Multi-Objective Optimization of Parallel-Connected Double-Effect Mechanical Vapor Recompression System Based on Genetic Algorithm

Abstract

To realize multi-objective optimization of the parallel-connected double-effect mechanical vapor recompression (MVR) system, this article established an optimization model based on the Strength Pareto Evolution Algorithm 2 (SPEA2), where the total power consumption and the heat exchange area were taken as the optimization objectives. The optimal combination of evaporation temperature, compression temperature rise, and emission concentration was obtained by employing the SPEA2-based multi-objective evolutionary algorithm together with the fuzzy set theory. The emission concentration was added as a variable on the basis of the original optimization, and the optimization results were compared with the original operation conditions. The results showed that the total power consumption of the system lowered by 22.9 kW, and the heat exchange area was reduced by 110.5m2; the coefficient of performance (COP) and exergy efficiency heightened by 8.4% and 24.0%, respectively, and the exergy destruction decreased by 84.6 kW. These results indicate that the established model for system optimization can make up for the deficiency of evaluating and optimizing system performance by manipulating a single-decision variable and improve the energy utilization and thermodynamic perfection of the target system.