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.

The use of renewable jet fuels (RJFs) is an option for meeting the greenhouse gases (GHG) reduction targets of the aviation sector. Therefore, most of the studies have focused on climate change indicators, but other environmental impacts have been disregarded. In this paper, an attributional life cycle assessment is performed for ten RJF pathways in Brazil, considering the environmental trade-offs between climate change and seven other categories, i.e., fossil depletion, terrestrial acidification, eutrophication, human and environmental toxicity, and air quality-related categories, such as particulate matter and photochemical oxidant formation. The scope includes sugarcane and soybean for first-generation (1G) pathways and residual materials (wood and sugarcane residues, beef tallow, and used cooking oil-UCO) for second-generation (2G) pathways. Three certified technologies to produce RJF are considered: hydroprocessed esters and fatty acids (HEFA), alcohol-to-jet (ATJ), and Fischer-Tropsch (FT). Assuming the residual feedstocks as wastes or by-products, the 2G pathways are evaluated by two different approaches, in which the biomass sourcing processes are either accounted for or not. Results show that 1G pathways lead to significant GHG reductions compared to fossil kerosene from 55% (soybean/HEFA) to 65% (sugarcane/ATJ). However, the sugarcane-based pathway generated three-fold higher values than fossil kerosene for terrestrial acidification and air quality impacts, and seven-fold for eutrophication. In turn, soybean/HEFA caused five-fold higher levels of human toxicity. For 2G pathways, when the residual feedstock is assumed to be waste, the potential GHG emission reduction is over 74% with no relevant trade-offs. On the other hand, if the residual feedstocks are assumed as valuable by-products, tallow/HEFA becomes the worst option and pathways from sugarcane residues, even providing a GHG reduction of 67% to 94%, are related to higher impacts than soybean/HEFA for terrestrial acidification and air quality. FT pathways represent the lowest impacts for all categories within both approaches, followed by UCO/HEFA.

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.

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.