A convenient-accurate optimization method of the multi-effect evaporation system

Abstract

In this work, a convenient-accurate multi-effect evaporation system optimization method that integrates regression analysis and a multi-objective genetic algorithm has been proposed to optimize the design of the multi-effect evaporation system. Firstly, a multitude of numerical simulation calculations are conducted employing precise thermodynamic models. Secondly, regression analysis is performed on the experimental data to establish the approximate model between the design variables and performance parameters. Finally, NSGA-II, a non-dominated sorting genetic algorithm with an elite strategy, is employed to optimize and solve the approximate model derived from regression analysis. Consequently, a series of Pareto solutions satisfying the given conditions are obtained. The two initial schemes of the FF-MEE (forward-feed) system and the PF-MEE (parallel-feed) system are optimized using the proposed optimization method. The results show that following optimization, the Gained Output Ratio (GOR) of the FF-MEE system is increased by 3.6%, while the specific heat transfer area (As) is reduced by 16%. Similarly, for the PF-MEE system, the GOR is enhanced by 7.8%, and As is decreased by 19%. The proposed method provides a comprehensive set of optimization solutions, ensuring rapid solution speed, straightforward application, and high solution accuracy.