A new artificial neural network model integrated with a cat swarm optimization algorithm for predicting the emitted noise during axial piston pump operation

Utilization of random vector functional link integrated with manta ray foraging optimization for effluent prediction of wastewater treatment plant

An innovative predictive model was employed to predict the key performance indicators of a full-scale wastewater treatment plant (WWTP) operated with an activated sludge treatment process. The data-driven model was obtained using data gathered from Cairo, Egypt. The proposed model consists of Random Vector Functional Link (RVFL) Networks incorporated with Manta Ray Foraging Optimizer (MRFO). RVFL is used as an advanced Artificial Neural Network (ANN) that avoids the common conventional ANN problems such as overfitting. MRFO is employed to determine the best RVFL parameters to maximize the prediction accuracy of the model. The developed MRFO-RVFL is compared with conventional RVFL to figure out the role of MRFO as an optimization tool to enhance model performance. Both models were trained and tested using experimental data measured during a long period of 222 days. This study aims to provide an accurate prediction of the most widely treated effluent indicators of BOD₅ and TSS in the wastewater treatment plants. In this study, ten well-known influent wastewater parameters, BOD₅, TSS, and VSS, influent flow rate, pH, ambient temperature, F/M ratio, SRT, WAS, and RAS, the output BOD₅ and TSS were modeled and predicted using the integrated MRFO-RVFL algorithms and compared with the standalone RVFL model. The performance of the models was evaluated using different assessment measures such as R², RMSE, and others. The obtained results of R² and RMSE for the MRFO-RVFL model were 0.924 and 3.528 for BOD₅ and 0.917 and 6.153 for TSS, which were much better than the results of conventional RVFL with 0.840 and 6.207 for BOD₅ and 0.717 and 10.05 for TSS. Based on the obtained results, the selective model (MRFO-RVFL) exhibited a higher performance and validity to predict the TSS and optimal BOD₅.