Multi-Objective Optimizations of Non-Isothermal Simulated Moving Bed Reactor: Parametric Analyses

• Main Authors: Jian Wang, Wenwei Chen, Yan Li, Jin Xu, Weifang Yu, Ajay K. Ray
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Processes
• Subjects: simulated moving bed reactor; non-isothermal; multiobjective optimization; parametric sensitivity; Pareto
• Online Access: https://www.mdpi.com/2227-9717/9/2/360

Simulated moving bed reactor (SMBR), a multicolumn multifunctional integrated reactor system, which can be exploited with on-site adsorptive separation to enhance conversion of equilibrium-limited reversible chemical reaction. In this article, for generality, a dimensionless SMBR model was developed and effects of five representative temperature distributions among different zones on the performance of an SMBR for reversible reaction in the general form of a reactant decomposed to two products were evaluated based on simultaneous maximization of unit throughput and product purity. Multipliers were applied to adjust some of the model parameters such that different operation modes can be compared under various conditions in the parametric space. The multiobjective optimization problems were solved using the non-dominated sorting genetic algorithm. All simulations were carried out using FORTRAN codes. The results showed that both kinetics and adsorptive separation play important roles in SMBR. When kinetics is fast or adsorptive potency of the reactant is higher than the desired product (B) but lower than byproduct (C), non-isothermal operation can significantly improve unit throughput. On the contrary, feed concentration and reaction enthalpy have minor effects on the optimal solutions. Decision variables based on flow rate ratios and internal concentration profiles were used to explain the trends of Pareto optimal solution.