Model-based optimization of continuous fermentation process with ex situ butanol recovery (ESBR) for biobutanol production from glucose/xylose mixtures

Abstract

This study proposes a mathematical model for the continuous fermentation process with ex-situ butanol recovery (ESBR) for biobutanol production using glucose and xylose, and performs model-based optimization and sensitivity analysis. The ESBR system is an integrated system of a fermenter and adsorption columns, alleviating the inhibitory effect on cell growth and production due to butanol toxicity through butanol recovery by adsorption. The system is difficult to operate and control intuitively owing to periodic switching of adsorption columns and complicated dynamic characteristics of the fermentation process. Thus, it is necessary to improve the operation stability and economic efficiency of the system through model-based optimization. Although the modeling and optimization of the bioethanol production process have been studied so far, modeling and optimization of the biobutanol production process have been rarely studied, and in particular, the modeling study on the process for producing biobutanol by simultaneous utilization of glucose and xylose has not yet been conducted. Therefore, this study proposes a concentration-dependent weighting factor to describe simultaneous utilization of glucose and xylose and develops a dynamic model of continuous biobutanol fermentation process with ESBR. A systematic approach has also been introduced for reliable model parameter identification. Based on the constructed model, multi-objective optimization is performed to improve the butanol productivity and to reduce the sugar loss. The ESBR system converges to cyclic steady state (CSS) due to periodical switching of adsorption columns, thus a simultaneous approach is used for optimization at the CSS. Finally, the roles and influences of each parameter are analyzed through sensitivity analysis under various optimal operating conditions to provide insights into optimal operation strategy by improving understanding of overall process dynamics.