Deep convolutional neural network with sine cosine algorithm based wastewater treatment systems

Abstract

Wastewater treatment systems are essential in today's business to meet the ever-increasing requirements of environmental regulations while also limiting the environmental impact of the sector's discharges. A new control and management information system is needed to handle the residual fluids. This study advises that Wastewater Treatment System (WWTS) operators use intelligent technologies that analyze data and forecast the future behaviour of processes. This method incorporates industrial data into the wastewater treatment model. Deep Convolutional Neural Network (DCNN) and Since Cosine Algorithm (SCA), two powerful artificial neural networks, were used to predict these properties over time. Remediation actions can be taken to ensure procedures are carried out in accordance with the specifications. Water treatment facilities can benefit from this technology because of its sophisticated process that changes feature dynamically and inconsistently. The ultimate goal is to improve the precision with which wastewater treatment models create their predictions. Using DCNN and SCA techniques, the Chemical Oxygen Demand (COD) in wastewater treatment system input and effluent is estimated in this study. Finally, the DCNN-SCA model is applied for the optimization, and it assists in improving the predictive performance. The experimental validation of the DCNN-SCA model is tested and the outcomes are investigated under various prospects. The DCNN-SCA model has achieved a maximum accuracy performance and proving that it outperforms compare with the prevailing techniques over recent approaches. The DCNN-SCA-WWTS model has shown maximum performance Under 600 data, DCNN-SCA-WWTS has a precision of 97.63%, a recall of 96.37%, a F score of 95.31%, an accuracy of 96.27%, an RMSE of 27.55%, and a MAPE of 20.97%.