Model-Based Optimization of Cyclic Operation of Acetone-Butanol-Ethanol (ABE) Fermentation Process with ex Situ Butanol Recovery (ESBR) for Continuous Biobutanol Production

Abstract

This paper proposes a model-based optimization strategy for a fermentation process coupled with an ex situ butanol recovery-by-adsorption (termed "ESBR-by-adsorption" hereafter) process used for continuous biobutanol production. The ESBR-by-adsorption system exhibits cyclic dynamic behavior caused by the periodic switching of the adsorption column for its renewal. Since performance of such a system is largely determined by its dynamic behavior seen after converging to Cyclic Steady State (CSS), the optimization strategy should search for the optimal operating condition leading to the most profitable CSS. For the CSS optimization, we select key optimization variables and define the objective function and constraints. The resulting CSS optimization problem is strongly nonconvex, largely due to the various nonlinearities in the objective function and constraints, e.g., those in the kinetics of the ABE fermentation and adsorption. To alleviate the numerical convergence problem associated with nonconvex optimization problems, we adopt an initialization strategy of identifying a feasible solution region and a "good" initial guess through a coarse grid search. With the initialization strategy, two CSS optimization approaches, "sequential" and "simultaneous," are examined for the system. With the model and simulation, performances of the two approaches are compared with respect to varying qualities of the initial guess to propose an effective practical CSS optimization strategy for the ESBR-by-adsorption system. The optimized continuous production by the ESBR-byadsorption system showed significantly improved volumetric productivity of butanol, 5.5-and 3.7-fold increases respectively over the batch fermentation or semibatch fermentation with in situ product recovery.

Dynamic Modeling of Acetone–Butanol–Ethanol Fermentation with ex Situ Butanol Recovery using Glucose/Xylose Mixtures

“…Kim et al 12 performed the CSS optimization for a pilot-scale ESBR-by-adsorption system using glucose as substrate. In this study, we conduct the CSS optimizations for the two ESBR-byadsorption systems with the same objective function and decision variables as in Kim et al System design specifications are also assumed to be the same as in Kim et al: the nominal liquid volume of the fermenter, that of the adsorption column, and the amount of adsorbents are set as 200 L, 20 L, and 10 kg, respectively.…”

“…The sugar loss over a cycle (the second term in eq 21) is calculated as the amount of remaining glucose and xylose in the broth discharged from the adsorption column at t f . The weighting factors, w 1 and w 2 , are set as 0.74 and 0.26, respectively, as in Kim et al, 12…”

Equilibrium Theory Analysis of Vacuum Swing Adsorption for Separation of Ethanol from CO2 in a Beverage Dealcoholization Process

“…Accordingly, adsorptive dealcoholisation processes have been designed based on the hydrophobic zeolites 15,16 . In 1983, Farhadpour et al proposed an adsorptive process to extract alcohol from beer using silicalite 17 .…”

“…13,14 Accordingly, adsorptive dealcoholization processes have been designed based on the hydrophobic zeolites. 15,16 In 1983, Farhadpour et al proposed an adsorptive process to extract alcohol from beer using silicalite. 17 It follows that Anglerot invented an adsorptive process that removes ethanol from beer liquid.…”

A Hybrid Model of Multi-Objective Differential Evolution Algorithm and Various Decision-making Methods to Optimize the Batch ABE Fermentation Process

“…Elmeligya et al [7] proposed an arti cial neural network as a metamodel for optimizing the biobutanol production process via the integrated ABE fermentation-membrane pervaporation process. Kim et al [8,9] suggested a model-based optimization strategy for the ABE fermentation process coupled with an ex-situ recovery system with periodically switched adsorption column for continuous biobutanol production. The Sequential Quadratic Programming (SQP) algorithm was used to search the optimal operating conditions, leading to the most pro table cyclic steady state.…”