Multi-objective optimization of direct solar water heater system by integrating LINMAP decision making analysis and NSGA-II

This paper aims to design a thermo-mathematical modeling and multi-objective programming of a solar water heater system (SWHS) that supplies domestic hot water for a typical building in the mild climate zone of Iran throughout the whole year. The numerical modeling of the SWHS is developed so that the performance of the system is solved through the Artificial Neural Network (ANN). After the verification of the SWHS model, the single-objective optimization (SOO) and multi-objective optimization (MOO) of the SWHS are carried out using genetic algorithm (GA) to obtain the optimal solutions of design parameters, where the coefficient of performance (COP) and the solar collector efficiency (SCE) are selected as two fitness functions. The optimal solutions achieved from the MOO process will be given as Pareto optimal frontier. The final optimum solution from the available solutions on the Pareto optimal frontier is selected using LINMAP decision-making method. The comparison of SOO and MOO results illustrate that the MOO method yields more proper results than single ones. The results of MOO show that SCE decreases ۱.۶% compared to the initial model while the COP increases close to ۲۰% that makes the optimum solution more desirable compared to the single ones. Moreover, the performance of the optimized SWHS is compared with the initial model in each month of the year. The results indicate that the performance of the optimized SWHS is highly improved so that the working hours of the system decrease close to ۱۰۹ hours compared to the initial model during the whole year.

Keywords:

Solar water heater system , Solar collector efficiency , Coefficient of performance , Parametric study , Multi-objective optimization , Decision-making