Optimizing managed Artificial Recharge backwash using a Multi-objective Particle Swarm Optimization coupled with a clogging simulation model

Artificial Recharge (AR) is pivotal in managing groundwater resources and addressing hydrogeological issues. Over the past decades, significant research has focused on the clogging mechanisms but paid limited attention to devising effective strategies. This study introduces an optimization framework that integrates a clogging model with two objective functions aimed at minimizing clogging during groundwater recharg by using multi-objective particle swarm optimization(MOPSO) algorithm .The proposed clogging model for groundwater recharge accounts for both physical clogging and iron oxide clogging. It comprehensively addresses suspended solids' adsorption and iron oxidation reactions using a coupled COMSOL and PHREEQC approach. The MOPSO algorithm is employed to obtain Pareto bounds, aiding in identifying suitable recharge and backwash options among diverse groundwater recharge scenarios. This approach enables stakeholders to assess varied scenarios based on blockage conditions and recharge efficiency. The optimization findings underscore the effectiveness of proper backwashing in significantly reducing clogging and extending equipment life in groundwater recharge projects.