Abstract

A transition to solar energy systems is considered one of the most important alternatives to conventional fossil fuels. Until recently, solar air heaters (SAHs) were among the other solar energy systems that have been widely used in various households and industrial applications. However, the recent literature reveals that efficiencies of SAHs are still low. Some metaheuristic algorithms have been used to enhance the efficiencies of these SAH systems. In the paper, we do not only discuss the techniques used to enhance the performance of SAHs, but we also reviewed a majority of published papers on the applications of SAH optimization. The metaheuristic algorithms include simulated annealing (SA), particle swarm optimization (PSO), genetic algorithm (GA), artificial bee colony (ABC), teaching-learning-based optimization (TLBO), and elitist teaching-learning-based optimization (ETLBO). For this research, it should be noted that this study is mostly based on the literature published in the last ten years in good energy top journals. Therefore, this paper clearly shows that the use of all six proposed metaheuristic algorithms results in significant efficiency improvements through the selection of the optimal design set and operating parameters for SAHs. Based on the past literature and on the outcomes of this paper, ETLBO is unquestionably more competitive than ABC, GA, PSO, SA, and TLBO for the optimization of SAHs for the same considered problem. Finally, based on the covered six state-of-the-art metaheuristic techniques, some perspectives and recommendations for the future outlook of SAH optimization are proposed. This paper is the first-ever attempt to present the current developments to a large audience on the applications of metaheuristic methods in SAH optimization. Thus, researchers can use this paper for further research and for the advancement of the proposed and other recommended algorithms to generate the best performance for the various SAHs.

Highlights

  • World energy consumption is increasing rapidly due to population growth and technological advancements

  • (i) artificial bee colony (ABC) and genetic algorithm (GA) optimization solutions were more precise than traditional methods. (ii) The results show that both the ABC and GA techniques were profitably applied for the thermal solar air collector (SAC) efficiency optimization

  • The present paper reviews the recent studies on the applications of six state-of-the-art metaheuristic algorithms in solar air heater optimization and highlights their recent trends and future prospects

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Summary

Introduction

World energy consumption is increasing rapidly due to population growth and technological advancements. The exploitation and use of solar power will be pivotal to future energy development because it acts as one of the most mature and promising options [5, 6]. Solar air heaters (SAHs) do have problems related to corrosion, salt deposits, and freezing and boiling point temperatures; but they have low thermal performance and very small heat storage capability [14,15,16]. The thermal performance of SAHs can be enhanced by the collector shape and design modification [16], the use of heat storage materials [17], the effect of artificial roughness [18], and other factors. Different optimization techniques such as metaheuristic algorithms have been extensively utilized to obtain sets of optimized parameter values that contribute to the thermal effectiveness of such solar heaters

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