Nagoya University · Engineering
Professor Yoshiaki Kawajiri's research lab specializes in the advanced simulation, optimization, and control of simulated moving bed (SMB) chromatography processes, with a focus on industrial separations in pharmaceuticals, petrochemicals, and biotechnology. The lab develops systematic, dynamic optimization frameworks that integrate transient experimental data and parameter estimation to achieve high-purity, high-productivity separations with minimal manual tuning. Key research directions include full and single discretization methods for solving complex PDAE-constrained optimization problems, and the design of innovative operating schemes such as time-variant flow rate configurations. The lab emphasizes the application of state-of-the-art numerical solvers like IPOPT to enable efficient and reliable process optimization.
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Abstract Simulated moving bed (SMB) processes have been applied to many important separations in sugar, petrochemical, and pharmaceutical industries. However, systematic optimization of SMB is still a challenging problem. Two tailored approaches are proposed, full discretization and single discretization, where both the optimal operating condition and concentration profiles are obtained by a Newton‐type solver. In a case study of fructose and glucose separation, it has been found that the full‐d
Over the past decade, many operating modifications have been proposed for simulated moving bed (SMB) processes, including three-zone, VARICOL, and PowerFeed. Nevertheless, it remains a challenge to find the most efficient operating scheme for a particular application. In this study, we generalize the standard SMB operation by considering a superstructure optimization problem with time-variant flow rates. The optimization problem, constrained by a partial differential algebraic equations (PDAEs)
A systematic algorithm for simulated moving bed (SMB) chromatography process development that utilizes dynamic optimization, transient experimental data, and parameter estimation to arrive at optimal operating conditions is described. These operating conditions ensure both high purity constraints and optimal productivity are satisfied. This algorithm proceeds until the SMB process is optimized without manual tuning. In a case study, it has been shown with a linear isotherm system that the optima
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