A Novel AGPSO3-based ANN Prediction Approach: Application to the RO Desalination Plant

One of the critical issues faced by the desalination plants is an accurate analysis of their real-time performance. Soft computing techniques are efficient in overcoming these issues and predicting accurate outcomes. In this paper, models based on the third version of the modified particle swarm optimization algorithm called autonomous groups particles swarm optimization—based artificial neural network (AGPSO3-ANN) have been proposed for accurate prediction of permeate flux of reverse osmosis (RO) desalination plant. It employs remarkable optimization strategy that demonstrates superiority than the conventional PSO-ANN techniques, to update acceleration factors (c₁ and c₂) and to screen the global best solution. Here, four input parameters: evaporator inlet temperature, feedwater salt concentration, condenser inlet temperature, and feed flow rate have been considered for the modeling, and models' performance evaluated in terms of the regression coefficient (R²) and mean square errors (MSE). The results show an impressive agreement between simulated and experimental datasets indicating that the proposed approach is strongly capable of finding optimal solutions to predict accurate results with minimum errors (R² = 99.2%, MSE = 0.005) compared to the existing ANN approaches. This demonstrates that the proposed models based on such soft computing tools like AGPSO3-ANN are perfect for analyzing and predicting real-time desalination plant performance that would present an effective way for improved process control and efficiency for plant engineers.