Using a Novel Algorithm Based on the Random Vector Functional Link Network and Multi-Verse Optimizer to Forecast Effluent Quality

The treatment of wastewater is a complicated biological reaction process. Reliable effluent prediction critical in the scientific management water plants. This research proposes soft sensor design strategy to address issues above, Multi-Verse Optimizer (MVO)-based random vector functional link network (MVO-RVFL). proposed approach utilized anticipate real-time data obtained from Benchmark Simulation Model 1 (BSM1). results experiments demonstrate that MVO methodology can successfully find optimum input-hidden weights and hidden biases RVFL model while outperforming original other typical machine learning approaches all types influent datasets. In situation significant quality variations, use fusion process for development was also investigated. experimental incorporating prior knowledge effectively improve model’s ability cope with unexpected situations.