To capture the recent technology improvements in advanced biofuels, this review conducted a systematic review of post-2015 publications and synthesized 116 techno-economic analysis (TEA), life cycle assessment (LCA), and social LCA studies to analyze the economic viability and environmental and social impacts. Capital costs, feedstock prices, operational expenses, and minimum fuel selling prices (MFSPs) were benchmarked alongside carbon emissions to enable direct comparison. This review finds key technical advancements emerged from new catalysts, enzymes, and chemicals, and optimized processes in pretreatment, conversion, and integrated system models. Results indicate that biomethane from anaerobic digestion of agriculture waste is the most cost-effective and climate-friendly option, with capital investments often below $10 million, MFSPs under $2/gal, and WTW emissions consistently below 30 g CO2-eq/MJ. Biodiesel from waste oils can achieve MFSPs below $2/gal when co-products (e.g., electricity, glycerol) are valorized, though emissions vary with feedstock and land use change impacts. For cellulosic ethanol, advances in pretreatment, enzyme efficiency, and lignin valorization have reduced MFSPs to $2.3–$3.2/gal and achieved 70–90% GHG reductions relative to gasoline. Overall, waste-based feedstocks consistently outperform crop-based systems in cost and emission metrics. Process optimization and co-product valorization help with improving resource efficiency, reducing production costs and lowering environmental impacts. The social LCA studies find advanced bioenergy deliver broad societal benefits, with employment as the most consistently reported positive outcome. The review highlights the importance of standardized TEA/LCA/social LCA frameworks, recommending targeted policy incentives to accelerate the deployment of low-carbon bioenergy technologies.

Farm-based anaerobic digestion (AD) has been increasingly adopted on dairy farms in the United States, but not yet on swine operations, largely due to scale limitations. While AD is often acknowledged for lowering greenhouse gas emissions, AD's effects on other agriculturally relevant impact categories (e.g., water quality or biodiversity) are less clear. This study attempts to address both limitations by evaluating the feasibility of scaling swine-based AD systems using herbaceous feedstocks that provide environmental co-benefits, such as annual cereal rye (Secale cereale) and mixed-species perennial prairie; a concept referred to as “grass-to-gas.” Semi-continuous AD systems were used to model process outcomes under varying feedstock mixtures and solids retention times using response surface methodology, with predicted methane yields ranging from approximately 110 – 365 mL g-1 of VS. The results were integrated within techno-economic models to evaluate and optimize the economic viability of renewable natural gas production under eight scenarios, considering retention times, methane recovery from digestate, and scale. Minimum fuel selling prices ranged from 47.5 to 160 $ GJ−1 depending on feedstock mixture and operating conditions, indicating that grass-to-gas systems are not yet economically competitive under current market conditions, largely due to high capital and feedstock costs. These findings highlight the importance of optimizing both technical and economic parameters when designing AD systems, as operational scenarios that provided the greatest methane yields were not necessarily correlated with the greatest economic outcomes. Lastly, to compensate for high feedstock costs, future research into the grass-to-gas concept should seek to quantify the ecosystem services these feedstocks may provide and identify possible measures to monetize that value.

A cobalt-doped RuO2 catalyst enables proton-exchange-membrane (PEM) electrolysers to operate on inexpensive reverse-osmosis water for thousands of hours by blocking chloride and cation impurities. Dual interfacial shielding preserves membrane conductivity, suppresses chlorine evolution and minimizes metal dissolution. This strategy lowers capital and operating costs while maintaining high current densities, advancing practical low-purity-water hydrogen production.

The growing global demand for energy and rising greenhouse gas emissions require effective mitigation strategies, including carbon capture and storage (CCS) technologies. This study reviews 16 widely used simulation tools, including Aspen Plus, MATLAB, Fluent, and gPROMS, for steady-state and dynamic modeling of post-combustion, pre-combustion, and oxy-fuel combustion carbon capture processes. The tools are evaluated using five criteria: chemical process simulation capability, dynamic modeling functionality, thermodynamic property management, heat transfer accuracy, and tool integration features. The results reveal distinct strengths across platforms. Aspen Plus and Aspen Plus Dynamics perform strongly in chemical process simulation and thermodynamic property modeling, reflecting their robustness in reaction modeling and property estimation. gPROMS excels in dynamic modeling, demonstrating strong capability for time-dependent and transient process analysis. MATLAB achieves the highest score in tool integration, highlighting its flexibility in coupling with optimization solvers, control systems, and external programming environments. Fluent shows strong performance in heat transfer modeling, particularly for detailed thermal analysis in oxy-fuel combustion systems. Most existing studies focus on individual carbon capture technologies rather than simulation tool capabilities. Following the PRISMA 2020 guidelines, a systematic search of Scopus yielded 53 peer-reviewed papers on CCS simulation, which were analyzed to identify dominant tools and inform the AHP-based evaluation. This work addresses that gap by clarifying tool-specific advantages, supporting informed model selection to improve the efficiency and sustainability of CCS process design.

Synthetic spider silk biomaterials with exceptional strength and thermal resistance have attracted growing interest for various applications, including the textile and medical industries. Natural spider silk production relies on farming spiders, which poses technical, economic, environmental, and ethical challenges. Synthetic spider silk offers an alternative path to high-quality silk materials. However, there is limited information on the costs and environmental benefits of synthetic spider silk. This study employs techno-economic analysis (TEA) and life cycle assessment (LCA) to evaluate the economic feasibility and environmental impact of large-scale synthetic spider silk manufacturing. Experimental data are based on Escherichia coli (E. coli) to produce recombinant spider silk proteins. A commercial-scale fiber production facility was simulated in BioSTEAM. Environmental impacts were assessed using OpenLCA. Our findings reveal that the production of synthetic spider silk can achieve a minimum sale price of 14.96 USD to 87.8 USD per kilogram, with associated greenhouse gas emissions (GHG) of 17.39 to 104.11 kg CO2e per kilogram. The machine learning analysis indicates that synthetic fiber market values could range between 5 and 25 USD per kilogram. Sensitivity analysis indicates that fiber yield, glycerol, and urea are the most important economic and environmental factors. Synthetic spider silk could become a competitive and environmentally friendly material for various industries by optimizing production processes for greater fiber yield and identifying novel raw materials.

Spent coffee grounds (SCGs) are a globally abundant organic residue with volatile solids exceeding 90% of total solids and a biomethane potential of 250-350 mL CH4/g VS. This study integrates Anaerobic Digestion Model No. 1 (ADM1) kinetics within the BioSTEAM process simulation framework to conduct coupled techno-economic analysis (TEA) and life cycle assessment (LCA) of renewable natural gas (RNG) production from two codigestion scenarios at 5000 kg/h total feed: (i) a Madrid scenario, in which SCGs are codigested with fruit and vegetable waste (FVW) from Mercamadrid, Spain, and (ii) an Iowa scenario, in which SCGs are codigested with mixed prairie biomass in Iowa, USA. Although parameterized for these two locations, the scenarios can also be interpreted more generally as transferable examples of SCG codigestion with food-waste biomass and herbaceous biomass, respectively. Biogas is upgraded to RNG via monoethanolamine scrubbing, and hydrothermal carbonization (HTC) biochar is recovered as a coproduct. The Madrid scenario achieves an RNG rate of 186.3 kg/h, a minimum selling price (MSP) of $41.8/GJ, an internal rate of return of 8.0%, and an NPV of $2.3 M. The Iowa scenario yields 180.6 kg/h RNG at an MSP of $55.9/GJ, an IRR of 3.3%, and an NPV of -$7.6 M; Monte Carlo analysis (n = 2000) identifies D_VS as the dominant RNG-rate uncertainty driver in both scenarios, while SCG price exerts the strongest influence on MSP. LCA under TRACI 2.1 with system expansion yields global warming potentials of -10.96 and -12.31 kg CO2-eq per kg RNG for the Madrid and Iowa scenarios, respectively, with 9 and 8 of 10 impact categories being net negative. Results demonstrate that SCG-based codigestion biorefineries can deliver strong environmental benefits, but economics remain scenario-dependent and do not necessarily align with the most favorable climate outcome.

This study coupled continuous anaerobic digestion (AD) experiments with a techno‑economic model to evaluate renewable natural gas (RNG) production from co‑digesting prairie biomass with beef manure on farm. A Box-Behnken design quantified the effects of solids retention time (SRT), organic load (OL), prairie:manure ratio (P:M), and 50% liquid‑digestate recirculation on methane yield, digestate nutrient value as fertilizer, and residual methane potential of the liquid fraction. Experiment‑based regressions were used to drive process models for continuously stirred AD systems with one‑stage and two‑stage AD configurations, the latter including a covered lagoon as the second stage to recover residual methane from the liquid digestate. Longer SRTs increased methane yield, while recirculation shifted the optimal OL and P:M, while raising the maximum methane yield by 15-35%. Two‑stage AD increased total methane by 12-67% without recirculation and 1-33% with recirculation by capturing residual methane from liquid digestate. Minimum fuel selling price (MFSP) decreased with higher P:M and, depending on recirculation, with lower SRT. Two‑stage AD with 50% recirculation resulted in the lowest MFSP of $36.1/MMBTU. Sensitivity analysis identified biogas yield and capital cost as dominant levers, suggesting benefits from improved feedstock quality (e.g., harvest timing, winter annual co‑feeds) and lower‑cost reactors. Our results quantify process settings that move prairie‑based RNG toward viability and align bioenergy with conservation goals.

The economic and environmental costs of petroleum-derived acrylic acid motivate the search for bio-based alternatives. This study presents an integrated techno-economic analysis (TEA) and cradle-to-gate life cycle assessment (LCA) of an industrial-scale bioprocess for producing 3-hydroxypropionic acid (3-HP), a platform chemical and direct precursor to bio-based acrylic acid and other polymers. A process model was developed in BioSTEAM using parameters and performance targets derived from current biotechnological capabilities and peer-reviewed literature, simulating a facility with a 3-HP production capacity of 2,000 metric tons per day. TEA results indicate a minimum selling price (MSP) of $0.62/kg 3-HP. The LCA yields a global warming potential of 1.71 kg CO2-eq/kg 3-HP below the 4.07-7.65 kg CO2-eq/kg cradle to grave range reported for petroleum-derived acrylic acid, confirming a meaningful greenhouse gas reduction. A 3,000-trial Monte Carlo analysis sampling twenty-five process, economic, and life-cycle parameters from independent ±20% uniform distributions yields a 5-95% MSP range of $0.49-0.85 per kilogram of 3-HP and a 5-95% GWP range of 1.28-2.45 kg CO2-eq per kilogram, with a strong positive correlation between the two outcomes (Pearson r = +0.80) driven primarily by fermentation 3-HP yield and fermenter recovery. Improvements in fermentation performance produce simultaneous cost and environmental gains. These results establish a quantitative benchmark for the economic viability and environmental performance of glycerol-to-3-HP bioprocesses and identify the research priorities most critical to advancing commercial scale-up.

Global plastic waste generation exceeds 460 million tons per year, while recycling rates remain below 10%. Innovative solutions are needed for recycling plastic waste into valuable products. This study employs experimental data and two methods to evaluate economic analysis of fluid catalytic cracking (FCC) of plastic-derived pyrolysis oil: physics-based and machine learning (ML)-based methods to model the effects of reactions in the FCC unit. FCC offers many advantages compared to hydrocracking: e.g., it operates under lower pressure (implying lower equipment costs), eliminates the use of hydrogen, and runs with reduced operating costs. The physics-based model was used to determine reaction stoichiometries, ensuring chemical plausibility, while a Decision Tree regressor was employed to predict product yields based on the cycle number and cycle time, a proxy for catalyst deactivation that captures time-dependent experimental observations. Both models were integrated surrogate models within a full process model in BioSTEAM to conduct a technoeconomic assessment (TEA) and a life-cycle assessment (LCA) with uncertainty analysis. The research also highlights the production of valuable chemicals, specifically light olefins such as ethylene and propylene and aromatic compounds such as benzene, toluene, and xylenes (BTX). The ML-based framework estimated a minimum selling price (MSP) for naphtha of approximately $1.38/kg under input uncertainties, while the physics-based approach estimated an MSP of $1.76/kg. These results are within 22% of each other, which is within the expected range of ± 30% for the preliminary TEA estimates. These findings suggest that ML-based approaches can be an effective substitute for physics-based models when there is limited understanding of the underlying chemical mechanisms. Sensitivity analysis captured the impacts of varying the catalyst cycle number and times. Cycle numbers between 1 and 4 and cycle times of up to 20 min resulted in MSPs ranging between 1 and 2 $/kg. This variation captures the impacts of catalyst deactivation in FCC systems, supporting the need for optimizing the catalyst performance.

Global plastic waste generation exceeds 430 million tonnes per year, yet fewer than 9% are recycled in the United States. Pyrolysis offers a chemical recycling route at scale, but existing techno-economic and life cycle assessments fix product yields to single pure polymers, producing economic and environmental outputs that break down when the feed composition changes. We present a superstructure optimization framework that addresses this by embedding a composition-aware random forest yield predictor, trained on 566 pyrolysis experiments, within a full-scale process simulation. Product distributions update automatically as feed allocation shifts across four reactor chemistries: conventional thermal, catalytic (HZSM-5), thermal oxo-degradation, and nonequilibrium CO2 plasma. The optimal superstructure achieves minimum selling prices of -0.56 to -0.76/kg feed and global warming potentials of -0.276 to -0.322 kg CO2-eq/kg feed across four commodity price scenarios, confirming profitable, carbon-negative operation without tipping fees. Carbon abatement costs of $0.46 to $1.25/kg CO2-eq are competitive with direct air capture. Sensitivity analysis shows that the catalytic-plasma split fraction is the single largest driver of both economic and climate performance, while hydrocracking allocation in the wax upgrading stage is emission-neutral across the full variable range. Mixed plastic waste streams, evaluated as composition-variable feedstocks rather than pure resins, are profitable and carbon-negative across realistic market conditions. These results give a quantitative basis for reactor selection, circular economy investment, and policy design targeting chemical recycling on a large scale.

IntroductionMunicipal solid waste is an abundant feedstock with established collection infrastructure and negative or near-zero acquisition costs. This study quantifies the techno-economic and environmental performance of a gasification and alcohol-to-jet pathway that converts 2,000 tonnes per day of municipal solid waste into sustainable aviation fuel.MethodsProcess modeling was conducted in BioSTEAM v2.44.3. Techno-economic analysis and life cycle assessment were performed, including Monte Carlo uncertainty analysis incorporating syngas H2/CO variability, catalyst lifetime, MSW moisture, plastic fraction, and financial parameters.ResultsThe process yields 9.3 gallons of fuel per tonne of incoming waste, with aluminum, iron, and propanol recovered as co-products. The minimum fuel-selling price (MFSP) ranges from $1.25 to $3.68 per gallon depending on tipping-fee credits and co-product revenues, with the lower bound within the 2019–2024 petroleum jet fuel range of $2.00–$3.50 per gallon. Cradle-to-gate global warming potential is 33.67 g CO2-eq/MJ, representing a 63% reduction relative to conventional jet fuel. Natural gas and electricity account for 54% of total emissions, while avoided landfill methane and metal recovery provide 5.8 g CO2-eq/MJ in credits. Monte Carlo analysis yields a 90% confidence interval of –$0.12 to $2.61/gal for MFSP and 28.4–42.8 g CO2-eq/MJ for GWP.ImplicationsEconomic performance is most sensitive to internal rate of return and aluminum price, while environmental performance is primarily driven by electricity sourcing and plastic composition. Overall, the pathway demonstrates robust climate benefits and competitive cost potential across uncertainty ranges.

There is a critical need for research into clean alternative energy sources to address future energy requirements. Renewable natural gas (RNG) can play a key role in sustainably meeting the projected increasing demand for natural gas. This study presents a techno-economic analysis (TEA) and life cycle assessment (LCA) of the anaerobic co-digestion of slaughterhouse waste (SW), domestic waste (DW), and bovine manure (BM). A kinetic model based on experimental data was developed and integrated into BioSTEAM, providing a robust evaluation of process performance and feasibility. The proposed process achieved higher biogas yields and produced biochar as a valuable co-product, contributing to a reduction in the minimum selling price (MSP) of RNG. The plant is projected to generate approximately 172 GJ center dot year-1RNG, with an estimated MSP of 2.1 $ kg-1 RNG. The global warming potential (GWP) was calculated at-4.73 kgCO2-eq kg-1RNG, highlighting the environmental benefits of co-digestion and the carbon sequestration potential of biochar. Sensitivity analysis identified residence time and the inoculum-to-substrate ratio (ISR) as the main factors influencing MSP and emissions, respectively. This integrated framework, combining experimental kinetic data, process modeling, TEA, and LCA, demonstrates the technical, economic, and environmental feasibility of waste-based RNG production systems, offering insights into circular bioeconomy strategies for sustainable energy and carbon management.

To achieve its "30-60 '' dual climate targets, China has to implement BECCS technology that will account for at least a quarter of all CO2 reductions. The techno-economic trade-offs associated with integrating CCS technology to biomass power plants within the Chinese context have not been fully explored in spite of China's huge combustion-based bioenergy industry. This study's findings demonstrate that CCS integration to a typical biomass CHP plant would result in a 90 % increase in the levelized cost of electricity, and sustainable use of China's biomass resources in the system would result in a CO2 abatement four times the current reduction from installed PV and wind technology. The negative emissions from the BECCS plant amount to 323 kt/yr at a competitive negative emission cost of 42.4$/ton-CO2. BECCS implementation in China requires a minimum negative emissions credit of 41$/ton-CO2 to achieve economic parity with fossil-fueled power, which is four times China's carbon price.

The United States has extensive biomass resources, with a potential for sustainable production exceeding 1 billion tons annually. The capacity for removal and permanent storage of carbon from the atmospheric carbon cycle is recognized as a key resource for stabilizing global temperatures and maintaining livable conditions by midcentury. Technological pathways that avoid high per-unit capitalization costs hold promise despite broader market uncertainty; building a comparison to the typical economies of scale proposed in the sector, this study investigates a technology development pathway using economies of numbers. This study investigates biomass fast pyrolysis for carbon removal and storage in the U.S.A, evaluating six common feedstocks: corn stover, switchgrass, clean pine, tulip poplar, hybrid poplar, and oriented strand board, supplied through a distributed network. Small-scale biorefineries convert biomass into bio-oil, biochar, and gas at 400-700 degrees C, with bio-oil transported to centralized storage. Process modeling, techno-economic analysis (TEA), and life-cycle assessment (LCA) reveal a base case (corn stover) bio-oil production of 5.3 tons/day and biochar production of 2.5 tons/day from 10 dry tons/day of feedstock. The base case capital cost is $1.28 million ($128,000/ton/day feedstock capacity; $241,000/ton/day bio-oil capacity), with a minimum bio-oil selling price of $175/ton at a 10 % IRR. The resulting carbon abatement cost is $83.6/ton CO2 (including biochar sequestration) or $152/ton CO2 (excluding it) for the base case. Feedstock production significantly impacts emissions for corn stover (0.20 kg CO2/kg oil) and switchgrass (0.72 kg CO2/kg oil). Switchgrass also has the highest carbon abatement cost, while woody feedstocks are around $100/Mt CO2 (potentially $70/Mt CO2 with a learning factor). Sensitivity analysis identifies feedstock price and production factors as key drivers of the marginal abatement cost (MAC). Small-scale pyrolysis systems are economically advantageous below 6,000 Mt CO2/year, becoming less competitive with direct air capture (DAC) at larger scales. This technology offers significant potential for reducing greenhouse gas emissions by converting diverse biomass resources into bio-oil for long-term storage.

The increasing demand for sustainable energy systems (SES) has driven significant advancements in the fields of techno-economic analysis (TEA) and life cycle assessment (LCA). This comprehensive review explores the integration of machine learning (ML) techniques into these assessments to address inherent data limitations and uncertainties. TEA and LCA methods are enhanced through ML’s predictive modeling, optimization algorithms, and data analysis capabilities, providing more precise and efficient evaluations of SES. The review’s scope includes recent TEA and LCA of SES to understand gaps in current practices, and ML SES studies that address these practices. Our literature search identified only three papers integrating TEA, LCA, and ML. Many studies investigate combinations of TEA or LCA with ML. However, there are unique challenges and opportunities for considering all three aspects of SES. Thus, we propose near- and long-term opportunities to further integrate ML with TEA and LCA. Key case studies demonstrate the transformative potential of ML in improving economic viability and environmental sustainability, highlighting its role in predicting system performance, optimizing configurations, and reducing costs and impacts. The review identifies critical areas for future research, including improving data quality, advancing ML techniques, interdisciplinary training, real-world applications, and policy considerations. This integration represents a significant advancement in the field, offering new opportunities for innovation and optimization in sustainable energy technology assessments.

Prairie grass remains an underutilized agricultural resource that could provide economic, environmental, and ecological benefits to the bioeconomy. Prairie grass and manure anaerobic digestion is a promising pathway for renewable natural gas (RNG) production, but there is limited information on how co-digestion ratios impact RNG performance. This study integrates the Anaerobic Digestion Model No. 1 (ADM1) into a techno-economic analysis (TEA) and life cycle assessment (LCA) framework to evaluate RNG production via co-digestion of prairie biomass and cattle manure. Simulations across eleven feedstock ratios showed that co-digestion can increase methane yields compared to mono-digestion of prairie biomass. The highest methane production rate (227 mL/gVS) and the lowest minimum fuel selling price (MFSP) of $41.88/GJ occurred at a 1:9 prairie-to-manure volatile solids (VS) ratio. RNG yields reached 10.1 GJ/dry tonne for this configuration—39% higher than prairie-only digestion. LCA results revealed that manure-based scenarios had the lowest global warming potential (−16.0 kg CO2-eq/GJ), while prairie-based scenarios reduced ecotoxicity (−190 kg 2,4-D-eq/GJ). Economic and environmental benefits were further improved by accounting for biochar coproducts via system expansion and allocation. Results underscore the value of ADM1 in optimizing AD system design for both profitability and sustainability.

There is a need for research into clean alternative energy sources to meet future energy requirements. Renewable natural gas (RNG) can assist in fulfilling a growing projected demand for natural gas in a sustainable manner. This study focuses on a techno-economic analysis (TEA) and a life cycle assessment (LCA) for the co-digestion of manure with pretreated prairie biomass. This process offers higher biogas yields and an additional co-product, hydroxycinnamic acid (HCA), to decrease the minimum fuel selling price (MFSP) of RNG. The plant produces between 524 000 GJ year-1 and 1 176 000 GJ year-1 of RNG in the five modeled scenarios. The MFSP of RNG for scenarios where biomass pretreatment occurs is estimated to be between $15.87 GJ-1 and $18.94 GJ-1. The MFSP for the scenario where no pretreatment occurs is estimated to be $21.21 GJ-1. The global warming potential results range from -7.30 kg CO2e GJ-1 to 21.59 kg CO2e GJ-1. Sensitivity and uncertainty analyses are also completed.