Optimization of Liquid Desiccant Regenerator with Multiobject Particle Swarm Optimization Algorithm

Abstract

In this paper, a model-based optimization strategy for a liquid desiccant regenerator operating with lithium chloride solution is presented. By analyzing the characteristics of the components, such as electric heater, pump, and fan, energy predictive models for the components in the regenerator are developed. To minimize the energy usage while maintaining the regeneration rate within an accepted level, one multiobjective optimization problem is formulated with two objectives, the constraints of decision variables, components interactions, and the outdoor conditions. A multiobjective optimization strategy based on decreasing inertia weight particle swarm optimization (DIWPSO) is proposed to obtain the optimal nondominated solutions of the optimization problem, and a decision making strategy is introduced to select the final solution, desiccant solution flow rate, desiccant solution temperature, and the regenerating air flow rate, to minimize the energy usage in the regenerator. Experimental studies are carried out on an existing system to compare the energy consumption and regeneration rate between the proposed optimization strategy and conventional strategy to evaluate energy saving performance of the proposed strategy. Experimental results demonstrate that an average of 8.55% energy can be saved by implementing the proposed optimization strategy in liquid desiccant regenerator.