This study explores key success factors for ethanol production via fermentation of gas streams, by assessing the effects of eight process variables driving the fermentation performance on the production costs and greenhouse gas emissions. Three fermentation feedstocks are assessed: off-gases from the steel industry, lignocellulosic biomass-derived syngas and a mixture of H₂ and CO₂. The analysis is done through a sequence of (i) sensitivity analyses based on stochastic simulations and (ii) multi-objective optimizations. In economic terms, the use of steel off-gas leads to the best performance and the highest robustness to low mass transfer coefficients, low microbial tolerance to ethanol, acetic-acid co-production and to dilution of the gas feed with CO₂, due to the relatively high temperature at which the gas feedstock is available. The ethanol produced from the three feedstocks lead to lower greenhouse gas emissions than fossil-based gasoline and compete with first and second generation ethanol.

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This study describes a methodological framework designed for the systematic processing of experimental syngas fermentation data for its use by metabolic models at pseudo-steady state and at transient state. The developed approach allows the use of not only own experimental data but also from experiments reported in literature which employ a wide range of gas feed compositions (from pure CO to a mixture between H₂ and CO₂), different pH values, two different bacterial strains and bioreactor configurations (stirred tanks and bubble columns). The developed data processing framework includes i) the smoothing of time-dependent concentrations data (using moving averages and statistical methods that reduce the relevance of outliers), ii) the reconciliation of net conversion rates such that mass balances are satisfied from a black-box perspective (using minimizations), and iii) the estimation of dissolved concentrations of the syngas components (CO, H₂ and CO₂) in the fermentation broth (using mass transfer models). Special care has been given such that the framework allows the estimation of missing or unreported net conversion data and metabolite concentrations at the intra or extracellular spaces (considering that there is availability of at least two replicate experiments) through the use of approximative kinetic equations.

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Co-fermentation of mixed sugars to produce butanol is an attractive route in sucrochemical production chains. Herein, high-level mixed sugars from sugarcane bagasse hemicellulosic hydrolysate (HH) and molasses (SCM) were investigated as potential substrates for acetone-butanol-ethanol (ABE) production in batch fermentation by Clostridium saccharoperbutylacetonicum DSM 14923. HH produced after hydrothermal pretreatment was concentrated 5-fold and was called concentrated hemicellulosic hydrolysate (CHH). The fermentation media that were investigated consisted solely of CHH and SCM and three CHH-to-SCM ratios were used to provide 30 g/L initial sugar concentrations and furan derivatives lower than 0.1 g/L. For CHH50/SCM50 and CHH75/SCM25, diluting CHH to concentrations of 15 g/L sugars and 22.5 g/L sugars, respectively, which were supplemented with SCM and nutrients, suffered growth inhibition as a function of the concentration of undissociated acid in the medium. The best-performing medium, CHH25/SCM75 (7.5 g/L and 22.5 g/L sugars from CHH and SCM, respectively and about 0.017 g/L furan derivatives), showed 97 % sugar consumption, and in the pH range of 5.5–6.5, undissociated acetic acid was not an inhibitory molecular form to C. saccharoperbutylacetonicum. After 30 h of fermentation, 7.8 and 9.8 g/L butanol and ABE were produced, respectively, which yielded 0.28 and 0.36 g/g. Based on our findings, C. saccharoperbutylacetonicum exhibits its potential and effective application for renewable butanol production by co-fermenting mixed sugars.

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This work presents a strategy for optimizing the production process of ethanol via integrated gasification and syngas fermentation, a conversion platform of growing interest for its contribution to carbon recycling. The objective functions (minimum ethanol selling price (MESP), energy efficiency, and carbon footprint) were evaluated for the combinations of different input variables in models of biomass gasification, energy production from syngas, fermentation, and ethanol distillation, and a multi-objective genetic algorithm was employed for the optimization of the integrated process. Two types of waste feedstocks were considered, wood residues and sugarcane bagasse, with the former leading to lower MESP and a carbon footprint of 0.93 USD/L and 3 g CO2eq/MJ compared
to 1.00 USD/L and 10 g CO2eq/MJ for sugarcane bagasse. The energy efficiency was found to be 32% in both cases. An uncertainty analysis was conducted to determine critical decision variables, which were found to be the gasification zone temperature, the split fraction of the unreformed syngas sent to the combustion chamber, the dilution rate, and the gas residence time in the bioreactor. Apart from the abovementioned objectives, other aspects such as water footprint, ethanol yield, and energy
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In this work, a model was developed to predict the performance of a bubble column reactor for syngas fermentation and the subsequent recovery of anhydrous ethanol. The model was embedded in an optimization framework which employs surrogate models (artificial neural networks) and multi-objective genetic algorithm to optimize different process conditions and design variables with objectives related to investment, minimum selling price, energy efficiency and bioreactor productivity. The results indicate the optimal trade-offs between these objectives while providing a range of solutions such that, if desired, a single solution can be picked, depending on the priority conferred to different process targets. The Pareto-optimal values of the decision variables were discussed for different case studies with and without the recovery unit. It was shown that enhancing the gas-liquid mass transfer coefficient is a key strategy toward sustainability improvement.

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Exergy and environmental analyses have been developed to determine the performance of the electricity generation in the Dutch mix. A comparative assessment of diverse technological routes, including fossil and renewable energy resources consumption, is carried out in terms of the exergy costs and specific CO₂ emissions. Hence, an exergoeconomy methodology is used to properly allocate the renewable and non-renewable exergy costs and specific CO₂ emissions among the various products of the polygeneration energy systems. By using a suitable methodology, the distribution of irreversibility throughout the different steps of the energy conversion processes of the Dutch electricity mix is characterized in the light of the Second Law of Thermodynamics. The results may help to propose performance indicators that support the Dutch government and research institutions. To identify sustainable energy planning strategies and fairly comparing electricity generation and end-use processing stages with other types of energy resources, such as fuels used in transportation, residential and industrial sectors. In brief, the weighted average of the renewable and non-renewable unit exergy costs and the specific CO₂ emissions of the electricity generated in each route of the Dutch mix is calculated and compared to another electricity mix with a higher share of renewable energy resources. The weighted average renewable and non-renewable unit exergy costs of the electricity generated in the Netherlands are calculated as c_R = 0.8375 kJ/kJ_E/W and c_NR = 1.7180 kJ/kJ_E/W, respectively (c_R/c_NR= 0.49). Furthermore, the specific CO₂ emissions in the Dutch electricity generation achieve 373.21 g_CO2/kWh_E/W.

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This study assesses the sensitivity of the technical, environmental and economic performance of three ethanol production process based on the fermentation of three gas mixtures: i) CO-rich flue gas from steel manufacturing, ii) biomass-based syngas with a H₂/CO ratio of 2 and iii) a 3:1 combination between H₂ and CO₂. The sensitivity analysis is based on stochastic bioreactor simulations constructed by randomly generated combinations of eight parameters that command the fermentation process i.e., temperature, pressure, gas feed dilution with an inert components, ethanol concentration, height of the liquid column, mass transfer coefficients, superficial gas velocity and, acetic acid co-production. The sensitivity analysis identified that the bioreactor technical performance is highly sensitive to variations on pressure, liquid column height and the mass transfer coefficients. The pressure mainly improves mass transfer and consequently ethanol productivity whereas liquid column height improves the gas residence time and consequently the efficiency in the gas utilization. The trend was common for the three gas supply options. The results suggested that in order to produce an optimal bioreactor design, there are options to optimize the productivity and the gas utilization simultaneously. The results from the sensitivity analysis may help guiding a subsequent multi-objective process optimization study.

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Background: Ethanol production through fermentation of gas mixtures containing CO, CO₂ and H₂ has just started operating at commercial scale. However, quantitative schemes for understanding and predicting productivities, yields, mass transfer rates, gas flow profiles and detailed energy requirements have been lacking in literature; such are invaluable tools for process improvements and better systems design. The present study describes the construction of a hybrid model for simulating ethanol production inside a 700 m³ bubble column bioreactor fed with gas of two possible compositions, i.e., pure CO and a 3:1 mixture of H₂ and CO₂. Results: Estimations made using the thermodynamics-based black-box model of microbial reactions on substrate threshold concentrations, biomass yields, as well as CO and H₂ maximum specific uptake rates agreed reasonably well with data and observations reported in literature. According to the bioreactor simulation, there is a strong dependency of process performance on mass transfer rates. When mass transfer coefficients were estimated using a model developed from oxygen transfer to water, ethanol productivity reached 5.1 g L^-1 h^-1; when the H₂/CO₂ mixture is fed to the bioreactor, productivity of CO fermentation was 19% lower. Gas utilization reached 23 and 17% for H₂/CO₂ and CO fermentations, respectively. If mass transfer coefficients were 100% higher than those estimated, ethanol productivity and gas utilization may reach 9.4 g L^-1 h^-1 and 38% when feeding the H₂/CO₂ mixture at the same process conditions. The largest energetic requirements for a complete manufacturing plant were identified for gas compression and ethanol distillation, being higher for CO fermentation due to the production of CO₂. Conclusions: The thermodynamics-based black-box model of microbial reactions may be used to quantitatively assess and consolidate the diversity of reported data on CO, CO₂ and H₂ threshold concentrations, biomass yields, maximum substrate uptake rates, and half-saturation constants for CO and H₂ for syngas fermentations by acetogenic bacteria. The maximization of ethanol productivity in the bioreactor may come with a cost: low gas utilization. Exploiting the model flexibility, multi-objective optimizations of bioreactor performance might reveal how process conditions and configurations could be adjusted to guide further process development.

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Ethanol may be produced from waste materials via a thermochemical-biochemical route employing gasification and syngas fermentation by acetogenic bacteria. This process is considered promising, but commercialization might be hindered by sub-optimal choices of design and operating conditions. In the present work, process systems engineering (PSE) techniques were applied for the optimization of a large-scale syngas fermentation bioreactor. Starting with the development of a dynamic model for a bubble column reactor with gas recycle, the multiple system outputs were studied with Principal Component Analysis to assist in the defmition of relevant objective functions, and artificial neural networks were used to approximate the steady-state responses with fast and accurate functions. This framework was then used to conduct a multi-objective optimization aiming at maximizing the ethanol production rate, lower heating value efficiency and ethanol titer, while also minimizing acetic acid titer and reactor volume.

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Syngas fermentation is one of the bets for the future sustainable biobased economies due to its potential as an intermediate step in the conversion of waste carbon to ethanol fuel and other chemicals. Integrated with gasification and suitable downstream processing, it may constitute an efficient and competitive route for the valorization of various waste materials, especially if systems engineering principles are employed targeting process optimization. In this study, a dynamic multi-response model is presented for syngas fermentation with acetogenic bacteria in a continuous stirred-tank reactor, accounting for gas–liquid mass transfer, substrate (CO, H₂) uptake, biomass growth and death, acetic acid reassimilation, and product selectivity. The unknown parameters were estimated from literature data using the maximum likelihood principle with a multi-response nonlinear modeling framework and metaheuristic optimization, and model adequacy was verified with statistical analysis via generation of confidence intervals as well as parameter significance tests. The model was then used to study the effects of process conditions (gas composition, dilution rate, gas flow rates, and cell recycle) as well as the sensitivity of kinetic parameters, and multiobjective genetic algorithm was used to maximize ethanol productivity and CO conversion. It was observed that these two objectives were clearly conflicting when CO-rich gas was used, but increasing the content of H₂ favored higher productivities while maintaining 100% CO conversion. The maximum productivity predicted with full conversion was 2 g·L⁻¹·hr⁻¹ with a feed gas composition of 54% CO and 46% H₂ and a dilution rate of 0.06 hr⁻¹ with roughly 90% of cell recycle.

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This paper presents the process design and assessment of a sugarcane-based ethanol production system that combines the usage of both mass and heat integration (pinch analysis) strategies to enhance the process efficiency and renewability performance. Three configurations were analyzed: (i) Base case: traditional ethanol production (1G); (ii) mass-integrated (1G2G); and (iii) mass and heat-integrated system (1G2G-HI). The overall assessment of these systems was based on complementary approaches such as mass and mass-heat integration, energy and exergy analysis, exergy-based greenhouse gas (GHG) emissions, and renewability exergy criteria. The performances of the three cases were assessed through five key performance indicators (KIPs) divided into two groups: one is related to process performance, namely, energy efficiency, exergy efficiency, and average unitary exergy cost (AUEC), and the other one is associated to environmental performance i.e., exergy-based CO₂-equation emissions and renewability exergy index. Results showed a higher exergy efficiency of 50% and the lowest AUEC of all the systems (1.61 kJ/kJ) for 1G2G-HI. Furthermore, the destroyed exergy in 1G2G-HI was lower by 7% and 9% in comparison to the 1G and 1G2G cases, respectively. Regarding the exergy-based GHG emissions and renewability performance (λ_index), the 1G2G-HI case presented the lowest impacts in terms of the CO₂-equivalent emissions (94.10 gCO₂-eq/MJ products), while λ_index was found to be environmentally unfavorable (λ = 0.77). However, λ_index became favorable (λ > 1) when the useful exergy of the byproducts was considered.

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The present study describes the procedure followed to construct, to quantitatively validate and the utilization of a mathematical model to simulate ethanol production inside a large-scale bubble column bioreactor fed by syngas of two possible compositions i.e., pure CO and a 3:1 mixture of H2 and CO2. The model is structured by i) a black-box model for catabolic ethanol production and biomass growth and ii) a mass transfer model of the bioreactor; both parts are combined through hyperbolic uptake kinetics for CO and H2. Thermodynamics are used to study the production of energy through catabolism and to estimate biomass yields and maximum specific CO and H2 uptake rates. The simulation identifies a strong dependence of bioreactor performance and mass transfer rate of CO and H2. When mass transfer rate is above the 90 % of the maximum estimated values, ethanol productivity would reach 4.7 g/L/h, gas utilization would be 22 % and, the fermentation process plus the distillation of the product would require 7 MW/kg of ethanol produced. H2/CO2 fermentation achieved 20 and 30 % higher productivity and gas utilization than CO fermentation, respectively. Moreover, gas compression and distillation of ethanol are the largest contributors to energetic costs.

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Increasing efforts in developing sustainable and economically viable technologies to produce transportation fuels have been made in the last decades. Particularly, the aviation industry has conceived that biojet fuels are vital to decrease 50% of the greenhouse gas emissions by 2050 and to achieve carbon-neutral growth by 2020. Thus, the goal of this study is to rank self-sufficient biorefineries for biojet fuel production in Brazil bases on an exergy-based performance analysis aiming to identify the processes irreversibilities. The production capacity assumed for this analysis covers 10% of the projected fuel demand by 2020 in São Paulo (Guarulhos) and Rio de Janeiro (Galeão) airports and considers that the biojet fuel produced is suitable for blending with fossil jet up to 50%. In this context, the base capacity analysed was 210 kton jet/year considering sugarcane (SC) and SC straw as feedstocks, largely available in Brazil. Hence, 24 scenarios were compared for lignocellulosic and lignin valorization processes. These technological pathways covers eight pre-treatment processes such as dilute acid (DA), dilute acid + alkaline treatment (DA-A), steam explosion (SE), steam explosion + alkaline treatment (SE-A), organosolv (O), wet oxidation (WO), liquid hot water (LHW) and liquid hot water + alkaline treatment (LHW-A), followed by enzymatic hydrolysis. Furthermore, three thermochemical processes for the direct conversion of bagasse and lignin upgrade to renewable jet fuel or electricity were considered (Fast pyrolysis, Gasification Fischer-Tropsch and Cogeneration). The exergy assessment evidenced that combined pretreatment processes with the alkaline treatment (DA-A, SE-A, LHW-A) have a better global exergetic performance than the lignocellulosic pre-treatments carried out standalone (DA, SE, O, WO, and LHW). In addition, the use of fast-pyrolysis as a technology for the lignin residues presented the higher performance for all the scenarios. It is shown that the CO2 equivalent index and the renewability exergy index are appropriate metrics to determinate the environmental impact/renewability performance of the technological pathways. Lastly, the environmental analysis shows that all scenarios lead to a 30% reduction of specific CO2 equivalent emissions in exergy base in comparison to the petroleum-based jet fuel impacts.

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Robust strains are essential towards success of n-butanol production from lignocellulosic feedstock. To find a suitable strain to convert a non-detoxified hemicellulosic hydrolysate of sugarcane bagasse, we first assessed the performance of four wild-type butanol-producing Clostridium strains (C. acetobutylicum DSM 6228, C. beijerinckii DSM 6422, C. saccharobutylicum DSM 13864, and C. saccharoperbutylacetonicum DSM 14923)in batch fermentations containing either xylose or glucose at 30 g L⁻¹ as sole carbon sources. C. saccharoperbutylacetonicum was selected after achieving butanol yields as high as 0.31 g g⁻¹ on glucose and 0.25 g g⁻¹ on xylose. In a 48-h fermentation containing a mixture of sugars (93% xylose and 7% glucose)that mimicked the hydrolysate, C. saccharoperbutylacetonicum delivered the highest butanol concentration (14.5 g L⁻¹)when the initial sugar concentration was 50 g L⁻¹. Moreover, the selected strain achieved the highest butanol yield (0.29 g g⁻¹)on xylose-rich media reported so far. Meanwhile, C. saccharoperbutylacetonicum produced 5.8 g butanol L⁻¹ (0.22 g g⁻¹ butanol yield)when fermenting a non-detoxified sugarcane bagasse hemicellulosic hydrolysate enriched with xylose (30 g total sugars L⁻¹). Although sugars were not exhausted (4.7 g residual sugars L⁻¹)even after 72 h because of the presence of lignocellulose-derived microbial inhibitors, these results show that C. saccharoperbutylacetonicum is a robust wild-type strain. This microorganism with high butanol tolerance and yield on xylose can, therefore, serve as the basis for the development of improved biocatalysts for production of butanol from non-detoxified sugarcane bagasse hemicellulosic hydrolysate.

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Production of second generation ethanol can be accomplished with biomass gasification followed by syngas fermentation using acetogenic bacteria in a hybrid process. Using process simulation and financial analysis, this study evaluated the feasibility of producing hydrous ethanol from sugarcane bagasse in a conceptual hybrid plant designed to be energetically self-sufficient. The process was found to be competitive with other second generation routes, achieving an ethanol yield of 330 L per metric ton of dry biomass and an overall carbon conversion of 30%. The minimum ethanol selling price to achieve Net Present Value break-even with 10% Internal Rate of Return was estimated to be 706 US$/m³ after taxes in the base model. When accounting for uncertainties in the fixed capital investment and the cost of raw materials, the Net Present Value was found to be non-negative in 80% of the cases for a selling price of 783 US$/m³.

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