(399e) Modeling and Multi-Objective Optimization of Continuous Protein Recovery in Liquid-Solid Circulating Fluidized Bed Ion Exchange System

In recent years, liquid-solid circulating fluidized beds (LSCFBs) are being applied as a reactor system in a number of new applications. This study focused on the modeling and multi-objective optimization of LSCFB system for continuous protein recovery process. A mathematical model was developed considering the protein adsorption and desorption characteristics, liquid-solid mass transfer and the hydrodynamics of the LSCFB, to predict the protein adsorption and desorption performance of the LSCFB system. The simulation results showed good agreement with available experimental data. Parametric sensitivity analysis showed complex interplay of various operational parameters over the system performance indicators. Moreover, the change in operating conditions in one column affects the performance of the other column as the two columns are interlinked.

Like most real-life optimization problems, the design and operation of LSCFB system for continuous protein recovery is also associated with several important objectives such as production rate and recovery of protein, and ion exchange resin requirements, which need to be optimized simultaneously. Multi-objective optimization of the LSCFB system at both the operation and the design stage were carried out using the model developed to determine the range of optimal solutions. Elitist non-dominate sorting genetic algorithm with its jumping gene adaptation (NSGA-II-aJG) was used to solve a number of two- and three- objective function optimization problems. The optimization resulted in Pareto optimal solutions, which provides a broad range of non-dominated solutions due to conflicting behavior of the operating and design parameters on the system performance indicators. Significant improvements were achieved, for example, for the same recovery level, the production rate at optimal operation increased by 33%, using 11% less solids compared to experimental results. In the design stage optimization, the performance of the system was further improved. This modelling and multi-objective optimization study is very general and can be easily extended for the improvement of LSCFB in other applications.

Keywords: Liquid-Solid Fluidization, LSCFB, protein recovery, ion-exchange, modelling, simulation, multi-objective optimization, Pareto sets, genetic algorithm.