名古屋大学 · 工学
Kawajiri教授の研究室では、クロマトグラフィー分離プロセスの最適化に注力しており、特にシミュレーテッドモービングベッド（SMB）技術を応用した分離プロセスの設計・最適化を主な研究テーマとしています。非線形動的モデルに基づく動的最適化や、偏微分代数方程式（PDAEs）を用いた完全離散化手法を駆使し、生産性と分離効率の両立を実現するプロセス設計法を開発しています。特に糖・医薬品・石油化学分野への応用を視野に、自動最適化アルゴリズムによるプロセス開発の実現を目指しています。
Figures are computed from collected data and may differ slightly.
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
Open papers in the app to read, cite, and organize with AI.
