A differential evolution based henry gas solubility optimizer for dynamic performance optimization problems of PRO system

As a promising renewable energy resource, pressure retarded osmosis (PRO) is developing rapidly. Under the fluctuating environmental condition, fewer oscillations and higher convergence speeds are necessary for a stable operation of the PRO system and higher energy extraction. Metaheuristic algorithms are potential techniques for PRO at an accelerating rate, but the balance between the exploitation and exploration process is an inherent challenge in real-time efficiency and accuracy. In this work, a differential evolution (DE) based henry gas solubility optimization (EHO) is proposed for the scaled-up PRO module based on experimental data with respect to varying operational situations. In EHO, the DE mechanism and levy flight technique are applied to enhance the reliability and effectiveness of the classic HGSO strategy. The most advanced intelligent algorithms, including DFOA, GWO and WOA, are conducted for competitive research for verification purposes. Moreover, the superiority of the proposed algorithm has been evaluated and validated in complex operational environments under variations in temperature, draw concentrations and flow rates levels. The modelling results indicate that compared with the classic HGSO method, the proposed method leads to an improvement of the extracted specific energy of the PRO system by an astonishing 84.21%, 111.11% and 175.03%, respectively.