A novel IEF-DLNN and multi-objective based optimizing control strategy for seawater reverse osmosis desalination plant

Over the past years, Seawater Desalination (SWD) has been enhanced regularly. In this desali-nation process, numerous technologies are available. The Reverse Osmosis (RO) process, which requires effectual control strategies, is the most commercially-dominant technology. Therefore, for SWD, a novel Interpolation and Exponential Function-centered Deep Learning Neural Network (IEF-DLNN) and multi-objective-based optimizing control system has been proposed in this research methodology. Initially, the input data are gathered; then, to control the desalination process, an optimal control technique has been utilized by employing Probability-centric Dove Swarm Optimization-Proportional Integral Derivative (PDSO-PID). The attributes of permeate are extracted before entering the RO process; after that, by utilizing the IEF-DLNN, the trajectory is predicted. For optimal selection, the extracted attributes are deemed if the trajectory is present, or else to mitigate energy consumption along with cost, the RO Desalination (ROD) is performed. In an experimental evaluation, regarding certain performance metrics, the proposed model's per-formance is analogized with the prevailing methodologies. The outcomes demonstrated that the proposed system achieved better performance.

Automated summary

A novel Interpolation and Exponential Function-centered Deep Learning Neural Network (IEF-DLNN) and multi-objective-based optimizing control system has been proposed for Seawater Desalination (SWD). Experimental evaluation shows that the proposed system achieves better performance compared to existing methodologies.