A dynamic model for a fermentation process equipped with an ex situ butanol recovery (termed “ESBR” hereafter) system is proposed for continuous production of biobutanol. Since the proposed ESBR system integrates a fermenter with a stirred-tank-type adsorption column, the dynamic model includes kinetic models for both the fermentation (the Monod/Luedeking-Piret model) and the adsorption (the extended Langmuir model). Parameters in the kinetic models are initially determined using data from batch and fed-batch fermentation experiments with in situ butanol recovery (ISBR). The initially developed model is then used to find a feasible operating condition for an experimental ESBR system, and its parameter values are further tuned using experimental data from the proposed ESBR system for accurate predictions in the butanol and glucose concentration range seen in the ESBR operation. The approach to improving the model accuracy consists of two steps: (1) identifying the critical parameters by performing a sensitivity analysis and (2) re-estimating the selected parameters using data obtained during cyclic operation of the proposed ESBR system. Accordingly, the developed model based on the kinetics for both fermentation and adsorption can describe and predict the behavior of the proposed ESBR system. Thus, the proposed systematic approach provides a reliable platform for the optimal scale-up design and control studies of the ESBR system.

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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.

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