Multi-criteria decision-making and artificial bee colony algorithm for optimization of process parameters in pyramid solar still

Abstract

This study focuses on optimizing process parameters for pyramid solar still using Multi-Criteria Decision-Making Grey Relational Analysis (MCDM-GRA) and bio-inspired metaheuristic Artificial Bee Colony (ABC) algorithm. The objective is to enhance the performance of the pyramid solar still (PSS) performance integrated with a peripheral solar water heater under different operational parameters, including saline water flow rate, temperature, and solar intensity. Experiments are designed using Taguchi's L9 (33) orthogonal array (OA), and the outputs are optimized with MCDM-GRA. The optimum conditions for maximized productivity and system efficiency are a solar intensity of 1000 W/m2, a saline water mass of 3 kg, and a saline water temperature of 40 °C. With these settings, the system achieves an efficiency of 65.33 % and produces 1.96 kg of potable water. The ABC Algorithm, on the other hand, suggests that the optimal conditions for achieving maximum productivity and system efficiency involve a solar intensity of 1000 W/m2, a mass of 3 kg for the saline water, and a water inlet temperature of 30 °C. In this scenario, the system produced 1.92 kg of potable water with an efficiency rate of 64 %. Analysis of variance (ANOVA) shows that solar intensity contributes significantly towards productivity by 81.72 % with an error of 3.82 % and regression coefficient of 96.18 %. Regression models are developed from the results obtained and fed to ABC for further optimization, yielding results similar to MCDM-GRA.