Abstract
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.
Highlights
Biofuels are one of the possible means to reduce CO2 emissions in transportation, a sector responsible for roughly 25% of global greenhouse gas (GHG) emissions in 2016 (IEA, 2019)
There is a clear trade-off between the two objectives, as shown with the Pareto fronts depicted in Fig. 2a for both case studies, meaning that, as expected, higher productivities can be achieved at the cost of higher investments
Different process systems engineering (PSE) tools were applied in this work to develop an optimization framework through which ranges of operating conditions and design variables can be selected for optimal production of ethanol fuel from syngas via fermentation, taking into account objectives of distinct natures
Summary
Biofuels are one of the possible means to reduce CO2 emissions in transportation, a sector responsible for roughly 25% of global greenhouse gas (GHG) emissions in 2016 (IEA, 2019). The rapid expansion of 1st generation biofuels (i.e. those produced from food crops) has been associated with impacts that were initially neglected or unforeseen, such as deforestation, indirect land use change, and significant GHG emissions during agricultural stages (Goldemberg et al, 2008). Lignocellulosic or 2nd generation biofuels have the potential to minimize these impacts by using waste carbon materials such as agricultural residues, forestry waste and marginal land (energy) crops. An alternative path to biofuels and the object of this work is syngas fermentation, the microbial conversion of CO/H2/CO2 to ethanol and potentially other chemicals using facultative autotrophic bacteria called acetogens. Since syngas can be produced via gasification of multiple types of feedstocks – including even municipal solid waste –, this process is considered a promising and flexible alternative to biochemical routes that rely on the liberation of sugars from lignocellulosic biomass
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