Multi-objective optimization of operation of lignocellulosic acetone-butanol-ethanol fermentation with ex situ butanol recovery (ESBR). (2nd September 2020)

• Record Type: Journal Article
• Authors: Byun, Ha-Eun; Kim, Boeun; Lim, Jongkoo; Lee, Jay H.
• Is Part Of: Computers & chemical engineering. Volume 140(2020)
• Publication Date: 2020-09-02
• Subjects: Acetone-butanol-ethanol fermentation -- Lignocellulosic biomass -- Multi-objective optimization -- Recovery by adsorption -- Process intensification -- Cyclic steady state -- Biobutanol; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.106915
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Multi-objective optimization of lignocellulosic ABE fermentation with ex situ recovery by adsorption is performed. Effect of feedstock type on system performance is investigated. Open-loop sensitivity analysis is conducted to identify influential parameters. Abstract: This study proposes a multi-objective operation strategy for a continuous acetone-butanol-ethanol (ABE) fermentation process with ex situ recovery by adsorption (termed "lignocellulosic ESBR-by-adsorption system"), which is used to produce biobutanol from lignocellulosic biomass. In this study, a model-based cyclic steady state (CSS) optimization of the lignocellulosic ESBR-by-adsorption system is performed to find the best operating conditions and gain better understandings of its dynamic behavior. To gain insights into the relationships between operating conditions and performance of the system, key operating variables are optimized with respect to three different objective functions: maximizing productivity, maximizing yield, and minimizing substrate loss. In addition, an optimization study is conducted for two different types of feedstocks to investigate the effect of feedstock type on system performance. Furthermore, an open-loop sensitivity analysis is performed to identify the most influential parameters and operating conditions that provide more stable system behavior when uncertainties are present.