Dynamic modeling of syngas fermentation in a continuous stirred-tank reactor: Multi-response parameter estimation and process optimization

A novel experimental method to determine substrate uptake kinetics of gaseous substrates applied to the carbon monoxide-fermenting Clostridium autoethanogenum

Syngas fermentation has gained momentum over the last decades. The cost-efficient design of industrial-scale bioprocesses is highly dependent on quantitative microbial growth data. Kinetic and stoichiometric models for syngas-converting microbes exist, but accurate experimental validation of the derived parameters is lacking. Here, we describe a novel experimental approach for measuring substrate uptake kinetics of gas-fermenting microbes using the model microorganism Clostridium autoethanogenum. One-hour disturbances of a steady-state chemostat bioreactor with increased CO partial pressures (up to 1.2 bar) allowed for measurement of biomass-specific CO uptake- and CO2 production rates (q CO ${q}_{{CO}}$ ,q CO 2 ${q}_{{{CO}}_{2}}$ ) using off-gas analysis. At a pCO of 1.2 bar, aq CO ${q}_{{CO}}$ of -119 ± 1 mmol g-1 X h-1 was measured. This value is 1.8-3.5-fold higher than previously reported experimental and kinetic modeling results for syngas fermenters. Analysis of the catabolic flux distribution reveals a metabolic shift towards ethanol production at the expense of acetate at pCO ≥ $\ge $  0.6 atm, likely to be mediated by acetate availability and cellular redox state. We characterized this metabolic shift as acetogenic overflow metabolism. These results provide key mechanistic understanding of the factors steering the product spectrum of CO fermentation in C. autoethanogenum and emphasize the importance of dedicated experimental validation of kinetic parameters.

Analytical tools for unravelling the metabolism of gas-fermenting Clostridia

Acetogens harness the Wood-Ljungdahl Pathway, a unique metabolic pathway for C1 capture close to the thermodynamic limit. Gas fermentation using acetogens is already used for CO-to-ethanol conversion at industrial-scale and has the potential to valorise a range of C1 and waste substrates to short-chain and medium-chain carboxylic acids and alcohols. Advances in analytical quantification and metabolic modelling have helped guide industrial gas fermentation designs. Further advances in the measurements of difficult to measure metabolites are required to improve kinetic modelling and understand the regulation of acetogen metabolism. This will help guide future synthetic biology designs needed to realise the full potential of gas fermentation in stimulating a circular bioeconomy.

Reactor Designs and Configurations for Biological and Bioelectrochemical C1 Gas Conversion: A Review

Microbial C1 gas conversion technologies have developed into a potentially promising technology for converting waste gases (CO₂, CO) into chemicals, fuels, and other materials. However, the mass transfer constraint of these poorly soluble substrates to microorganisms is an important challenge to maximize the efficiencies of the processes. These technologies have attracted significant scientific interest in recent years, and many reactor designs have been explored. Syngas fermentation and hydrogenotrophic methanation use molecular hydrogen as an electron donor. Furthermore, the sequestration of CO₂ and the generation of valuable chemicals through the application of a biocathode in bioelectrochemical cells have been evaluated for their great potential to contribute to sustainability. Through a process termed microbial chain elongation, the product portfolio from C1 gas conversion may be expanded further by carefully driving microorganisms to perform acetogenesis, solventogenesis, and reverse β-oxidation. The purpose of this review is to provide an overview of the various kinds of bioreactors that are employed in these microbial C1 conversion processes.

Multi-Objective Sustainability Optimization of Biomass Residues to Ethanol via Gasification and Syngas Fermentation: Trade-Offs between Profitability, Energy Efficiency, and Carbon Emissions

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 self-sufficiency were also discussed.

Bioethanol Production via Herbaceous and Agricultural Biomass Gasification Integrated with Syngas Fermentation

In this paper, a simulation model based on the non-stoichiometric equilibrium method via ASPEN Plus was established to analyze the gasification performance of 20 herbaceous and agricultural biomasses (H&ABs) linked with syngas fermentation and product purification units for ethanol production. The established simulation model does not consider the gasification system as a black box; it focuses the important processes in gasification such as drying, pyrolysis, gasification, and connection with bioethanol production plants. The results for the 20 H&AB options suggest that the specific mass flow rate of bioethanol from 1 kg of biomass input to the unit is in the range of 99–250 g/kg, and between them, the system fed by hazelnut shell biomass remarkably outranked other alternatives by 241 g/kg production due to the high beneficial results gained from the performance analysis. Additionally, a sensitivity analysis was performed by changing operating conditions such as gasification temperature and air-to-fuel ratio. The modeling results are given and discussed. The established model could be a useful approach to evaluate the impacts of a huge numbers of biomasses and operating parameters on bioethanol output.

Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train

Bioprocess modeling has become a useful tool for prediction of the process future with the aim to deduce operating decisions (e.g. transfer or feeds). Due to variabilities, which often occur between and within batches, updating (re-estimation) of model parameters is required at certain time intervals (dynamic parameter estimation) to obtain reliable predictions. This can be challenging in the presence of low sampling frequencies (e.g. every 24 h), different consecutive scales and large measurement errors, as in the case of cell culture seed trains. This contribution presents an iterative learning workflow which generates and incorporates knowledge concerning cell growth during the process by using a moving horizon estimation (MHE) approach for updating of model parameters. This estimation technique is compared to a classical weighted least squares estimation (WLSE) approach in the context of model updating over three consecutive cultivation scales (40–2160 L) of an industrial cell culture seed train. Both techniques were investigated regarding robustness concerning the aforementioned challenges and the required amount of experimental data (estimation horizon). It is shown how the proposed MHE can deal with the aforementioned difficulties by the integration of prior knowledge, even if only data at two sampling points are available, outperforming the classical WLSE approach. This workflow allows to adequately integrate current process behavior into the model and can therefore be a suitable component of a digital twin.

Modeling ethanol production through gas fermentation: a biothermodynamics and mass transfer-based hybrid model for microbial growth in a large-scale bubble column bioreactor

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.