 13.6% 10.2% 10.2% 10.2% 10.2% 10.2% 9.3% 9.3% 9.3% 9.3% 9.3% InterestInterestInterestInterest rate rate rate rate rate 5% 5% 5% 5% 8% 8% 8% 8% Not Not Not Not Not specified specified specified speciﬁed specified 5% 5% 5% 5% 5% DepreciationDepreciationDepreciationDepreciation period period period period 20 20 20 years 20years years years 15 15 15years 15 years years years 20 20 20 20years 20years years years 20 20 20years 20 20years years years MaintenanceMaintenanceMaintenanceMaintenance 4% 4% 4% 4% 4%of of TCIofTCI ofTCI TCI TCI 2% 2% 2% 2% 2%of of ofTCI TCIofTCI TCI TCI 6% 6% 6% 6% 6%of of of TCI ofTCI TCI TCI 7% 7% 7% 7%of 7% of ofTCI of TCI TCI TCI BiomassBiomassBiomassBiomass 70 70 70 EUR/tEUR/t 70EUR/t EUR/t EUR/t 68 68 68EUR/t 68 EUR/tEUR/t EUR/t EUR/t 65 65 65EUR/t 65EUR/t EUR/t EUR/t 60 60 60EUR/t 60EUR/t EUR/t EUR/t ElectricalElectrical energy energy price price 90 90 EUR/MWh EUR/MWh 50 50 EUR/MWh EUR/MWh 150 150 EUR/MWh EUR/MWh - - ElectricalElectrical energy energy energy priceprice price 90 90 EUR/MWh EUR/MWh 50 50 EUR/MWh EUR/MWh 150 150 150 EUR/MWh EUR/MWh EUR/MWh - - - ElectricityElectricityElectricityElectricity demand demand demand demand 1.7 1.7 1.7 kW/L 1.7 kW/L kW/L kW/L 1.3 1.3 1.3 kW/L 1.3 kW/L kW/L kW/L 1.2 1.2 1.2 kW/L 1.2 kW/L kW/L kW/L - - - - Electricity demand 1.7 kW/L 1.3 kW/L 1.2 kW/L - PersonnelPersonnelPersonnelPersonnel 533 533 533 533 293293293 293 232232232 232 251251251 251 Personnel 533 293 232 251 AnnualAnnualAnnualAnnual operation operation operation operation time time time time 7008 7008 7008 7008 h h h h 7000 7000 7000 7000 h h h h 7008 7008 7008 7008 h h h h 7008 7008 7008 7008 h h h h Annual operation time 7008 h1:1: for1: for1: for localfor local local local and and and andcentral central central central 7000 plant, plant, plant, h plant, respectively. respectively. respectively. respectively. 7008 h 7008 h 1: for local and central plant, respectively. MostMostMostMost of of ofthe ofthe the thefixed fixed fixed fixed cost cost cost cost contributions contributions contributions contributions depend depend depend depend on on on theon the the theinvestment investment investment investment costs costs costs costs and and and and are are are areusually usually usually usually calculatedcalculatedcalculatedcalculated from from from from fixed fixed fixed fixed percentage percentage percentage percentage shares. shares. shares. shares. Theref Theref Theref Therefore,ore,ore,ore, the the the theestimation estimation estimation estimation of of ofthe ofthe the thetotal total total total investment investment investment investment mademademademade in in in the inthe the thestudies studies studies studies earned earned earned earned specific specific specific specific care. care. care. care. To To To determineTo determine determine determine the the the theinvestment investment investment investment in in in BtL inBtL BtL BtL studies, studies, studies, studies, a a a a factorfactorfactorfactor method method method method was was was was most most most most commonly commonly commonly commonly applied. applied. applied. applied. Th Th The Th ecostse costs ecosts costs for for for thefor the the themain main main main technical technical technical technical components components components components suchsuchsuchsuch as as asapparatus asapparatus apparatus apparatus and and and and machines machines machines machines were were were were estimated estimated estimated estimated either either either either on on on the on the the thebasis basis basis basis of of ofpersonal ofpersonal personal personal experience, experience, experience, experience, bybyby requestingby requesting requesting requesting quotes quotes quotes quotes from from from from companies, companies, companies, companies, or or orwith orwith with with the the the thehelp help help help of of ofliterature ofliterature literature literature and and and and suitable suitable suitable suitable publica- publica- publica- publica- tions.tions.tions.tions. The The The The adjustment adjustment adjustment adjustment to to tothe tothe the therequired required required required conversion conversion conversion conversion capacity capacity capacity capacity was was was was done done done done by by by meansby means means means of of ofdegres- ofdegres- degres- degres- sionsionsionsion factors, factors, factors, factors, the the the thevalues values values values of of ofwhich ofwhich which which were were were were also also also also taken taken taken taken from from from from literature literature literature literature and and and and took took took took into into into into account account account account the the the the differentdifferentdifferentdifferent economies economies economies economies of of ofscale ofscale scale scale for for for forvarious various various various comp comp comp components.onents.onents.onents. The The The The price price price price was was was was then then then then adjusted adjusted adjusted adjusted to to tothe tothe the the yearyearyearyear in in inwhich inwhich which which the the the theplant plant plant plant was was was was built built built built using using using using a a pricea price aprice price index. index. index. index. This This This This should should should should have have have have compensated compensated compensated compensated for for for for fluctuatingfluctuatingfluctuatingfluctuating raw raw raw raw material material material material costs, costs, costs, costs, especially especially especially especially fo fo rfo rfosteel. rsteel. rsteel. steel. After After After After these, these, these, these, and and and and possibly possibly possibly possibly some some some some other other other other adjustments,adjustments,adjustments,adjustments, the the the theso-determined so-determined so-determined so-determined purchase purchase purchase purchase costs costs costs costs were were were were multiplied multiplied multiplied multiplied by by by factors by factors factors factors considering considering considering considering en- en- en- en- gineering,gineering,gineering,gineering, construction, construction, construction, construction, instru instru instru instrumentation,mentation,mentation,mentation, piping piping piping piping and and and and unforeseen unforeseen unforeseen unforeseen events. events. events. events. The The The The choice choice choice choice and and and and valuesvaluesvaluesvalues of of ofthese ofthese these these factors factors factors factors was was was was usually usually usually usually individually individually individually individually adapted adapted adapted adapted by by by theby the the theauthors. authors. authors. authors. Studies Studies Studies Studies 3, 3, 3,5, 3,5, 5,6, 5,6, 6,7 6,7 7 7 andandandand 8 8used 8 used 8used used this this this this method method method method for for for thefor the the theentire entire entire entire processi processi processi processingngng plant,ng plant, plant, plant, whereas whereas whereas whereas studies studies studies studies 2 2and 2 and 2and and 4 4only 4 only 4only only used used used used itit forit forit for forthe the the thecost cost cost cost estimation estimation estimation estimation of of of decentralized ofdecentralized decentralized decentralized locations. locations. locations. locations. Study Study Study Study 4 4 used4 used 4used used known known known known costs costs costs costs of of of a of a GtLa GtL aGtL GtL (gas-to-liquid)(gas-to-liquid)(gas-to-liquid)(gas-to-liquid) plant plant plant plant to to toderive toderive derive derive the the the thecosts costs costs costs of of ofth ofth eth ethcentrale central ecentral central gasification/synthesis gasification/synthesis gasification/synthesis gasification/synthesis plant plant plant plant with with with with ap- ap- ap- ap- propriatepropriatepropriatepropriate scaling scaling scaling scaling to to tothe tothe the thedesired desired desired desired capacity. capacity. capacity. capacity. Study Study Study Study 2 2 estimated2 estimated 2estimated estimated the the the thecosts costs costs costs at at at439 at439 439 439million million million million EUR EUR EUR EUR withoutwithoutwithoutwithout providing providing providing providing further further further further information. information. information. information. AccordingAccordingAccordingAccording to to toTable toTable Table Table 5, 5, 5,large 5,large large large deviations deviations deviations deviations in in inthe inthe the thedetermination determination determination determination of of ofthe ofthe the thetotal total total total investment investment investment investment betweenbetweenbetweenbetween the the the thestudies studies studies studies exist. exist. exist. exist. Studies Studies Studies Studies 2, 2, 2,5 2,5 and5 and 5and and 8 8 calculated8 calculated 8calculated calculated almost almost almost almost identical identical identical identical investments investments investments investments with with with with littlelittlelittlelittle differences, differences, differences, differences, although although although although they they they they varied varied varied varied greatl greatl greatl greatlyy iny iny intheir intheir their their conversion conversion conversion conversion capacity. capacity. capacity. capacity. The The The The capacity capacity capacity capacity ofof ofStudy ofStudy Study Study 5 5 with5 with 5with with 1100 1100 1100 1100 MW MW MW MW was was was was almost almost almost almost twice twice twice twice that that that that of of ofStudy ofStudy Study Study 8 8 (6008 (600 8(600 (600 MW), MW), MW), MW), while while while while the the the thecosts costs costs costs of of of of thethethe theplants plants plants plants were were were were only only only only 10% 10% 10% 10% more. more. more. more. In In Incontrast, Incontrast, contrast, contrast, studies studies studies studies 6 6 and6 and 6and and 3 3 calculated3 calculated 3calculated calculated significantly significantly significantly significantly lower lower lower lower totaltotaltotaltotal investments investments investments investments at at atthe atthe the thedecentralized decentralized decentralized decentralized location location location location than than than than Study Study Study Study 8 8 with8 with 8with with similar similar similar similar capacities. capacities. capacities. capacities. This This This This pronouncedpronouncedpronouncedpronounced ratio ratio ratio ratio suggests suggests suggests suggests that that that that in in inaddition inaddition addition addition to to toeconomies toeconomies economies economies of of ofscale ofscale scale scale there there there there were were were were other other other other factors factors factors factors thatthatthatthat explained explained explained explained the the the thesignificant significant significant significant deviations deviations deviations deviations in in ininvestments. ininvestments. investments. investments. In In Intwo Intwo two two studies, studies, studies, studies, learning learning learning learning led led led ledto to toa to a a a reductionreductionreductionreduction in in ininvestments: ininvestments: investments: investments: Study Study Study Study 5 5 included5 included 5included included a a learninga learning alearning learning effect effect effect effect in in inthe inthe the thecost cost cost cost calculation calculation calculation calculation of of ofthe ofthe the the decentralizeddecentralizeddecentralizeddecentralized plants, plants, plants, plants, which which which which reduced reduced reduced reduced the the the thecosts costs costs costs to to toan toan an averagean average average average of of of80% of80% 80% 80% of of ofthe ofthe the thefirst first first first one. one. one. one. Study Study Study Study 6 6also6 also 6also also assumed assumed assumed assumed a a costa cost acost cost reduction reduction reduction reduction potential potential potential potential of of of15% of15% 15% 15% compared compared compared compared to to tothe tothe the thefirst first first first plant. plant. plant. plant. TheTheTheThe level level level level of of of detail ofdetail detail detail was was was was quite quite quite quite different different different different betw betw betw betweeneeneeneen the the the thestudies. studies. studies. studies. In In In Study InStudy Study Study 5, 5, 5,the 5,the the theauthors authors authors authors determineddetermineddetermineddetermined costs costs costs costs down down down down to to tothe tothe the thelevel level level level of of ofindividual ofindividual individual individual technical technical technical technical components, components, components, components, while while while while studies studies studies studies 2, 2, 2, 2, 3,3, 3,6 3, 6 and6 and 6and and 8 8 used8 used 8used used a a rougha rough arough rough approach approach approach approach and and and and summarized summarized summarized summarized the the the thecomponents components components components in in inless inless less less detail detail detail detail or or or re- orre- re- re- ferredferredferredferred to to to a to a referencea reference areference reference plant. plant. plant. plant. Another Another Another Another differenc differenc differenc difference earosee arose earose arose from from from from the the the thetype type type type and and and and value value value value of of of factors offactors factors factors appliedappliedappliedapplied to to tothe tothe the theplant plant plant plant purchase purchase purchase purchase costs. costs. costs. costs. Studies Studies Studies Studies 2, 2, 32, 32,and 3 and 3and and 4 4used 4 used 4used used components components components components supplied supplied supplied supplied as as asa as abase a base abase base

ProcessesProcesses2021 2021, 9,, 9 684, 684 1213 of of 23 23

FigureFigure 4. 4.Speciﬁc Specific productionproduction costscosts andand theirtheir compositioncomposition by ﬁxedfixed and variable costs.

Table 5. Economic parameters andMost cost of contributions the fixed cost of the contributions BtL cost evaluation; depend Colors on the of investmentthe pie chart: costslight blue: and capital are usually cost; orange: maintenance;calculated grey: tax & frominsurance; fixed yellow: percentage personnel; shares. blue: Therefore, overhead; green: the estimation biomass; dark of the blue: total materials; investment brown: utilities. made in the studies earned specific care. To determine the investment in BtL studies, a factor method Study was 1 most commonly Study applied. 2 The costs for Study the main3 technical components Study 4 such as apparatus and machines were estimated either on the basis of personal experience, by Invest local plants requesting 1,177,415 quoteskEUR from companies, 341,598 kEUR or with the help 130,920 of literaturekEUR and suitable 700,000 publications.kEUR Invest central plant The 2,011,136 adjustment kEUR to the required 439,000 kEUR conversion capacity 401,490 was kEUR done by means 750,000 of kEUR degression Production costs factors, 1.01 theEUR/L values of which 1.12 were EUR/L also taken from 1.02 literatureEUR/L and took 0.8 into EUR/L account the different economies of scale for various components. The price was then adjusted to the year in which the plant was built using a price index. This should have compensated for fluctuating raw material costs, especially for steel. After these, and possibly some other adjustments, the so-determined purchase costs were multiplied by factors considering engineering, construction, instrumentation, piping and unforeseen events. The choice and values of these factors was usually individually adapted by the authors. Studies 3, 5, 6, 7 and 8 used this method for the entire processing plant, whereas studies 2 and 4 only Capital cost used 11.4% it for of the TCI cost estimation 9.4% of decentralized locations. 10.2% Study 4 used known 14.2% costs of a Interest rate GtL (gas-to-liquid) 7% plant to derive 7% the costs of the central 8% gasification/synthesis 7% plant with Depreciation period appropriate 18 years scaling to the desired 20 years capacity. Study 2 20 estimated years the costs at 10 439 years million EUR Maintenance without 4% of providing TCI further 5/3% information. 1 of TCI 4% of TCI 6% of TCI Biomass According 72 EUR/t to Table5, large 68 EU deviationsR/t in the 63 determination EUR/t of the64 total EUR/t investment Electrical energy price between thestudies exist. 61 Studies EUR/MWh 2, 5 and 8 calculated 76 EUR/MWh almost identical investments - with little differences, although they varied greatly in their conversion capacity. The capacity Electricity demand - 2.6 kW/L 1.9 kW/L - of Study 5 with 1100 MW was almost twice that of Study 8 (600 MW), while the costs of Share of electricity the plants- were only 10% more. 0.06 EUR/L In contrast, studies 0.05 6 EUR/L and 3 calculated significantly - lower costs total investments at the decentralized location than Study 8 with similar capacities. This Personnel pronounced1287 ratio suggests that306 in addition to economies50 of scale there were1168 other factors Annual operation time that explained 7500 h the significant 7500/8000 deviations h in investments. 7502 h In two studies, learning8000 h led to a reductionStudy in 5 investments: Study Study 5 6 included a learning Study effect 8 in the cost calculation Study 9 of the decentralized plants, which reduced the costs to an average of 80% of the first one. Study 6 Invest local plants 374,880 kEUR 245,160 kEUR 343,295 kEUR 640,722 kEUR also assumed a cost reduction potential of 15% compared to the first plant. Invest central plant 693,200 kEUR 414,440 kEUR 691,526 kEUR 864,734 kEUR The level of detail was quite different between the studies. In Study 5, the authors Production costs 1.20 EUR/L 1.45 EUR/L 1.90 EUR/L 2.57 EUR/L determined costs down to the level of individual technical components, while studies 2, 3, 6 and 8 used a rough approach and summarized the components in less detail or referred to a reference plant. Another difference arose from the type and value of factors

ProcessesProcesses 20212021, 9,, 9684, 684 1414 of of23 23

applied to the plant purchase costs. Studies 2, 3 and 4 used components supplied as a base value multiplied with sum factors of 3.5, 2, and 5, respectively. Studies 5, 6 and 7 assumes value multiplied with sum factors of 3.5, 2, and 5, respectively. Studies 5, 6 and 7 assumes installed components, multiplied by factors of 3.43, 1.65, and 3.18, respectively. Only two installed components, multiplied by factors of 3.43, 1.65, and 3.18, respectively. Only two of of the studies took into account an additional surcharge for working capital, which in- the studies took into account an additional surcharge for working capital, which increased creased the investment costs by a factor of 1.75 and 1.56, respectively. the investment costs by a factor of 1.75 and 1.56, respectively. InIn Figure Figure 5,5 the, the specific specific investment investment costs costs (SIC) (SIC) in Euro in Euro per perkW kWproduct product are shown are shown for thefor local the and local the and central the central conversion conversion plants plantsas well as as well for the as foroverall the overallinvestment. investment. The num- The bersnumbers for overall for overall SIC included SIC included several several local local plan plants,ts, in addition in addition to one to one central central conversion conversion plant,plant, according according to to the the different different configuratio configurationsns considered considered in in the the studies. studies. The The conversion conversion capacitiescapacities were were related related to to the biomass feedstockfeedstock for for the the local local plants, plants, to to the the biosyncrude biosyncrude feed feedfor for gasification/synthesis gasification/synthesis central central plants plants and toand the to product the product capacity capacity for the for overall the overall process, process,respectively. respectively. In the RENEWIn the RENEW EU project EU SICproject values SIC ofvalues 1885 EUR/kWof 1885 EUR/kW were estimated were esti- for matedthe bioliq for the® concept bioliq®with concept five pyrolysiswith five plantspyrolysis of 100 plan MWts of each 100 and MW a gasification/synthesiseach and a gasification/synthesisplant of around plant 400 of MW around feedstock 400 MW conversion feedstock capacity. conversion capacity.

Figure 5. Specific investment costs (SIC) for summarized local and central conversion plants and Figure 5. Specific investment costs (SIC) for summarized local and central conversion plants and overalloverall SIC. SIC. Study 8 with 1600 EUR/kW determined about twice the specific investment for the Study 8 with 1600 EUR/kW determined about twice the specific investment for the central location compared to studies 3 and 6 with similar conversion capacities. It is very centrallikely location that this compared was due toto thestudies significantly 3 and 6 with larger similar surcharges conversion that Studycapacities. 8 added It is very to the likelyequipment that this purchase was due costs. to the With significantly the high larger level of surcharges detail inthe that cost Study calculation, 8 added Studyto the 5 equipmentestimated purchase the costs costs. for a muchWith the larger high system level of (941 detail MW) in atthe comparable cost calculation, costs toStudy studies 5 estimated2 and 3. the Studies costs 4 for and a 1,much on the larger other system hand, (941 estimated MW) at significantly comparable lower costs specificto studies costs, 2 andwhich 3. Studies seems plausible4 and 1, dueon the to theother much hand, higher estimated capacity significantly according to lower the economy-of-scale specific costs, whicheffect. seems Study plausible 4 also presented due to the a specialmuch higher feature capacity in the fact according that the to central the economy-of-scale location would be effect.incorporated Study 4 also into presented an existing a refineryspecial feature complex in asthe a fact so-called that the “brown central field” location facility. would The beconcept, incorporated therefore, into didan existing not envisage refinery the comp constructionlex as a so-called of a new air“brown separation field” unit,facility. since The the concept,required therefore, oxygen wasdid boughtnot envisage “over the fence”.construction of a new air separation unit, since the requiredDespite oxygen the wide was spreading bought “over of SICs the for fence”. the bioliq ® concepts they were a relatively close matchDespite for the the range wide of spreading specific capital of SICs costs for of the 2900–4500 bioliq® EUR/kWconcepts asthey estimated were ain relatively a study of closeIEA match Bioenergy for the Task range 41 [21 of]. specific This estimation capital costs was basedof 2900–4500 on a report EUR/kW on the as technology estimated statusin a studyand reliabilityof IEA Bioenergy of the value Task chains 41 [21]. for This advanced estimation biofuels was bybased SGAB on [a35 report]. Correspondingly, on the tech-

Processes 2021, 9, 684 15 of 23

nology status and reliability of the value chains for advanced biofuels by SGAB [35]. Cor- respondingly,fuel production fuel costsproduction were calculated costs were to ca varylculated between to vary 0.80 between and 1.54 EUR/L0.80 and for 1.54 gasiﬁcation- EUR/L forbased gasification-based Fischer-Tropsch Fischer-Tropsch fuels with a fuels plant with size a of plant 200 size MW. of Even 200 MW. though Even the though SIC for the the SICcapital-intensive for the capital-intensive construction construction of the pyrolysis of the pyrolysis and gasiﬁcation/synthesis and gasification/synthesis plants appeared plants appearedto be lower to be inlower several in several studies studies on the on bioliq the ®bioliqprocess® process than forthan other for other BtL technologies BtL technol- as ogiesinvestigated as investigated in the SGABin the study,SGAB thestudy, production the production costs (see costs Table (see5 and Table Figure 5 and6) wereFigure found 6) wereto befound higher. to be higher.

Figure 6. Specific fuel production costs in EUR/L for the different studies investigated (blue points), Figureand 6. costs Specific from fuel other production studies blue costs bar in [EUR/L25], red for point the different [7], green studies point [investigated36] and yellow (blue point points), [37]. and costs from other studies blue bar [25], red point [7], green point [36] and yellow point [37]. 3.2.2. BtL Production Cost Distribution 3.2.2. BtLThe Production percentage Cost contribution Distribution of the individual cost items to the specific production costsThe is percentage shown in Tablecontribution5 for all of studies the individu exceptal no. cost 7, initems which to thisthe specific information production was not costsprovided. is shown The in Table total investment5 for all studies costs except for both no. the 7, localin which as well this as information for the central was plantsnot provided.are given. The Since total notinvestment clearly stated costs infor all both studies, the local it is as assumed well as herefor the that central green plants field plantsare given.were Since constructed. not clearly In stated all studies, in all studies, cost factors it is assumed like maintenance here that and green tax field & insurance plants were were constructed.related to theIn all investment studies, cost costs. factors In some like maintenance studies, personnel and tax costs & insurance and overhead were wererelated also to therelated investment also to the costs. total In costs, some while studies, in otherpersonnel studies costs detailed and overhead calculations were were also carried related out alsoto to determine the total costs, the staff while requirements in other studies for plant detailed operation calculations and the were overhead carried composition,out to de- termineincluding the staff administration, requirements marketing, for plant oper medicalation and and safety the overhead services, composition, licenses and patents, includ- as ingwell administration, as research. marketing, medical and safety services, licenses and patents, as well as research.In the following sections the effect of the most relevant economic parameters presented inIn Table the5 followingtogether with sections the relative the effect share of ofthe cost most contributions relevant economic to the production parameters costs, pre- are senteddiscussed. in Table It becomes5 together obvious with the that relative the costs share for of biomass cost contributions and the cost to of the capital production made up costs,the are largest discussed. share. InIt Studybecomes 1, theobvious percentage that the of costs these for two biomass items was and around the cost 85%, of capital but they madehad up the the smallest largest proportion share. In Study in Study 1, the 8 withpercentage approx. of 53%.these two items was around 85%, but theyThe had annual the smallest capital proportion costs were in directly Study dependent8 with approx. on the 53%. investment costs, the depreciation period and the imputed interest rate. Furthermore, the construction time of the The annual capital costs were directly dependent on the investment costs, the depre- plant also influenced the cost of capital due to the time lag before turnover was generated. ciation period and the imputed interest rate. Furthermore, the construction time of the However, the latter was only taken into account in studies no. 1 and 6. In all studies, plant also influenced the cost of capital due to the time lag before turnover was generated. except no. 6, the capital costs ranged between 20% and slightly above 30%. Related to the However, the latter was only taken into account in studies no. 1 and 6. In all studies, except investments, Study 4 showed the highest percentage of capital costs with nearly 15%. This no. 6, the capital costs ranged between 20% and slightly above 30%. Related to the invest- was due to the significantly shorter depreciation period of 10 years. The annual cost of ments, Study 4 showed the highest percentage of capital costs with nearly 15%. This was capital in Study 5 was approximately 8% and had the lowest value compared to the other due to the significantly shorter depreciation period of 10 years. The annual cost of capital studies. This can be traced back to the much lower imputed interest rate, which was 5% in Study 5 was approximately 8% and had the lowest value compared to the other studies. and thus below the average interest rate of 7% in the other studies.

Processes 2021, 9, 684 16 of 23

The costs for maintenance and servicing were also directly dependent on the total investment. The average values used in each study are also given in Table5 and range between approx. 2% and 6% of the capital expenditures. This resulted in a cost contribution between 6 and 18% of the specific production costs. Studies 1 and 6 also included the construction time of the plant into their estimates. The costs incurred due to a lack of income during this period and could be determined from the annual cost of capital using the data provided for the depreciation period and the interest rate. They resulted from the difference between the cost of capital and the annuity. For Study 1, the annual capital costs increased by around 14% by taking into account the construction time, and for Study 6 by as much as 17%. This is an increase of 3% and 6%, respectively, in the total annual costs. The cost of the biomass and the biosyncrude transport costs made a substantial contribution to the production costs of the FT fuel. The share of biomass costs fitted well into other studies revealing 40–60% of biomass costs for production of Fischer-Tropsch hydrocarbons via biomass gasification [8] except for studies 7 and 9 with lower shares due to larger annuities. The costs of the biomass supplied to the plant gate varied between 63 and 72 EUR/t. However, market effects e.g., due to the increased demand of raw material were not taken into account, but could be expected, given the enormous plant capacities of up to 9 Mt in Study 1. Increasing feedstock costs in a sourcing area would lead to the acquisition of new areas with a greater distance, thus increasing feedstock supply costs with regard to limited availability, which, for crop residues in Europe, is estimated to be around 212 Mt/a [38]. For the transport of biosyncrude from the local to the central plants, no explicit information could be found in studies 6 and 7; they are thus included in the costs for the biomass feedstock. However, despite a surcharge for transport, these values were similar to the costs of biomass free plant in the other studies. For the latter, the surcharge for transportation varied between € 7 and € 19 per ton of slurry. This cost contribution also became significant for large scale plant constellations with biosyncrude quantities of up to 6 Mt/a. The differences in the costs for biomass free plant and the biosyncrude transport arose from different assumptions for raw material costs, the sourcing radius of the biomass and its transport costs, the distance of the decentralized locations to the central location and the means of transport used. In contrast to fossil fuels, this kind of considerations will be of decisive importance for scaling biomass-based conversion plants. However, on the basis of the studies investigated here, no detailed comparison can be concluded. The costs for energy were mainly dependent on the plant concept and the corresponding energy flows. Furthermore, there was a dependency on the specified specific energy prices, which, in turn, differed drastically for each study. This influence can be discussed disregarding the studies no. 1, 4 and 7. Due to the self-sufficient concept, energy prices were not relevant for Study 1. For studies 4 and 7, the corresponding data were not provided. It should be noted that the studies mostly offset the excess electricity produced at the central location with the electricity required at the decentralized locations. Since this disregarded any usage fees for the electricity grids, the electricity required, unless otherwise stated, was purchased at the decentralized location and the excess electricity sold at the central location at the same price. This did not affect the calculated costs, but a comparison of the energy costs could be performed. Only Study 3 took a cost difference between feed-in tariffs and electricity consumption into account. A comparison of energy price and demand, as well as the share of electricity cost as given in Table5, shows that the fixed electricity price had an influence on the cost contribution of electrical energy to the production costs, which should not be underestimated. While the specific electricity demand for Study 2 was more than twice that of Study 8, the cost share per liter of FT fuel differed only slightly due to the significantly higher electricity price in Study 8. Table5 also provides information on personnel costs. Studies 3 and 8 stated the annual personnel costs without specific employee wages and number of employees. An average wage of € 60,000 was assumed for these studies, and the resulting number of employees was calculated based on that. A comparison of the personnel requirements was Processes 2021, 9, 684 17 of 23

possible by calculating a specific employee requirement per production site. For Study 3, a staffing requirement of around 50 employees was calculated with an average salary of 60,000 EUR/a. Due to the number of ten decentralized and one central location, a personnel requirement of approx. five employees per location was calculated. Compared to the other studies, this is an exceptionally low value. For the other studies, a trend of decreasing personnel requirements with increasing plant size can be seen, which is plausible with regard to economies of scale. Study 2 was another exception; here, the personnel requirements appeared to be very high compared to the size of the plant and the other studies. In Figure6, the specific production costs found in the investigated studies are compared to other data for gasification based hydrocarbon fuels compiled within a study of IEA Bioenergy [21], which are covered by the blue bar. Calculated for a plant size of 200 MW, different technical developments including the bioliq® process were considered in that study. By optimization, an additional cost reduction potential of up to 15% was estimated by that group for hydrocarbon fuels. The data point taken from Dimitriou et al. [7] was generically calculated for FT fuel produced via entrained flow gasification of woody biomass, marking the lower end of cost estimates performed on BtL fuels. For corn stover, Wang et al. [36] estimated around 1 EUR/L of FT fuel at a production capacity around 240 MW. Finally, a TEA study including the bioliq® process within the RENEW project resulted in 1.42 EUR/L of fuel [37]. In general, there was a clear trend according to the economy of scale. However, for the decentralized/central bioliq® process requiring several plants at different locations, this benefit became apparent only when large conversion capacities were achieved [18,23,37]. It is only then that the decentralized pretreatment for energy densification overcompensated the increasing transportation costs for the direct supply of a large scale synfuel production plant. On the other hand, these considerations were conducted without the important relation to biomass availability and supply as discussed in the introduction. Among the studies examined here, only in the BioBoost study [33] a logistic model was taken into account within a simulation, identifying areas for biomass supply in central Europe and creating a scenario of conversion sites and capacity for pyrolysis and gasification/synthesis plant with minimum costs. A similar study was conducted by Li and Hu [39] based on a supply chain design relevant for the collection of crop residues in Iowa, USA. For conversion, central processing by pyrolysis and gasification, as well as central gasification with decentralized pyrolysis plants, were compared to direct central, as well as decentralized gasification of the biomass. The decentralized plants varied from 2000 t/d to 6000 t/d of feedstock conversion capacity, while the central units were fixed to produce around 1.5 Mt of diesel fuel equivalent. It turned out that the concept with decentralized pyrolysis was better than the centralized one (0.82 vs. 0.84 EUR/L). This way, the decentralized concept could outperform the single site process concept, but not the concept with direct gasification at 0.75 EUR/L, which won the trade-off between biomass supply and operational and capital costs of the processing plants. One reason may be that the sourcing radius considered here did not exceed 200 miles, so that the hybrid concept could not invert that trade-off. Regarding the plant configurations used in the different studies investigated, some conclusions may be drawn. Studies 1 and 4 were based on not too realistic scenarios within central Europe because the overall feedstock capacity appears too large (demanding for nearly up to one third and one twentieth of the agricultural residue potential in Germany and Europe, respectively [38,40]). Scenario 9 considered straw supply chains, but did not allow for multiple feedstock which would reduce costs The overall conversion capacity in study 7 appeared as too small to benefit from the bioliq® concept, resulting in relatively high, but not unreasonable, production costs. According to the state of the art in fast pyrolysis technology today, a feedstock conversion capacity of 50–100 MW can be assumed as realistic for future plants. That way, studies 3, 5, 6, and 8 would be realistic scenarios also in terms of the assumed gasification capacities from 500 to beyond 1000 MW in modular reactors. In study 3, investment cost were unreasonably below the range given by the Processes 2021, 9, 684 18 of 23

IEA study [21] resulting in too low production costs. In study 8, no electricity was by- produced to generate additional revenues. Thus, studies 5, 6, and 8 proviedreasonable cost estimates from a useful combination of useful plant conﬁguration, estimated investment and operational costs and economic parameters, resulting in a production cost range of around (1.5 ± 0.3) EUR/L.

3.3. Model Variations This chapter examines the effects of variations in some of the economic key parameters discussed above. First, the time dependency of the investment estimate is checked using a time dependent plant cost index. Also, the direct effect of learning effects on the production costs is examined. Then, the sensitivity towards the cost items “Investments” and “Biomass and Transport” is investigated. Finally, the cost models are harmonized; the annual capital costs and costs for maintenance and servicing are adjusted to the percentage of the investments for all studies. The operation time is also fixed to one value. The variations are intended to yield a worst case scenario for which the value of the study with the highest percentage is adopted, and a best case scenario in which the lowest value is assumed. The estimation of investments in chemical industrial plants depended on the year in which the plants are planned to be built. This is due to numerous factors, which in turn depended on global economic developments, such as the price of steel. Such chemical plant cost indices are, for example, the Chemical Engineering Plant Cost Index (CEPCI) and the PCD index for chemical plants in Germany (https://www.chemietechnik.de/anlagenbau/ pcd-preisindex-fuer-chemieanlagen-4.html, accessed on 12 April 2021), published by the Chemie Technik journal on a quarterly basis. During the time in which the studies were conducted, significant fluctuation of the average cost indices took place. Therefore, a clear development of investment costs cannot be concluded. From the cost curve fluctuations, deviations of ±5% can be assumed between 2008 and 2015. The plant configuration in Study 5 made use of eleven pyrolysis plants with a thermal fuel capacity of 100 MW each. For the first plant, investments of approx. 42.6 million EUR were estimated. For the following plants, savings due to the construction of additional plants of the same type were assumed using a progress factor, resulting in the 11th plant costing 79% of the first plant. Study 2 deliberately did not take into account the effect of technological learning because the construction of the plants should have occurred, more or less, in parallel. The investments for a pyrolysis plant for wood and straw biomass with an input power of 200 MW amounts to approx. 85.4 million EUR. Study 8, on the other hand, anticipated investments into six pyrolysis plants with 100 MW each to the amount of 57.2 million EUR. By adapting to the model used in Study 5, a progress factor was also applied to the cost estimates of studies 2 and 8, which led to lower investments for the 4th and the 6th plants of 89 and 85%, respectively, as compared to the original cost estimates for the local plants. The fuel production cost would be reduced by about 2% in both cases. On the other hand, without learning effects, the production costs in Study 5 would increase by 4% due to the large number of plants, which significantly benefited from the learning effect. The investment, and the biomass and transportation costs, significantly contributed to the production costs. Therefore, their effect was studied by increasing their values by 10 and 30%, without changing any other study-specific economical parameter. In the first case, as depicted in Figure7, the fuel production costs rose by 4–7% and 2–7%, respectively, when the associated investment and feedstock supply costs rose. When the costs were increased by 30%, the production costs were found to rise accordingly by 11–17% and 7–17%, respectively. Thus, the changes in investment and feedstock supply cost had a similar influence on the fuel production cost, even though the cost share of biomass costs was larger than that of the investment costs (see Table5), which were only part of the capital costs. The smallest effect of biomass and transportation costs, which were not separated because of a lack of information in the studies, was found in studies 6 and 8, where the share of feedstock supply in the production costs had the lowest values. Processes 2021, 9, 684 19 of 23

and 7–17%, respectively. Thus, the changes in investment and feedstock supply cost had a similar influence on the fuel production cost, even though the cost share of biomass costs was larger than that of the investment costs (see Table 5), which were only part of the capital costs. The smallest effect of biomass and transportation costs, which were not separated because of a lack of information in the studies, was found in studies 6 and 8, where Processes 2021, 9, 684 19 of 23 the share of feedstock supply in the production costs had the lowest values.

Study no. 1 20 % Change of production cost

15 8 2 10 Investment +10 %

5 Investment + 30 % 0 Biomass/Transport + 10 % 6 3

Biomass/Transport + 30 %

5 4

Figure 7. Change of production cost by increasing the investment and biomass/transportation costs Figureby 107. Change and 30%. of production cost by increasing the investment and biomass/transportation costs by 10 and 30%. Processes 2021, 9, 684 In the following, the effects of adjusting some economic key parameters on the20 of calcu- 23 lated production costs are examined. Namely, the percentages of the investment costs to In the following, the effects of adjusting some economic key parameters on the cal- calculate the capital costs and the costs for maintenance and repair, as well as the operation culated production costs are examined. Namely, the percentages of the investment costs time are fixed. Worst case and best case data were determined using the highest and the to calculateThe operation the capital time costs for andthe productionthe costs fo rplants maintenance was assumed and repair, to be as between well as 7000the oper- and lowest value of these parameters from the studies to calculate the production costs. The 8000ation h/a, time which are fixed. had Worsta direct case effect and on best the case variable data costswere anddetermined the amount using of the fuel highest produced. and resulting deviations are compiled in Figure8. By aligning these factors of the cost model, the lowest value of these parameters from the studies to calculate the production costs. Thisquantitative resulted in differencesan increase canin production be shown thatcosts arise of up solely to 7% from in studies the choice 4 and of 8. the In the cost best- model, caseThesince resultingscenario, the balance withdeviations 8000 of the h are of material operationcompiled and perin energy Figure annum, flows, 8. theBy theproductionaligning determined these costs factors investments in studies of the 5 and costand the 6model, reducedspecific quantitative by costs 7% ofand the differences 8%, operating respectively. can resources be shown maintain that arise the solely study-specific from the values. choice of the cost model, since the balance of the material and energy flows, the determined investments and the specific costs of the operating resources maintain the study-specific values. In the worst-case scenario for the capital costs of 14.3% (see Table 5), the production costs for five of the six studies increased significantly by 7% for Study 1 to 18% for Study 5. The effect on production costs for Study 6, with 2%, was much lower. In contrast to this, the best-case scenario (8.8% of investment costs) for Study 6 showed a production cost reduction by 16%, while it was around −2% to −7% in all other studies. These differences can be explained by the different dependency of the manufacturing costs on the capital costs. In the same way as the cost of capital, the effects of variations in the costs of maintenance and servicing on the manufacturing costs were examined and are displayed in Fig- ure 8. In the cost models of the studies, the highest value occurred in Study 4, and the lowest value occurred in Study 6. The worst-case scenario, with a cost share of 6% of the investments for maintenance and repairs, led to an increase of production costs by more than 5% in all studies, and up to 14% in Study 6, where originally a percentage of 2% was used for maintenance. Since the cost model of Study 8 also made use of a cost share of close to 6%, the effect of 0.1% was very low. In the best-case scenario, the share of maintenance and servicing was only 2% of the investments. This adjustment reduced the production costs of the studies by between 5% and 12%. Figure 8. Changes of production cost in percentages by worst- and best-case adjustments of shares for capital cost and maintenance and of operation time. Figure 8. Changes of production cost in percentages by worst- and best-case adjustments of shares for capitalIn cost the and worst-case maintena scenarionce and of for operation the capital time. costs of 14.3% (see Table5), the production costs for five of the six studies increased significantly by 7% for Study 1 to 18% for Study 5.So The far, effect the economic on production parameters costs forhave Study been 6, altered with 2%, without was much changing lower. the In other contrast pa- to rametersthis, the used best-case in the different scenario studies. (8.8% of Theref investmentore, the costs)effect forof accumulated Study 6 showed worst-case a production and best-casecost reduction values on bythe 16%, change while of production it was around costs −have2% also to − been7%in estimated. all other For studies. the worst These case, capital costs were calculated at 14.3% and costs for maintenance and repairs at 6% of the investments for 7000 h of operation time. Figure 9 shows that in this scenario the study-specific production costs increased by 7% to 27%. In the best-case scenario, with an operation time of 8000 h/a, the capital costs were 8.8% and the costs for maintenance and repairs were 1.9% of the investments. In the best case, fuel production costs may be reduced by 22%.The results of these scenario variations showed large differences in the effects on manufacturing costs between the studies. This is a consequence of the differently weighed proportions of the investment-dependent costs in the production cost calculation of each study and the difference in the values between standard and variation scenarios. The pronounced cost differences, while maintaining the study-specific material and energy flows and specific variable costs, underline the strong dependency of the calculated production costs on the cost model used.

Processes 2021, 9, 684 20 of 23

differences can be explained by the different dependency of the manufacturing costs on the capital costs. In the same way as the cost of capital, the effects of variations in the costs of maintenance and servicing on the manufacturing costs were examined and are displayed in Figure8. In the cost models of the studies, the highest value occurred in Study 4, and the lowest value occurred in Study 6. The worst-case scenario, with a cost share of 6% of the investments for maintenance and repairs, led to an increase of production costs by more than 5% in all studies, and up to 14% in Study 6, where originally a percentage of 2% was used for maintenance. Since the cost model of Study 8 also made use of a cost share of close to 6%, the effect of 0.1% was very low. In the best-case scenario, the share of maintenance and servicing was only 2% of the investments. This adjustment reduced the production costs of the studies by between 5% and 12%. The operation time for the production plants was assumed to be between 7000 and 8000 h/a, which had a direct effect on the variable costs and the amount of fuel produced. This resulted in an increase in production costs of up to 7% in studies 4 and 8. In the best-case scenario, with 8000 h of operation per annum, the production costs in studies 5 and 6 reduced by 7% and 8%, respectively. So far, the economic parameters have been altered without changing the other parameters used in the different studies. Therefore, the effect of accumulated worst-case and best-case values on the change of production costs have also been estimated. For the worst case, capital costs were calculated at 14.3% and costs for maintenance and repairs at 6% of the investments for 7000 h of operation time. Figure9 shows that in this scenario the study-specific production costs increased by 7% to 27%. In the best-case scenario, with an operation time of 8000 h/a, the capital costs were 8.8% and the costs for maintenance and repairs were 1.9% of the investments. In the best case, fuel production costs may be reduced by 22%.The results of these scenario variations showed large differences in the effects on manufacturing costs between the studies. This is a consequence of the differently weighed proportions of the investment-dependent costs in the production cost calculation of each study and the difference in the values between standard and variation scenarios. The pronounced cost differences, while maintaining the study-specific material and en- Processes 2021, 9, 684 21 of 23 ergy flows and specific variable costs, underline the strong dependency of the calculated production costs on the cost model used.

FigureFigure 9. 9. ChangeChange of production costcost byby using using accumulated accumulated best- best- and and worst-case worst-case economic economic parameters. parameters.

4. Conclusions In this study, investigations by various research institutions on the costs for the production of BtL fuels using the bioliq® process were analyzed and evaluated. Both their specific approach with regard to the material and energy flows and the cost model used to calculate the specific fuel production costs were taken into account. Differences in conversion efficiency were large in those cases when assumptions for electricity generation at the central location were taken into account. However, since all studies were based on published data of the bioliq® process, the fair agreement in the calculated efficiencies was not surprising. In relation to the energy content of the fuel produced, the estimated conversion efficiency of all studies showed values around (36 ± 3)%. This is below the data provided by the survey on advanced fuels of IEA Bioenergy [21] but can be explained by the additional efforts of pretreatment by pyrolysis. In agreement with other studies on BtL and advanced fuels it was found that the largest cost items were attributable to biomass (including transport of the biosyncrude) and the capital costs. Comparison of the specific investments per installed megawatt of the plants showed that, in addition to economies of scale, particularly the origin of the equipment purchase costs and the cost factors applied to them, induced significant variations. Major differences between the studies also occurred in the cost of capital calculation. Significant deviations were already found in the depreciation period and the amount of the assumed interest rate. The effect of the dates of origin of the studies could not be clearly determined because of the intense fluctuations of chemical plant costs in the investigated period of time but could be assumed to contribute ±5% of the production costs. Two studies took a construction period that accounted for a proportion of the fuel production costs into account, which should not be underestimated. Even though staff costs were below 10% in all cases, differences by a factor of four could be found due to the strength of the required workforce, assuming that all plants were planned for being constructed and operated in Germany. Additional cost variations may occur due to different staff costs and other issues related to cross-border activities in addition to equipment purchase. The use of learning potentials for the several local plants led, in one case investigated, to cost savings of around 20% when more than 10 plants are constructed. The effect on the fuel production costs was around 3% in this case. Finally, adjustments to the cost model of the studies showed the importance of the leverage of

Processes 2021, 9, 684 21 of 23

4. Conclusions In this study, investigations by various research institutions on the costs for the production of BtL fuels using the bioliq® process were analyzed and evaluated. Both their specific approach with regard to the material and energy flows and the cost model used to calculate the specific fuel production costs were taken into account. Differences in conversion efficiency were large in those cases when assumptions for electricity generation at the central location were taken into account. However, since all studies were based on published data of the bioliq® process, the fair agreement in the calculated efficiencies was not surprising. In relation to the energy content of the fuel produced, the estimated conversion efficiency of all studies showed values around (36 ± 3)%. This is below the data provided by the survey on advanced fuels of IEA Bioenergy [21] but can be explained by the additional efforts of pretreatment by pyrolysis. In agreement with other studies on BtL and advanced fuels it was found that the largest cost items were attributable to biomass (including transport of the biosyncrude) and the capital costs. Comparison of the specific investments per installed megawatt of the plants showed that, in addition to economies of scale, particularly the origin of the equipment purchase costs and the cost factors applied to them, induced significant variations. Major differences between the studies also occurred in the cost of capital calculation. Significant deviations were already found in the depreciation period and the amount of the assumed interest rate. The effect of the dates of origin of the studies could not be clearly determined because of the intense fluctuations of chemical plant costs in the investigated period of time but could be assumed to contribute ±5% of the production costs. Two studies took a construction period that accounted for a proportion of the fuel production costs into account, which should not be underestimated. Even though staff costs were below 10% in all cases, differences by a factor of four could be found due to the strength of the required workforce, assuming that all plants were planned for being constructed and operated in Germany. Additional cost variations may occur due to different staff costs and other issues related to cross-border activities in addition to equipment purchase. The use of learning potentials for the several local plants led, in one case investigated, to cost savings of around 20% when more than 10 plants are constructed. The effect on the fuel production costs was around 3% in this case. Finally, adjustments to the cost model of the studies showed the importance of the leverage of financing. By harmonizing the depreciation period and the assumed interest rate, the production costs changed from −16% to +17%. Similar effects were achieved by adjusting the costs for maintenance, servicing and the plant operation time. In the best-case scenario, which adopted the most beneficial combination of economic parameters used in the studies, the fuel production costs could be reduced by up to 21%. The most expensive variant in the opposing worst-case scenario, on the other hand, raised costs by up to 27%. That way, the harmonization of economic parameters generated an uncertainty that contributed, by more than half to the cost accuracy level class 4 of the AACE classification [41], of a 15–30% lower and a 20–50% higher limit relevant to a preliminary estimate based on a conceptual design be available at TRL6.

Author Contributions: Conceptualization, J.S., methodology, N.D., J.S.; data analysis, N.D.; investigation, N.D.; original draft preparation, N.D.; writing—review and editing, N.D.; supervision, J.S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study did not involve humans. Informed Consent Statement: The study does not report any supporting data. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References 1. Huber, G.W.; Iborra, S.; Corma, A. Synthesis of transportation fuels from biomass: Chemistry, catalysts, and engineering. Chem. Rev. 2006, 106, 4044–4098. [CrossRef] Processes 2021, 9, 684 22 of 23

2. Grim, R.G.; To, A.T.; Farberow, C.A.; Hensley, J.E.; Ruddy, D.A.; Schaidle, J.A. Growing the Bioeconomy through Catalysis: A Review of Recent Advancements in the Production of Fuels and Chemicals from Syngas-Derived Oxygenates. ACS Catal. 2019, 9, 4145–4172. [CrossRef] 3. Yarulina, I.; Chowdhury, A.D.; Meirer, F.; Weckhuysen, B.M.; Gascon, J. Recent trends and fundamental insights in the methanol- to-hydrocarbons process. Nat. Catal. 2018, 1, 398–411. [CrossRef] 4. Dahmen, N.; Henrich, E.; Henrich, T. Synthesis Gas Biorefinery; Springer: Berlin/Heidelberg, Germany, 2019; Volume 166. 5. Albrecht, F.G.; König, D.H.; Baucks, N.; Dietrich, R.U. A standardized methodology for the techno-economic evaluation of alternative fuels—A case study. Fuel 2017, 194, 511–526. [CrossRef] 6. Snehesh, A.S.; Mukunda, H.S.; Mahapatra, S.; Dasappa, S. Fischer-Tropsch route for the conversion of biomass to liquid fuels—Technical and economic analysis. Energy 2017, 130, 182–191. [CrossRef] 7. Dimitriou, I.; Goldingay, H.; Bridgwater, A.V. Techno-economic and uncertainty analysis of Biomass to Liquid (BTL) systems for transport fuel production. Renew. Sustain. Energy Rev. 2018, 88, 160–175. [CrossRef] 8. Kargbo, H.; Harris, J.S.; Phan, A.N. “Drop-in” fuel production from biomass: Critical review on techno-economic feasibility and sustainability. Renew. Sustain. Energy Rev. 2021, 135, 110168. [CrossRef] 9. Brown, T.R. A techno-economic review of thermochemical cellulosic biofuel pathways. Bioresour. Technol. 2015, 178, 166–176. [CrossRef][PubMed] 10. Meyer, B.; Krzack, S.; Stahlschmidt, R.; Boblenz, K. Ermittlung Spezifizierter Kosten und Ökologischer Auswirkungen der Erzeugung von BtL-Kraftstoffen und Biogas; TU Bergakademie Freiberg, Institut für Energieverfahrenstechnik und Chemieingenieurwesen: Freiberg, Germany, 2010. 11. Kumar, A.; Cameron, J.B.; Flynn, P.C. Biomass Power Cost and Optimum Plant Size in Western Canada. Biomass Bioenergy 2003, 24, 445–464. [CrossRef] 12. Brown, D.; Rowe, A.; Wild, P. A techno-economic analysis of using mobile distributed pyrolysis facilities to deliver a forest residue resource. Bioresour. Technol. 2013, 150, 367–376. [CrossRef] 13. Maung, T.A.; Gustafson, C.R.; Saxowsky, D.M.; Nowatzki, J.; Miljkovic, T.; Ripplinger, D. The logistics of supplying single vs. multi-crop cellulosic feedstocks to a biorefinery in southeast North Dakota. Appl. Energy 2013, 109, 229–238. [CrossRef] 14. Sultana, A.; Kumar, A. Optimal configuration and combination of multiple lignocellulosic biomass feedstocks delivery to a biorefinery. Bioresour. Technol. 2011, 102, 9947–9956. [CrossRef] 15. Leible, L.; Kälber, S.; Kappler, G. KIT Scientific Reports 7580: Systemanalyse zur Gaserzeugung aus Biomasse; Karlsruhe Institute of Technology: Karlsruhe, Germany, 2011. 16. Biomass Based Energy Intermediates Boosting Biofuel Production—Executive Summary. Available online: http://www.bioboost. eu/results/public-results.htm. (accessed on 12 April 2021). 17. Gunukula, S.; Daigneault, A.; Boateng, A.A.; Mullen, C.A.; DeSisto, W.J.; Wheeler, M.C. Influence of upstream, distributed biomass-densifying technologies on the economics of biofuel production. Fuel 2019, 249, 326–333. [CrossRef] 18. Brown, D.; Rowe, A.; Wild, P. Techno-economic comparisons of hydrogen and synthetic fuel production using forest residue feedstock. Int. J. Hydrogen Energy 2014, 39, 12551–12562. [CrossRef] 19. Brown, T.R.; Thilakaratne, R.; Brown, R.C.; Hu, G. Techno-economic analysis of biomass to transportation fuels and electricity via fast pyrolysis and hydroprocessing. Fuel 2013, 106, 463–469. [CrossRef] 20. Carrasco, J.L.; Gunukula, S.; Boateng, A.A.; Mullen, C.A.; DeSisto, W.J.; Wheeler, M.C. Pyrolysis of forest residues: An approach to techno-economics for bio-fuel production. Fuel 2017, 193, 477–484. [CrossRef] 21. Brown, A.; Waldheim, L.; Landälv, I.; Saddler, J.; Ebadian, M.; McMillan, J.; Bonomi, A.; Klein, B. Advanced Biofuels-Potential for Cost Reduction; IEA Bioenergy: Paris, France, 2020. 22. Zimmerlin, B.; Eberhard, M.; Lam, H.; Mai, R.; Michelfelder, B.; Niebel, A.; Otto, T.; Pfitzer, C.; Weih, N.; Willy, M.; et al. Ther- mochemische Konversion-Schlüsselbaustein für zukünftige Energie-und Rohstoffsysteme bioliq®-Pilotanlage zur Herstellung synthetischer Kraftstoffe-Betriebserfahrungen. In DGMK Tagungsbericht 2019-2; DGMK: Hamburg, Germany, 2019. 23. Leible, L.; Kälber, S.; Kappler, G.; Lange, S.; Nieke, E.; Proplesch, P.; Wintzer, D.; Fürniss, B. Wissenschaftliche Berichte FZKA 7170: Kraftstoff, Strom und Wärme aus Stroh und Waldrestholz; Forschungszentrum: Karlsruhe, Germany, 2007. 24. Lange, S. Systemanalytische Untersuchung zur Schnellpyrolyse als Prozessschritt bei der Produktion von Synthese-Kraftstoffen aus Stroh und Waldrestholz; Universität Karlsruhe (TH): Karlsruhe, Germany, 2007. 25. Kerdoncuff, P. Modellierung und Bewertung von Prozessketten zur Herstellung von Biokraftstoffen der Zweiten Generation; Universität Karlsruhe (TH): Karlsruhe, Germany, 2008. 26. Dahmen, N.; Henrich, E.; Dinjus, E. Cost estimate for biosynfuel production by biosyncrude gasification. Biofuels Bioprod. Biorefin. 2009, 3, 28–41. 27. Trippe, F. Techno-Ökonomische Bewertung Alternativer Verfahrenskonfigurationen zur Herstellung von Biomass-to-Liquid (BtL) Kraftstoffen und Chemikalien. Ph.D. Thesis, Karlsruhe Institute of Technology, Karlsruhe, Germany, 2013. 28. Trippe, F.; Fröhling, M.; Schultmann, F.; Stahl, R.; Henrich, E. Techno-economic assessment of gasification as a process step within biomass-to-liquid (BtL) fuel and chemicals production. Fuel Process. Technol. 2011, 92, 2169–2184. [CrossRef] 29. Trippe, F.; Fröhling, M.; Schultmann, F.; Stahl, R.; Henrich, E. Techno-economic analysis of fast pyrolysis as a process step within biomass-to-liquid fuel production. Waste Biomass Valorization 2010, 1, 415–430. [CrossRef] Processes 2021, 9, 684 23 of 23

30. Deutsche Energie-Agentur GmbH (dena). Biomass to Liquid-BtL Realisierungsstudie Zusammenfassung; Deutsche Energie-Agentur: Berlin, Germany, 2006. 31. Stahlschmidt, F.; Boblenz, K.; Krzack, S.; Meyer, B. Ermittlung spezifizierter Kosten und ökologischer Auswirkungen der Erzeugung von BtL-Kraftstoffen. Erdöl Erdgas Kohle 2010, 126, 346–350. 32. Beiermann, D. Analyse von thermochemischen Konversionsverfahren zur Herstellung von BtL-Kraftstoffen; Fortschrittberichte VDI: Karlsruhe, Germany, 2010; Volume 596. 33. Mireles, I.H.; Van Horssen, A.; Van Harmelen, T.; Hagen, E. BioBoost Deliverable D6.4: Energy Carrier Chain LCA: Sustainability Assessment of Energy Carriers. Available online: www.BioBoost.eu (accessed on 12 April 2021). 34. Seyfried, F. RENEW Final Report. Available online: www.renew-fuel.com (accessed on 12 April 2021). 35. Landälv, I.; Waldheim, L.; Maniatis, K. (Eds.) Continuing the Work of the Sub Group on Advanced Biofuels for the RED II Market Deployment for Advanced Biofuels; ART Fuels Forum: Karlsruhe, Germany, 2018. 36. Wang, B.; Gebreslassie, B.H.; You, F. Sustainable design and synthesis of hydrocarbon biorefinery via gasification pathway: Integrated life cycle assessment and technoeconomic analysis with multiobjective superstructure optimization. Comput. Chem. Eng. 2013, 52, 55–76. [CrossRef] 37. Müller-Langer, F.; Vogel, A.; Brauer, S. RENEW-Renewable Fuels for Advanced Powertrains Integrated Project Sustainable energy Systems—Deliverable 5.3.8 Overall Costs. Available online: http://www.renew-fuel.com/fs_documents.html (accessed on 12 April 2021). 38. Scarlat, N.; Fahl, F.; Lugato, E.; Monforti-Ferrario, F.; Dallemand, J.F. Integrated and spatially explicit assessment of sustainable crop residues potential in Europe. Biomass Bioenergy 2019, 122, 257–269. [CrossRef] 39. Li, Q.; Hu, G. Techno-economic analysis of biofuel production considering logistic configurations. Bioresour. Technol. 2016, 206, 195–203. [CrossRef][PubMed] 40. Brosowski, A.; Krause, T.; Mantau, U.; Mahro, B.; Noke, A.; Richter, F.; Raussen, T.; Bischof, R.; Hering, T.; Blanke, C.; et al. How to measure the impact of biogenic residues, wastes and by-products: Development of a national resource monitoring based on the example of Germany. Biomass Bioenergy 2019, 127, 105275. [CrossRef] 41. Christensen, P.; Dysert, L.R.; Bates, J.; Burton, D.; Creese, R.C.; Hollmann, J. 18R-97: Cost Estimate Classification System—As Applied in Engineering, Procurement, and Construction for the Process Industries; AACE, Inc.: Morgantown, WV, USA, 2005.