Optimal fed-batch bioreactor operating strategies for the microbial production of lignocellulosic bioethanol and exploration of their economic implications: A step forward towards sustainability and commercialization

The enduring adoption of greener technologies from the biofuel manufacturing industries requires the balanced integration of multiple inter-disciplinary concerns (i.e., engineering, business, sustainability, societal) to make production processes more attractive to the industrial sector. From an engineering perspective, the commercial establishment of lignocellulosic bioethanol plants is still hindered by increased production cost. High product concentrations with improved yield and productivity at the fermentation stage seem to be inviolable condition to straightly compete with fossil-based and first generation bioethanol facilities. This study proposes a model-based multi-objective dynamic optimization approach to systematically derive optimal operating policies for the fermentation bioreactor as a step forward towards enhancing the economic performance of lignocellulosic bioethanol plants. The economic benefit of the derived optimal bioreactor strategies is evaluated via a scalable economic index, linking for the first time the performance of the fermentation bioreactor to the economics of the entire production process. It is demonstrated that high values in productivity and substantial compromises in yield and final bioethanol concentration are conditions which favor economic feasibility with the achieved fermentation performance (3.32 g L-1 h(-1), 0.399 g g(-1), 106.0 g L-1) being comparable to first generation bioethanol production. Finally, a global system analysis framework is employed to investigate the robustness of the optimal value of the economic index to the variability of key model parameters, illustrating the impact of model uncertainty on the process economic performance. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The adoption of greener technologies in biofuel manufacturing industries requires integration of multiple interdisciplinary concerns to make production processes more attractive. This study proposes a model-based multi-objective dynamic optimization approach to enhance the economic performance of lignocellulosic bioethanol plants.