Process analysis and techno-economic comparison of aviation biofuel production via microbial oil and ethanol upgrading

Full Length Article Process analysis and techno-economic comparison of aviation biofuel production via microbial oil and ethanol upgrading Vasiliki Kaperneka , Leda Maragoudaki , Konstantinos Atsonios * Centre for Research & Technology Hellas /Chemical Process and Energy Resources Institute (CERTH/CPERI), 6th Km. Charilaou-Thermis, GR 570 01 Thermi, Greece ARTICLE INFO Keywords: Sustainable Aviation Fuel (SAF) Microbial oil Biomass-to-Liquid (BtL) Hydrotreatment (HDT) Alcohol to Jet (AtJ) Process simulations Techno Economic Analysis (TEA) ABSTRACT The transport sector is the largest source of greenhouse gases in the EU after the energy supply one, contributing approximately 27% of total emissions. Although decarbonization pathways for light-duty transport are relatively well established, heavy-duty transport, shipping and aviation emissions are difficult to eliminate through electrification. In particular, the aviation sector is strongly dependent on liquid hydrocarbons, making the development of sustainable aviation fuels (SAFs) a critical priority for achieving long-term climate targets. This study evaluates four biomass-to-liquid pathways for producing jet-like SAF from lignocellulosic biomass: (1) triacylglycerides (TAGs) production from syngas fermentation, (2) TAGs production from sugar fermentation, (3) ethanol production from syngas fermentation, and (4) ethanol production from sugar fermentation. These pathways are simulated using Aspen Plus™, and the mass and heat balances obtained are used to assess their technical performance (e.g., carbon utilization, energetic fuel efficiency) and techno-economic viability (e.g., production cost, capital investment). Pathway (4) demonstrated the highest jet fuel selectivity (63%) and total carbon utilization (32.5%), but at higher power demands. Pathway (1) was self-sufficient in energy due to internal syngas utilization but exhibited lower carbon efficiencies. Cost analysis revealed that microbial oil-based pathways were restrained by higher hydrogen demands and lower product selectivity compared to ethanol-based routes. However, with advancements in microbial oil production efficiency and reduced water usage, these pathways could become competitive. 1. Introduction Global warming and climate change are considered among the most crucial concerns that threaten the survival of humanity and ecosystems on earth. To prevent the catastrophic consequences of exceeding 1.5 ◦C global warming, zero-emission technologies must be rapidly deployed at a commercial scale [1]. The increasing global emphasis on climate change mitigation, driven by international agreements such as the Paris Agreement, has placed the aviation and maritime sectors under intense inspection due to their growing contributions to greenhouse gas (GHG) emissions. Without significant intervention, aviation emissions could reach 21.2 Gt CO 2 by 2050, with over 10 billion annual passengers [2], while maritime CO 2 emissions could increase by up to 250 % compared to 2012 [3]. Transportation is the second sector after energy with the largest GHG emissions [4–6] and it remains the only sector in the EU where emissions have increased over the past 30 years [5,7]. Unlike the road and railway transports that can eliminate their emissions through electrification, the aviation sector must rely only on the development of sustainable fuels with similar properties to the fossil-derived ones. This is the only way for long distance flights to be carbon neutral since there is no alternative technology at high attitudes than the aircraft turbo engine [8]. In the maritime sector, advanced biofuels offer the advantage of maintaining existing vessel infrastructure and operations while being safer than gaseous and explosive fuels [9]. Biofuels have emerged as a promising strategy to decarbonize sectors like transport, with the International Energy Agency (IEA) predicting that they could supply up to 27 % of total transport fuels by 2050 [10]. In aviation, Sustainable Aviation Fuels (SAFs) are particularly crucial for reducing emissions, as aircraft engines require fuels that meet strict specifications similar to fossil-derived jet fuel. Among the currently approved SAF pathways, Hydroprocessed Esters and Fatty Acids (HEFA) dominate commercial production due to being the only market-proven and cost-competitive option [11]. However, HEFA faces significant challenges, such as the limited availability and high costs of feedstocks like used cooking oil (UCO) and animal fats, which also offer the greatest environmental benefit. These constraints, combined with concerns over * Corresponding author at: Egialias 52 Maroussi, Athens, Greece. E-mail address: [email protected] (K. Atsonios). Contents lists available at ScienceDirect Fuel journal homepage: www.elsevier.com/locate/fuel https://doi.org/10.1016/j.fuel.2025.137118 Received 28 April 2025; Received in revised form 15 September 2025; Accepted 9 October 2025 Fuel 406 (2026) 137118 Available online 16 October 2025 0016-2361/© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). the GHG reduction potential of some first-generation biofuels, raise skepticism about HEFA’s long-term sustainability. Consequently, there is growing interest in alternative technologies that can use more abundant and sustainable biomass feedstocks, such as lignocellulosic residues. Over the next two decades, advanced feedstock types (i.e. marginal crops, biogenic residues, wastes, algae) and technologies are expected to mature, addressing the feedstock limitations of HEFA and contributing to the scale-up of advanced biofuels production [8]. One of the promising feedstock types that is expected to support advanced oil in the HEFA based route is microbial oil, which is commonly defined as lipids that are accumulated by oleaginous microorganisms that are able to accumulate intracellular oil at more than 20 % of their cell dry weight [12]. The composition of that type of oils has strong similarities with those of vegetable oils and can potentially be a promising source of feedstock for HEFA based plants. Some studies in the literature investigate the economic feasibility of using microbial oil as feedstock for SAF production: Karamerou et al. showed that the microbial oil production cost from 1G feedstock (sugarcane) can range from $1.2–$1.81/kg depending on scale, and productivity [13]. Recently, Marchesan et al. revealed that the SAF production cost of SAF from microbial oil from the same substrate is between $1.83 and $3.00 per liter [14]. There are various C-sources that can be used as substrates for lipids production from yeast fermentation, such as acetate [15], sugars (glucose) and glycerol [16]. If the production of such substrates is performed with feedstocks that are eligible with Annex IX of Renewable Energy Directive (RED) II [17], the final aviation and marine biofuels can be considered as sustainable. The rate of effectiveness and costcompetitiveness compared to other advanced biofuel pathways needs further investigation. Another pathway for producing SAF is the Alcohol-to-Jet (ATJ) process, where alcohols, primarily ethanol, are upgraded catalytically to jet fuel through consecutive dehydration to olefins, oligomerization, and hydrogenation. The two main ATJ routes involve either ethylene oligomerization or the Guerbet reaction [18,19]. A critical comparison between these two options shows that ethylene-based oligomerization is superior to the Guerbet reaction in jet fuel production [20]. For that pathway, the most critical aspect from a technical and economic point of view is the efficient production of bioethanol from advanced biomass feedstock. There are two primary pathways for bioethanol production: the hydrolysis-fermentation route, where biomass is first broken down with chemicals and enzymes and then fermented by microorganisms like yeast to produce ethanol, and the gasification route, in which biomassderived synthesis gas (a mix of H 2 , CO, and CO 2 ) is fermented by bacteria to produce ethanol [21]. From the economic point of view, various studies of the ATJ route report minimum selling prices ranging from 0.88 to 0.93 € /L for 1G ethanol use as feedstock [22], 1.49 € /L when advanced feedstock such as corn stover is employed [23], and higher up to 2.5 € /L for cases that agricultural residues is used as initial feedstock for ethanol synthesis [24]. Regardless of the origin of ethanol, its cost plays the most significant factor (>90 %) at the formulation of jet fuel breakeven price [23,25]. Despite increasing interest in sustainable aviation fuels (SAFs), most techno-economic work to date has focused on either (a) ethanol upgrading pathways (Alcohol-to-Jet, ATJ) or (b) microbial oil production from first-generation feedstocks, but rarely compares these routes on the same technical and economic footing using lignocellulosic feedstocks. To place our analysis in context we compared representative techno-economic studies across the main SAF pathways (Table 1). The literature shows detailed TEAs for ATJ that identify ethanol feedstock costs as the dominant driver (Yao/Tao 2017) [22,23], and several recent TEAs and industry reports for syngas fermentation (Regis et al. 2023) [21] and commercial demonstrations of gas-fermentation ATJ (LanzaTech/LanzaJet) [26,27]. In contrast, techno-economic studies of microbial oils (Karamerou et al., Marchesan et al.) [13,14] have mainly considered 1G feedstocks or a limited set of scenarios and emphasize the sensitivity of final lipid costs to productivity, extraction energy and scale. This motivates the present study’s direct, side-by-side TEA of ethanol-based ATJ and microbial-oil upgrading using the same lignocellulosic feedstock and consistent costing assumptions, which — to the best of our knowledge — has not been reported previously. Addressing this gap will reveal whether differences in final SAF cost are driven mainly by fundamental conversion chemistry and mass-balance constraints or by uncertain economic assumptions (CAPEX/OPEX, water use, scale-up effects), and therefore where technological R&D should be prioritized. Nomenclature Abbreviation List AD Anaerobic Digestion ATJ Alcohol to Jet BPC Biofuels Production Cost BtL Biomass to Liquid CAPEX Capital Expenditure CEPCI Chemical Engineering Plant Cost Index CU Carbon Utilization CW Cooling Water DFBG Dual Fluidized Bed Gasifier EFE Energetic Fuel Efficiency FCI Fixed Capital Investment GHG Greenhouse Gas HDT Hydrotreatment HEFA Hydroprocessed Esters and Fatty Acids HHV Higher Heating Value HMF Hydroxymethylfurfural IEA International Energy Agency LCA Life Cycle Assessment LP Low Pressure LT-HP Low Temperature −High Pressure NREL National Renewable Energy Laboratory NRTL Non-random two-liquid OPEX Operating Expenditures PSA Pressure Swing Adsorption RED Renewable Energy Directive SAF Sustainable Aviation Fuel ST Steam Turbine TAG Triglyceride TCI Total Capital Investment TDIC Total Direct and Indirect Cost TIC Total Installed Cost UCO Used Cooking Oil WGS Water-Gas Shift WWTP Waste Water Treatment Plant Subscript th thermal e electrical SYN-HDT biomass gasification to SYNgas to TAGs HyDroTreatment pathway SUG-HDT biomass hydrolysis to SUGars to TAGs HyDroTreatment pathway SYN-ATJ biomass gasification to SYNgas to Alcohol to Jet pathway SUG-ATJ biomass hydrolysis to SUGars to Alcohol to Jet pathway V. Kaperneka et al. Fuel 406 (2026) 137118 2 This study introduces four biomass-to-liquid (BtL) pathways for producing drop-in biofuels for aviation, integrating thermochemical and biochemical processes. These pathways include (1) biomass gasification followed by syngas fermentation to acetate, which is then converted to microbial oil and hydrotreated to produce jet fuel; (2) biomass hydrolysis to sugars, followed by fermentation to microbial oil and subsequent hydrotreatment; (3) biomass gasification with syngas fermentation to ethanol, upgraded through the Alcohol-to-Jet (ATJ) process; and (4) biomass hydrolysis, sugar fermentation to ethanol, and conversion to jet fuel via the ATJ process. These routes leverage lignocellulosic biomass (wheat straw), a non-food, advanced feedstock, aligning with the sustainability criteria set by CORSIA [36] and the EU’s RED II [17]. The aim of this study is to assess the performance of these four pathways, the second of which is presented for the first time, in terms of technical (i.e. product yield, carbon utilization, and overall energy efficiency) and techno-economic point of view (i.e. cost production, specific capital investment). As the microbial oil pathways are less mature than the ethanol-based one, this research provides the first comprehensive techno-economic assessment of SAF production at an industrial scale from microbial oil derived from advanced feedstock and benchmarks its competitiveness against ATJ. Moreover, the detailed process integration and the respective cost estimation and assessment enable the extraction of useful findings on how these novel pathways can be more competitive and to what extent. By bridging this research gap, this study contributes to the development of scalable and costeffective alternatives to HEFA, helping the aviation sector meet its long-term decarbonization goals. The novelty of this study lies in its comprehensive evaluation of microbial oil pathways for SAF production, providing a unique comparison with the established ATJ process at an industrial scale, which has not been previously explored in the literature. 2. Concept description The production processes of the four investigated concepts are shown in Fig. 1. To evaluate the concepts on equal terms, the same feedstock is Table 1 Summary of representative techno-economic assessments (TEAs) and reviews across major SAF pathways (ATJ, HEFA, FT/PtL, syngas fermentation, microbial oils), highlighting feedstocks, scale & key findings. Study (Year) Pathway / Focus Feedstock TRL / Scale Key Findings Karamerou et al. (2021) [13] Microbial lipids (oleaginous yeasts) TEA 1G sugars (sugarcane) Pilot / modelling Lipid cost ≈1.2–1.8 € /kg; scale & productivity critical Marchesan et al. (2025) [14] Microbial oil → HEFA upgrading Sugarcane (1G) Pilot assumptions SAF cost varies with assumptions; feedstock important Yao et al. (2017) [22] / Tao et al. (2017) [23] Alcohol-to-Jet TEA Corn grain, corn stover, sugarcane Detailed plant TEA Ethanol feedstock cost dominates ATJ SAF price Regis et al. (2023) [21] Syngas fermentation → ethanol TEA Switchgrass (lignocellulose) Conceptual modelling Syngas cleanup & integration key for ethanol economics LanzaTech / LanzaJet (2021–2023) [26,27] Gas fermentation ethanol → ATJ (industrial demo) Industrial off-gases, woody residues Commercial demo Industrial validation of gas-fermentation ATJ pathway Wang et al. (2022) [28] FT-to-jet / PtL TEA Various biomass / syngas TEA comparison study FT can yield high efficiencies; cost depends on syngas Collis et al. (2022) [29] FT from steel-mill gases TEA & LCA Steel-mill off-gas Simulation / TEA / LCA FT SAF viable from industrial gases; emissions reduced Detsios et al. (2024) [30] Gasification-driven BtL TEA Lignocellulosic biomass Conceptual modelling Gasification concepts benchmark BtL costs Gallego-García et al. (2022) [31] / GallegoGarcía et al. (2023) [32] Yeast-based microbial oil review Lignocellulosic sugars / wastes Review / lab-scale studies TAG recovery & cell disruption are major cost drivers Renegar et al. (2024) [33] Microbial oil TEA scenarios (metaanalysis) Multiple scenarios Scenario analysis Large scenario study; microbial oil economics sensitive to scale NREL SAF State-of-Industry (2024) [34] HEFA, ATJ, FT state-of-industry report Multiple feedstocks Industry report Authoritative TEA guidance for HEFA/ ATJ/FT Cort´ es-Pe˜ na et al. (2024) [35] Microbial oil processing & extraction review Bioenergy crops / engineered oilcane Review / lab & modelling Extraction energy & cost critical for TAG recovery Fig. 1. Block flow diagrams of the four investigated concepts. Each pathway is identified by a sequence of two words: the first word indicates the product of the biomass treatment (gasification/hydrolysis) and the second word specifies the main process for the jet fuel synthesis. V. Kaperneka et al. Fuel 406 (2026) 137118 3 considered, i.e. lignocellulosic biomass from agricultural residues. All routes lead to the same final product (jet-like fuel) and side-products (naphtha, diesel). 2.1. Route SYN-HDT: Jet fuel from microbial oil through syngas The biogenic solid feedstock is converted into high-quality syngas in a dual fluidized bed gasifier. This type of gasifier operates at atmospheric pressure using steam as the gasification agent. The heat required for the endothermic reactions comes from combusting a part of the produced char, with the heat transferred to the gasifier via hot sand from the oxidizer reactor. The resulting syngas serves as the substrate for the gas-phase fermentation under anaerobic conditions, using acetogenic bacteria (M. thermoacetica) to produce acetate. In the second biological step, liquid-phase fermentation is carried out using oleaginous yeast (Y. lipolytica) that metabolizes the acetate into Triglycerides (TAGs) at aerobic conditions. Both fermentation processes are conducted under mild conditions, i.e. 30–60 ◦C and atmospheric or slightly elevated pressure (1–5 bar). After extracting the TAGs from the cells, they are processed into paraffinic jet-like fuel through hydrotreatment and hydrocracking, producing light-ends and diesel as by-products. This concept has already been presented in previous studies [30,37]. The flowsheet for this route is seen in Fig. 2. 2.2. Route SUG-HDT: Jet fuel from microbial oil through glucose/xylose The biogenic feedstock is firstly pretreated with dilute acid and then converted into sugars (mainly glucose and xylose) through enzymatic hydrolysis. During these steps, cellulose and hemicellulose are broken down to their monomers (glucose, xylose, arabinose, galactose, rhamnose and manose), while HMF (Hydroxymethylfurfural) and furfurals are also produced as byproducts. Lignin is also separated and sent to a combustion unit for energy recovery. The sugars are routed to an aerobic fermenter, where oleaginous yeast uses them as a substrate to produce intracellular TAGs. After extracting the TAGs from the cells, they are finally transformed into paraffinic fuels in the same way described at Route SYN-HDT. This complete production process is presented in this study for the first time. The flowsheet for this route is seen in Fig. 3. 2.3. Route SYN-ATJ: Jet fuel from ethanol through syngas In this pathway, syngas is produced through biomass gasification as described in the SYN-HDT route. It is then directed to an anaerobic reactor, where acetogenic bacteria (C. autoethanogenum) use it as a substrate to synthesize ethanol as an extracellular product. After distillation, the ethanol is further converted into drop-in jet fuel through the ATJ process. In this process, ethanol is dehydrated to ethylene, which undergoes oligomerization over heterogeneous nickel catalysts at low temperatures and high pressure (120–230 ◦C and 35 bar) to form olefins in the C4-C20 range. Finally, the olefins are hydrotreated to produce paraffins, resulting in a mixture of paraffinic fuels that are separated by distillation into light ends, jet fuel, and diesel. This concept is inspired by the LanzaTech −LanzaJet processes, which have been successfully implemented at a commercial scale [26,27]. The flowsheet for this route is seen in Fig. 4. 2.4. Route SUG-ATJ: Jet fuel from microbial oil through glucose/xylose This pathway processes the biomass feedstock similarly to Route SUG-HDT, converting it into sugars. These sugars are then introduced into an anaerobic fermenter, where engineered Zymomonas mobilis bacteria convert glucose and xylose into extracellular ethanol. The ethanol is then separated from the broth through distillation and converted into paraffinic fuels through the same process described in Route SYN-ATJ, involving dehydration, oligomerization, and hydrotreatment. The flowsheet for this route is seen in Fig. 5. 3. Model description The process models were developed in the commercial software Fig. 2. SYN-HDT flowsheet. V. Kaperneka et al. Fuel 406 (2026) 137118 4 Aspen Plus TM . The simulation results are validated against the provided literature data and serve as conceptual models. While they provide valuable insights into process performance, uncertainties may arise due to variations in experimental conditions and inherent model assumptions. The simulations were performed at full scale assuming a fuel heat input of 220 MW th on a HHV basis for all the examined cases, using wheat straw as the selected lignocellulosic feedstock. The selected plant size of 220 MW th reflects a typical median scale for European biorefineries, aligning with studies that show the sustainable availability of biomass in the region supports such large-scale deployments [38]. In fact, the enabled biomass conversion technologies offer the feature of feedstock flexibility, especially that of gasification and various lignocellulosic feedstock types can be used, ensuring the sustainable supply and continuous operation of the plant. The selection of straw pellets as Fig. 3. SUG-HDT flowsheet. Fig. 4. SYN-ATJ flowsheet. V. Kaperneka et al. Fuel 406 (2026) 137118 5 the common feedstock for all case studies is done for benchmarking the pathways on equal terms and does not preclude the use of other biogenic residues and waste found in abundance in Europe, such as forest and agricultural residues [39,40]. 3.1. Feedstock properties The primary specifications of the feedstock are presented in Table 2. The higher heating value (HHV) of the feedstock is 16MJ/kg and its flow rate in all BtL plants is 49140 kg/h. (Fig. 6). For routes SUG-HDT and SUG-ATJ, the feedstock was defined as a mixture of conventional solids (lignin, cellulose, xylan, arabinan, galactan, mannan, ash) and liquids (water, extractives). The chemical formula, as well as the physical property data (such as molecular weight, density, heat capacities, enthalpy of formation, acentric factor, critical temperature, pressure and volume) for the biomass components, like cell mass, hemicellulose, enzymes were extracted from the NREL (National Renewable Energy Laboratory) Aspen Plus database for biofuel components [44]. For routes SYN-HDT and SYN-ATJ, the feedstock was defined based on its proximate and ultimate analysis since it was considered a non-conventional solid. The standard cubic equation of state Peng-Robinson was selected as the property method for all models. To compensate for the poor job that equations of state generally do at predicting liquid density, the PengRobinson (PENG-ROB) property method in Aspen Plus TM uses the American Petroleum Institute (API) method for pseudocomponents and the Rackett model for real components to calculate liquid molar volume. The Peng-Robinson model has also been extended to handle polar Fig. 5. SUG-ATJ flowsheet. Table 2 Composition and analysis of the selected feedstock (wheat straw) [41–43]. Composition (% db) Lignin Cellulose Xylan Arabinan Galactan Mannan Extractives Ash 19.0 42.0 21.0 3.0 1.7 0.8 6.5 6.0 Proximate analysis (%) Moisture Fixed Carbon Volatile Matter Ash 9.7 19.7 75.8 6.0 Ultimate analysis (%) Ash Carbon Hydrogen Nitrogen Chlorine Sulfur Oxygen 6.0 46.21 5.96 0.34 −0.006 41.484 Fig. 6. General scheme of heat recovery system from combustibles burning. V. Kaperneka et al. Fuel 406 (2026) 137118 6 components and non-ideal chemical systems. For biological processes like gas and liquid fermentation and anaerobic digestion, the NRTL (Non-Random Two-Liquid) method is used due to the prevalence of oxygenated and polar molecules. This method works well under the mild temperature and pressure conditions typical of these operations. Across all models, the compressors’ isentropic efficiency was set at 85 %, while the pumps’ efficiency at 70 %. The pathways analyzed involve well-established biochemical and thermochemical conversion processes, and no novel hazardous compounds are introduced beyond those typically present in biofuel production. While certain intermediates (e.g., syngas, ethanol, and triglycerides) have associated handling risks, their safety considerations align with existing industrial standards and should be objective for future work that focuses on risk assessment addressing storage, transport, and operational safety concerns [45]. The following paragraphs provide a detailed description of the models’ sub-processes. The reactions and main conditions, as well as the input parameters for these processes are summarized in the Supplementary material. 3.2. Biomass pretreatment and hydrolysis The model of this process comprises two main parts: the dilute acid pretreatment of the lignocellulosic material (and the subsequent detoxification and neutralization procedure of the liquid hydrolysate) and the enzymatic hydrolysis of cellulose. All the processes are modelled as stoichiometric reactors (RStoic) with specific reaction stoichiometry. For modelling purposes, starch, cellulose and hemicellulose (xylan, arabinan, galactan, mannan) polymers are represented by their monomers. As a first step, the lignocellulosic material is mixed with water to obtain an H 2 O/feedstock mass ratio of 2.8, and sulfuric acid is added to the produced slurry to reach a concentration of 2 % w/w. The reactor’s product undergoes solid/liquid separation through a pneumapress pressure filter. The separated hydrolysate liquor is cooled and fed to the overliming tank. Solid Ca(OH) 2 is added to the tank to react with the H 2 SO 4 and raise the pH of the liquid. The effluent stream is then sent to the neutralization reactor, where H 2 SO 4 is added to neutralize the solution by reacting with the excess of Ca(OH) 2 . The design of the pneumapress pressure filter, the overliming tank and the subsequent neutralization tank was based on NREL’s previous studies [46,47]. During the neutralization reaction, gypsum (CaSO 4 ⋅2H 2 O) is formed, which is then removed through hydrocyclone and rotary drum filtration. The solid fraction deriving from the pneumapress pressure filter is mixed with water to reach a H 2 O/solids mass ratio of 13 and sent to the cellulose hydrolysis reactor. After cellulose hydrolysis, the produced slurry is filtered to separate the solids. The solid/liquid separation was modelled as a simple splitter block assuming 100 % recovery of fermentable liquids [48,49]. 3.3. Glucose and xylose fermentation to TAGs The scope of this process is the synthesis of TAGs via fermentation, using as substrate the derived glucose and xylose solution from the biomass pretreatment and hydrolysis process. More specifically, this solution is sent to the aerobic fermenter, where oleaginous yeast consumes the sugars to produce biomass and intracellular TAGs. Triolein (C 57 H 104 O 6 ), tripalmitin (C 51 H 98 O 6 ), trilinolein (C 57 H 98 O 6 ) and tristearin (C 57 H 110 O 6 ) are selected as the representative TAGs produced during the lipid accumulation phase. The reactions that represent the biomass formation phase, and the reactions that represent the lipid production phase, are based on [31,50]. The conversion rates of the reactions are selected in such a way that the subsequent decomposition of TAGs simulates the optimum lipid content [51] and the fatty acid distribution, as reported in the literature [30]. 3.4. Biomass gasification For the implementation of the gasification and the reforming reactions, equilibrium models have been used, while for kinetically and hydrodynamically controlled phenomena that cannot be predicted with the rules of chemical equilibrium (e.g. unconverted solid carbon, formation of gaseous hydrocarbons), fitting of selected parameters with experimental data was followed. The selected parameters and the fitting of the model are based on previous Dual Fluidized Bed Gasifier (DFBG pilot) tests [52,53]. For the DFBG unit, a gasifier operating with 100 % steam and an oxidizer operating with air are considered. Char, as well as gas fermenter’s off-gases and light gases from the hydrotreatment unit, are used as fuel sources for the oxidizer. Filtration of syngas takes place at gasifier outlet temperature, while the filter ashes are also directed to the oxidizer. The syngas cleaning train was modeled to include particulate filtration, tar removal, and sulfur scrubbing, following design assumptions from NREL techno-economic design report [54]. A mixture of sand and calcium carbonate was used to represent the bed material. The governing reactions in the gasifier are the steam gasification reaction, the Water-Gas Shift (WGS) reaction, the Boudouard reaction, the homogeneous gas reactions that form hydrocarbons and the partial combustion reactions. The catalytic reformer operates under autothermal conditions with the addition of air as the oxidation medium, and steam as the reforming agent. 3.5. Gas and liquid fermentation (syngas to TAGs) This part includes the syngas fermentation for the production of acetate and the acetate fermentation for the production of TAGs. The reformed syngas from the biomass gasification section is sent to the anaerobic fermenter where syngas fermentation takes place. A stoichiometric reactor was used to simulate this stage of the process. The reactor operates at 55◦C since the optimal temperature range for Moorella thermoacetica, the acetogenic bacterium considered in this study, is 55 – 60◦C [55], and at slightly elevated pressure to achieve higher solubility of the reacting gases in the liquid phase. For modelling purposes, the acetate, which is the real product of gas fermentation, is represented by acetic acid (C 2 H 4 O 2 ). Additionally, it was considered that the H 2 and CO utilization of the syngas inlet stream by the bacteria is 80 % and 90 %, respectively. The selected values are based on literature data [56]. The remaining unconverted gas is utilized at the oxidizer of the gasification unit and depending on the pathway, it provides the necessary hydrogen for the hydrotreatment processes via pressure swing adsorption (PSA). The dilute acetate solution deriving from the gas fermenter is sent to the liquid fermenter to be converted into biomass and intracellular lipids by oleaginous yeast. Triolein (C 57 H 104 O 6 ), tripalmitin (C 51 H 98 O 6 ), trilinolein (C 57 H 98 O 6 ) and tristearin (C 57 H 110 O 6 ) are selected as the representative TAGs produced during the lipid accumulation phase. The reactions that represent the lipid production phase are based on [30]. After the double-stage fermentation process, the fermentation broth containing the cells undergoes certain purification steps in order to extract the lipids from the yeast cells. Microbial oil is assumed to be recovered through cell disruption (via enzymatic hydrolysis) followed by solvent extraction. However, for simplicity reasons, these steps are omitted from the model. 3.6. Microbial oil hydrotreatment This part of the process refers to the hydrotreatment of the produced TAGs to obtain the targeted jet-like fuel. Initially, the decomposition of the representative TAGs is taken into account to simulate the fatty acid distribution that contains palmitic acid (C 16 H 32 O 2 ), oleic acid (C 18 H 34 O 2 ), stearic acid (C 18 H 36 O 2 ), and linoleic acid (C 18 H 32 O 2 ). Total conversion of the triglycerides into acids and propane (C 3 H 8 ) is assumed at a temperature of 370 ∞C [57] and 140 bar. The H 2 :TAGs mass ratio is set to 0.09 according to [58]. V. Kaperneka et al. Fuel 406 (2026) 137118 7 Then an equilibrium reactor is employed for the simulation of the hydrotreating reactor involving hydrogenation, deoxygenation and reduction reactions. The yield of the product is established based on the equilibrium state of the reactions taking place within the reactor [37]. The temperature selected for this reactor is 370∞C [57]. Following this, hydrocracking and isomerization are employed to break down long-chain paraffins into shorter, branched hydrocarbons, enhancing the fuel’s properties, especially for jet fuel. Hydrocracking is simulated at conditions of 340 ◦C and 140 bar, ensuring the breakdown of heavier hydrocarbons into the desired jet-range products (C8-C16) [58]. The TAGs are converted into approximately 65–73 wt% n-paraffins and 24–33 wt% iso-paraffins [58]. To account for this, an isomerization step is simulated using a stoichiometric reactor. Appropriate catalytic system selection is assumed for maximization of the jet fraction. The reaction conversions were carefully chosen to simulate the paraffinic composition of the three fuel fractions. This selection is based on relevant literature studies on the production of jetlike and diesel-like fuels from hydrotreated oils [58]. The hydrotreated microbial oil is then separated from the gas phase (unreacted hydrogen, light hydrocarbons, produced CO/CO 2 ) and sent to a Flash Separator in order to retrieve the targeted drop-in biofuels. The fractionation part of the process is modelled employing two distillation columns where naphtha, jet and diesel fractions are separated. The light gases produced during the process are recycled and utilized as supplementary fuel in other process stages where additional energy is required. The unreacted hydrogen is recycled back to these stages. 3.7. Syngas fermentation to ethanol This part includes the syngas fermentation for the production of ethanol. The reformed syngas from the biomass gasification section is sent to the anaerobic fermenter where syngas fermentation takes place. A stoichiometric reactor is used to simulate this stage of the process. The reactor operates at 37◦C since it is the optimal temperature for the growth of most ethanol-producing acetogenic bacteria (Clostridium autoethanogenum) [59], and at slightly elevated pressure to achieve higher solubility of the reacting gases in the liquid phase. The reactions that represent microbial growth are based on [30], while the reactions that refer to the production of acetic acid (side product) and ethanol are based on [21]. It is considered that the H 2 and CO utilization of the syngas inlet stream by the bacteria in each pass is 80 % and 90 %, respectively. The selected values are based on literature data [56]. The conversion rates are based on optimum conversions of CO and H 2 found in literature [60]. The remaining unconverted gas is utilized at the oxidizer of the gasification unit. 3.8. Glucose and xylose fermentation to ethanol The scope of this process is the synthesis of ethanol via fermentation, using the derived glucose and xylose from the biomass pretreatment and hydrolysis process as substrates. More specifically, this glucose/xylose solution is sent to the anaerobic fermenter where bacteria (engineered Zymomonas mobilis) convert glucose and xylose into biomass and ethanol [61,62]. The reactions for the cell growth and the ethanol production phase, as well as the conversion rates of these reactions, are based on [62]. 3.9. Alcohol-to-Jet (ATJ) process The ATJ process consists of three main reactive stages: dehydration, oligomerization and hydrogenation, followed by a separation zone [63]. 3.9.1. Ethanol dehydration to ethylene In the first reactive stage of the ATJ process, ethanol is dehydrated at 450 ◦C and 11.4 bar to achieve 99.5 % conversion to ethylene. The reactor setup consists of four tubular adiabatic reactors connected in series, each containing a catalyst bed. The overall ethanol conversion in this stage is assumed to be around 99.5 %. In Aspen Plus TM , the reactions are modeled using a Stoichiometric Reactor (RStoic). Following this stage, the ethylene produced is directed through a turbine, reducing its pressure to 3 bar. This pressure drop is essential for efficient separation of ethylene in a distillation column, which is modeled using the RadFrac unit, designed to recover 99 % of the ethylene [63,64]. After this step, the ethylene is compressed to reach a higher pressure of 35 bar for the oligomerization process. This is achieved through multistage compression, utilizing a pressure ratio of 3.3 across three stages, resulting in intermediate pressures of 3.3 bar and 10.8 bar before reaching the final pressure of 35 bar. Between each compression stage, the gas is cooled down to 38 ◦C using intercoolers, and any condensed water is removed in a knock-out drum, modeled as a flash in Aspen Plus TM [64]. The ethylene oligomerization process requires the introduction of ethylene with a purity ranging from approximately 99 vol% to 99.95 vol% [65]. 3.9.2. Ethylene oligomerization The recovered ethylene is then directed to the second reactive stage, where it undergoes oligomerization into products within the C4-C20 range over heterogeneous nickel catalysts under low temperature and high pressure (LT-HP) conditions of 120 ◦C and 35 bar, achieving an ethylene conversion level of 99 % and a selectivity of 97 % for the desired products. In Aspen Plus, the reactor is modeled using the RStoic unit, while the reactions are based on [63,66]. The olefins generated from ethylene oligomerization are directed into a secondary oligomerization reactor, where they undergo further oligomerization into higherchain-length olefins. This stage employs an Al-SBA-15-supported Ni catalyst along with a LiAlH 4 co-catalyst, and operates at a higher temperature of 230 ◦C compared to the initial oligomerization step, with the reactions based on [67]. The conversion rates for these stoichiometric reactions are estimated based on the anticipated hydrocarbon fractions [23]. 3.9.3. Hydrotreatment The final step for the production of paraffinic fuels is the olefins hydrotreatment, while a total conversion of the alkenes to alkanes is assumed [18]. A hydrogen-to-olefins ratio of 4:1 is employed in this process. The hydrocarbon mixture is routed to one flash separator and two distillation columns for the separation of light gases, naphtha, jet fuel, and diesel. 3.10. Wastewater treatment and biogas synthesis In this study, the treatment of effluent streams is considered. Since the water volumes used in the examined pathways are significant, particularly in hydrolysis or fermentation-based processes, wastewater management cannot be outsourced and must be integrated into the overall plant design. A simplified approach for power requirements is adopted, using the detailed model from [62], as a reference. Specifically, the required air mass flow for the aerobic part of the treatment is set at 55 % of the wastewater flow, and a compressor is used (discharge pressure: 1.7 atm, isentropic efficiency: 0.7). Moreover, it is assumed that 40 % of the water is evaporated and released to the atmosphere from the reverse osmosis unit. Additional details about the process can be found in [62]. The clean water is considered when calculating the fresh water required to meet the overall process water demands. For biogas synthesis via anaerobic digestion (AD), the model follows the approach of [68], aiming to convert all organic compounds into CO 2 and CH 4 , without violating stoichiometric principles. 3.11. Heat integration and utilities A common strategy for all the examined cases is adopted regarding the way that heat and cooling demands at each process are fulfilled. At V. Kaperneka et al. Fuel 406 (2026) 137118 8 first, the characteristics of the steam utilities that are employed to cover the heat demands at the endothermic processes through steam condensation or to recover the excess heat at the exothermic ones are determined the same for all cases (see Supplementary Material for details). Moreover, cooling water and chilled water are used for the unexploited heat and refrigerant medium for low-temperature cooling duties. Finally, a heating oil is employed for very high temperature demands. 3.11.1. Steam generation Apart from the steam generated during the exothermic reaction processes, combustible side streams like lignin and the biogas produced in the AD are burned to generate steam (and heating oil where necessary). The heat duty for the heat exchangers is configured such that steam (and heating oil) generation matches the respective process demands. Additionally, surplus low-pressure (LP) steam is produced from the excess heat and utilized as a by-product to generate additional revenue. 3.11.2. Cooling tower The cooling tower is an important part of the plant that secures the constant inlet temperature of the cooling water but a considerable amount of water is released to the atmosphere and should be taken into account in the water management analysis. The cooling tower is modeled as a Flash unit, where water at a temperature of 30 ◦C mixes with ambient air and is partially evaporated so that it achieves the desired temperature according to utility specifications for cooling water (see Supplementary Material for details). The amount of water is determined to maintain adiabatic conditions. For the cooling tower operation, three pumps are employed: one for feeding cooling water (CW) to the tower (ΔP =0.717 bar), another for draining water (P out =1.5 bar) and mixing it with make-up water, and a third for distributing the final stream back to the plant (P out =5.2 bar). Additionally, an induced draft fan operates at P out =1.0 bar. 4. Economic assessment methodology For the economic analysis, the under examination cases are compared against the biofuels production cost (BPC, expressed in € /MWh) and the minimal selling price of the produced jet fuel fraction. The methodology for the calculation of the equipment cost and the assumptions for Total Capital Investment (TCI) and OPEX estimation follows. The equipment cost of each component is estimated based on an “n th plant” assumption, using corresponding equipment costs from the literature according to the following equation: Ci=Co(Si So)f (1) In this study, the scaling parameter f is 0.6 for all equipment units. The parameters for all equipment cost estimation for all examined cases are summarized in Supplementary material – S.Table 4. The Total Installed Cost (TIC) calculation is based on the installation factor n ist , which is multiplied by the respective equipment cost. This includes the purchased equipment, erection, piping, site improvements, instruments, control systems, and final installation/integration. ICi=nistCi(2) The reference year is 2020, using the average annual CEPCI (Chemical Engineering Plant Cost Index) value (596.2) [69] (see Supplementary material – S.Table 4). This reference year was chosen to ensure consistency across all modeled pathways and comparability with widely cited techno-economic assessments in the literature, many of which also benchmark to 2020 cost indices. While more recent years would reflect current price increases, updating to later indices (e.g. years 2024–2025) would require rescaling of all cost inputs and could introduce inconsistencies with the techno-economic assumptions and external benchmark data sets. Additionally, the period of 2021–2023 was strongly affected by pandemic-related supply-chain disruptions, the Russia–Ukraine war, and resulting energy price volatility, which several macroeconomic analyses have identified as sources of exceptional instability in production and commodity costs [70–73]. In contrast, 2020 provides a more stable and consistent baseline across all pathways and aligns with many widely cited TEA studies and reports that also use this year as a benchmark (e.g., [34,74,75]). Importantly, the purpose of this work is to compare pathways under the same boundary conditions, and thus the relative differences between process routes remain valid irrespective of the chosen reference year. If desired, absolute production costs may be escalated to more recent years using standard chemical engineering cost indices without altering the comparative conclusions of this study significantly. Regarding the novel equipment and units, such as gas and liquid fermenters that are not yet available at commercial scale, the same cost estimation approach is adopted for the gas fermentation units in both ethanol and acetate production using syngas as a substrate (SYN-ATJ and SYN-HDT, respectively). For lipid production in the SYN-HDT and SUG-HDT scenarios, the unit cost estimation is based on a previous study for similar application [30], in which certain figures from the literature and appropriate correlations for the reactors number and volumes have been considered [37]. Similarly, the cost estimation of the lipids recovery part was based on techno-economic studies from processes with same configuration for the extraction and purification of the desired product [76]. The indirect costs that include engineering, contractors, legal fees, etc. are set as 89 % of the Total Purchased Equipment Cost. The contingencies are assumed 10 % of the sum of total direct and indirect costs (TDIC), and the sum of them (contingencies and TDIC) constitutes the fixed capital investment (FCI). The total capital investment (TCI) for each case is the sum of FCI with the working capital, which is defined as 10 % of the FCI [77,78]. The parameters for the operational cost calculation such as O&M and insurance are considered as a portion of the FCI [79]. The assumptions made for the economic evaluation are summarized in Supplementary material. The feedstock cost has a critical contribution to the overall cost assessment (24–28 % of total cost, as seen below in Section 5.4). The price of 70 € /t is taken from [80] as the average price of straw in the last trimester of 2024. As for the hydrogen cost, it also plays a considerable role yet with no great importance on the final fuel price formulation for the cases that external H 2 are required for the hydrotreatment process (4.7 % −11.9 % for the SUG-based cases). Despite the projections for a gradual decrease of its price, it was considered the current value of green H 2 from IRENA’s report for conservative purposes [81]. 5. Results and discussion 5.1. Main process simulation results The heat and mass balances for the entire production processes were analyzed, and the performance of the four pathways was evaluated based on the key factors listed in Table 3. Energetic Fuel Efficiency (EFE) indicates the fraction of the chemical energy in the initial feedstock that is retained in the final fuel products. Carbon Utilization (CU), on the other hand, represents the percentage of the carbon in the original feedstock that is effectively converted into the final fuels. The results reveal distinct differences among the four pathways (SUG-HDT, SYN-HDT, SUG-ATJ, SYN-ATJ) in terms of fuel yield, energy efficiency, and hydrogen demand. SUG-ATJ shows the highest liquid fuel mass yield (0.10) and overall yield (0.16), with the strongest focus on jet fuel production, as 63 % of its products are jet fuel. However, it requires the highest external power consumption (10.07 MW), which makes it energy-intensive despite its impressive yield. This results from V. Kaperneka et al. Fuel 406 (2026) 137118 9 illustrates this trend, where ethanol values are derived from the SUGATJ pathway, and the remaining values come from the SUG-HDT pathway. The upgrading in energy content is significant when moving from acetic acid to microbial oil, highlighting the importance of the liquid fermentation step and the subsequent downstream processing required to produce a product with improved energy content, along with the associated increases in investment and operational costs. Regarding the final upgrading step for hydrocarbon formulation, the SUG-HDT case shows an improvement in energy content (from microbial oil to hydrocarbon) of 16.3 %, with a corresponding increase in production cost of 5.7 %. In contrast, for the SUG-ATJ case, the energy content increases by 61.2 %, but the respective increase in production cost is 7.3 %, emphasizing the high importance of the upgrading section for this pathway. Fig. 17b clearly shows that the contribution of OPEX to the total production cost increases as the heating value of the targeted product rises. In other words, the most capital-intensive components are concentrated in the early stages of biomass processing. A sensitivity analysis on critical parameters is performed in order to illustrate their influence on the economic performance of each pathway. As seen from Fig. 18, the capital cost has a great influence especially on SYN-HDT and secondly on SUG-HDT as the most CAPEX demanding cases. Since their main technologies are still at low technology readiness level, they have more prospects of investment cost reduction. Moreover, the biomass feedstock cost is another important factor, and better MSP can be achieved if low cost, yet challenging feedstock types like biogenic residues and biowastes are employed. Gasification is a proven technology that can handle quite effectively such feedstock, making concepts like SYN-HDT and SYN-ATJ improve their economic performance. The electricity price has smaller impact on cases that have relevant low power consumptions (SUG-HDT). Finally, due to the relatively lower selectivity in jet fuel for the TAG bases cases (SYN-HDT and SUG-HDT), the revenues from selling of the side products (LPG, gasoline and Diesel) play a significant role in the determination of jet fuel breakeven point, highlighting the necessity of improvement in hydrotreatment catalyst towards the maximization of jet fuel fraction yield. Overall, the structured comparison in Table 7 highlights the tradeoffs between the four pathways. The SUG-ATJ route shows the highest carbon efficiency and jet fuel yield, but this advantage comes at the expense of significantly higher external power demand. In contrast, the SYN-HDT pathway benefits from high energy self-sufficiency and relatively low external power needs due to syngas-derived hydrogen, although its overall fuel yield and carbon efficiency remain limited. The SYN-ATJ pathway represents a balanced option, with moderate carbon losses and favorable hydrogen demand compared to the hydroprocessing routes. Finally, the SUG-HDT process performs in the midrange across most categories, but its relatively high water demand may affect its sustainability at larger scale. This structured assessment makes clear that the pathways differ not only in cost but also in their technical and environmental performance, and no single route is superior across all factors. 5.5. Ways for making microbial oil based pathway more competitive In contrast to ethanol, there are only a few studies on using microbial oil as an intermediate carrier for advanced biofuels production, which are mainly based on first-generation feedstocks or scenario analyses (see Table 1), whereas our study performs a direct comparison of microbial oil and ethanol upgrading from lignocellulosic biomass under consistent assumptions. Although the above analysis shows that ethanol-to-jet pathways offer better performance, this paper highlights certain aspects that need optimization, which could make microbial oil-based pathways more competitive and economically viable. Compared to ethanol fermentation, TAG production is aerobic and requires oxygen, which binds hydrogen and/or carbon and reduces overall efficiency. Potential mitigation strategies include developing more oxygen-efficient microbial strains, coupling with renewable hydrogen supply to compensate for the hydrogen deficit, and improving gas–liquid mass transfer to reduce aeration demand. These measures could improve the competitiveness of microbial oil routes relative to ethanol-based pathways. Fig. 19a illustrates how the biofuels production cost can be reduced through certain realistic improvements in the SUG-TAG process. If the wastewater intake at the WWT plant is reduced by 25 % (e.g., by recycling part of the contaminated water in the process), the cost decreases by 6.5 € /MWh. Additionally, further optimization of the biological processes, aimed at achieving a total requirement for fresh process water equal to that of the SUG-ATJ case, is expected to result in a similar reduction in production costs. Finally, the development of more efficient strains in the second fermentation step, which could lead to a 5 % increase in TAGs production compared to the base case, would reduce production costs by approximately 5.8 € /MWh. In total, these improvements could lower the cost below that of the SUG-ATJ case, making the SUG-TAG concept a competitive advanced biofuels production option. Fig. 16. Impact of excess heat utilization on the biofuels production cost. Fig. 17. a) Production cost and heating content of the intermediate and final products, b) cost breakdown of the intermediate and final products. V. Kaperneka et al. Fuel 406 (2026) 137118 16 Regarding the minimum jet fuel selling price, the oil hydrotreatment modeling was based on the only available experimental study on microbial oil hydroprocessing in the literature. However, there is significant potential for further improvements in catalysts to maximize jet fuel selectivity. Fig. 19b shows how the minimum selling price of jet fuel is influenced by the different distributions of hydrocarbon product yields. Catalysts for hydrocracking that can minimize the yield of lighter fractions have a significant beneficial impact on the plant’s sustainability when aviation biofuel is the primary desired product. 5.6. Limitations of the study This study is subject to several limitations. First, many process parameters—particularly for microbial oil fermentation and recovery—are based on laboratoryor pilot-scale data, which may not be directly representative of industrial performance. Scale-up to commercial plants often introduces additional challenges such as contamination risk, reactor hydrodynamics, and process control, which are difficult to capture in conceptual models. Second, long-term continuous operation data are scarce, especially for gas fermentation and aerobic TAG production. The limited number of pilot demonstrations makes it uncertain whether the high yields and productivities reported in batch or fedbatch experiments can be maintained at scale over extended operation periods. Third, some blocks were modeled using standard design assumptions, as discussed in the model description section. While these are consistent with TEA literature, they may not fully capture some aspects such as the variability of sugars / syngas composition from different feedstocks, the potential formation of inhibitory by-products, or the energy penalties associated with advanced cleaning steps. Fourth, capital and operating cost estimates remain uncertain due to scale-up effects and the use of standard cost correlations. These estimates also do not explicitly account for potential reductions through technological learning, supply chain improvements, or policy incentives that could emerge as the sector matures. Finally, broader sustainability aspects such as water footprint, land use impacts, or indirect greenhouse gas Fig. 18. Sensitivity analysis of jet fuel minimum selling price. Table 7 Structured comparison of pathway performance with respect to key technoeconomic and environmental factors. The qualitative scale (‘+’ favorable, ‘0 ′ neutral, ‘–’ unfavorable) is based on relative performance among the four pathways. Factor SYN-HDT SUG-HDT SYN-ATJ SUG-ATJ Energy self-sufficiency +0 0 – Hydrogen demand 0 0 +– Carbon efficiency –0 0 + Jet fuel yield –0+ + Power demand +0 0 – Water demand 0 –0+ Fig. 19. a) Expected decrease in BPC after advancements in SUG-TAG pathway, b) minimum jet fuel selling price for different selectivities in final products. V. Kaperneka et al. Fuel 406 (2026) 137118 17 emissions were outside the scope of this analysis, but they may influence the relative attractiveness of different pathways in practice. These limitations should be considered when interpreting the results, which are intended to provide comparative insights into the relative performance of pathways rather than precise cost forecasts. 6. Conclusions This study compared four advanced pathways for lignocellulosic biomass to SAFs production via ethanol and microbial oil intermediates. Ethanol-based Alcohol-to-Jet (ATJ) pathways, particularly SUG-ATJ, achieved the highest jet fuel yield and carbon utilization, with over 60 % jet fuel selectivity. However, its high external power demand highlights the need for process optimization strategies and integration with renewable energy. Microbial oil pathways (SUG-HDT and SYN-HDT) demonstrate significant long-term potential, with advances in strain engineering and catalyst development likely to improve TAG yields, selectivity, and cost competitiveness. Syngas-based pathways (SYN-HDT and SYN-ATJ) stand out for their energy self-sufficiency, utilizing syngas-derived hydrogen and process heat recovery. However, these pathways face limitations in fuel yield and carbon efficiency due to higher carbon losses and lower jet fuel selectivity. To address the energy and carbon losses, especially in the SYN-HDT pathway, heat integration and waste stream utilization were implemented to enhance process efficiency. Future study should focus on comprehensive life cycle assessment, water footprint evaluation, and carbon capture and utilization (CCU) to address environmental trade-offs. Further innovations in strain/catalyst optimization are recommended to enhance TAG yields and improve jet fuel selectivity, ultimately lowering production costs. By overcoming current technological bottlenecks and implementing these innovations, microbial oil pathways could emerge as viable alternatives for commercial-scale SAF production, contributing significantly to aviation sector decarbonization. This study fills a critical gap in the literature by conducting a detailed techno-economic analysis assessment of microbial oil pathways at an industrial scale. It also provides valuable insights into SAF development, guiding strategic investments and policy frameworks toward more sustainable and economically viable biofuel technologies. CRediT authorship contribution statement Vasiliki Kaperneka: Writing – original draft, Visualization, Software, Formal analysis, Conceptualization. Leda Maragoudaki: Writing – review & editing, Software, Data curation. Konstantinos Atsonios: Writing – review & editing, Supervision, Project administration, Methodology. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was carried out within the framework of the projects BioSFerA (“Biofuels production from Syngas FERmentation for Aviation and maritime use”), which has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 884208 and FUELPHORIA (“Accelerating the sustainable production of advanced biofuels and RFNBOs”), which has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101118286. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.fuel.2025.137118. Data availability Data will be made available on request. References [1] Intergovernmental Panel on Climate. Global Warming of 1.5◦C. 2018. [2] IATA. Fly Net Zero 2024. https://www.iata.org/en/programs/sustainability/flyn etzero/ (accessed October 21, 2024). [3] Olmer N, Comer B, Roy B, Mao X, Rutherford D, Smith T, et al. 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