Alternative fermentation feedstocks such as ethanol can be produced from CO₂ via electrocatalytic processes that coproduce O₂. In this study, industrial-scale fermentation of ethanol with pure O₂ for single cell protein (SCP) production was studied using a modeling approach. This approach considered (i) microbial kinetics, (ii) gas–liquid transfer, and (iii) an exploration of potential operational constraints. The technical feasibility for producing up to 58 kt/y of SCP in a 600 m³ bubble column operating in continuous mode was assessed and attributed mainly to a high O₂ transfer rate of 1.1 mol/(kg h) through the use of pure O₂. However, most of the pure O₂ fed to the fermenter remains unconsumed due to the large gas flows needed to maximize mass transfer. In addition, biomass production may be hampered by high dissolved CO₂ concentrations and by large heat production. The model estimates a microbial biomass concentration of 114 g/kg, with a yield on ethanol of 0.61 g_x/g_ethanol (> 95% (Formula presented.)). Although the large predicted O₂ transfer capacity seems technically feasible, it needs further experimental validation. The model structure allows the analysis of alternative substrates in the same way as identifying the best carbon feedstock.

BACKGROUND: Carboxylates such as volatile fatty acids (VFA) can be produced by acidogenic fermentation (AF) of dairy wastes including cheese whey, a massive residue produced at 160.67 million m³ of which 42% are not valorized and impact the environment. In mixed-culture fermentations, selection pressures can favor AF and halt methanogenesis. In this study, inoculum pre-treatment was evaluated as a selective pressure for AF demineralized cheese whey in batches. Alkaline (NaOH, pH 8.0, 6 h) and thermal (90 °C for 5 min, ice-bath until 23 °C) pre-treatments were tested with batch operations runs at initial pH 7.0 and 9.0, food-to-microorganism (F/M) ratios of 0.5 to 4.0 g COD g⁻¹ VS, and under pressurized (P) and nonpressurized (NP) headspace, in experiments duplicated in two different research institutes. RESULTS: Acetic acid was highly produced on both Unicamp and TU Delft samples (1.36 and 1.40 g COD_AcOH L⁻¹, respectively), at the expense of methanogenesis by combining a thermal pre-treatment of inoculum with a NP batch operation started at pH 9.0. Microbial communities comprising VFA and alcohol producers, such as Clostridium, Fonticella and Intestinimonas, and fermenters such as Longilinea and Leptolinea. The lipid-accumulating Candidatus microthrix was observed in both bulk material and foam. Despite the absence of methane production, Methanosaeta were detected within the microbial community. An F/M ratio of 0.5 g COD g⁻¹ VS led to the best VFA production of 1769.4 mg L⁻¹. CONCLUSION: Overall, inoculum thermal pre-treatment, initial pH 9.0 and NP headspace acted as a selective pressure for halting methanogenesis and producing VFAs, valorizing cheese whey via batch acidogenic fermentation.

In large-scale syngas fermentation, strong gradients in dissolved gas (CO, H₂) concentrations are very likely to occur due to locally varying mass transfer and convection rates. Using Euler-Lagrangian CFD simulations, we analyzed these gradients in an industrial-scale external-loop gas-lift reactor (EL-GLR) for a wide range of biomass concentrations, considering CO inhibition for both CO and H₂ uptake. Lifeline analyses showed that micro-organisms are likely to experience frequent (5 to 30 s) oscillations in dissolved gas concentrations with one order of magnitude. From the lifeline analyses, we developed a conceptual scale-down simulator (stirred-tank reactor with varying stirrer speed) to replicate industrial-scale environmental fluctuations at bench scale. The configuration of the scale-down simulator can be adjusted to match a broad range of environmental fluctuations. Our results suggest a preference for industrial operation at high biomass concentrations, as this would strongly reduce inhibitory effects, provide operational flexibility and enhance the product yield. The peaks in dissolved gas concentration were hypothesized to increase the syngas-to-ethanol yield due to the fast uptake mechanisms in C. autoethanogenum. The proposed scale-down simulator can be used to validate such results and to obtain data for parametrizing lumped kinetic metabolic models that describe such short-term responses.

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.

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.

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.

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.

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.

This study presents the design and assessment of site-specific supply chains and related manufacturing processes for the production of bio-based chemicals from the syngas platform and via gasification of lignocellulosic biomass followed by syngas fermentation. The supply chains include feedstocks production and collection, biomass gasification, syngas fermentation, and downstream processing. For each of these stages, different alternatives were considered: four feedstocks (pine, corn stover, sugarcane bagasse, and eucalyptus), three products (ethanol, 2,3-butanediol and hexanoic acid), and three geographical locations (the Netherlands, the USA, and Brazil). Conceptual development and analysis of the supply chains were done through the combination of different design and assessment tools, namely biomass supply chains design, fermentation process design (based on thermodynamics and transport), process simulation, and economic and environmental assessments. The minimum selling price (MSP) and two environmental impact categories, i.e., global warming potential (GWP) and non-renewable energy use (NREU), were used as performance indicators. These were compared to data reported in scientific literature and commercial sources for similar processes and ­products. The best overall performance was obtained for the production of 2,3-butanediol from pine sourced in the USA. In the cases of ethanol and hexanoic acid, the syngas fermentation stage had significant contributions to MSP, GWP, and NREU, due mainly to its high energy requirements. Regarding the geographical location, the best economic performance was obtained for the USA followed by the Netherlands and Brazil respectively. Furthermore, operation in Brazil led to the lowest environmental impacts, followed by the Netherlands and the USA.