Artificial neural network and response surface methodology for modeling reverse osmosis process in wastewater treatment

Abstract

The reverse osmosis (RO) process is widely utilized to reduce highly hazardous chemicals in wastewater, resulting in decreased electrical conductivity and enhanced usability. Reverse osmosis is regarded as an efficient desalination technique, and a comprehensive understanding of the mathematical modeling correlation in the RO system can contribute to the development of advanced RO systems. This research is concerned with the modeling and optimization of the RO process by leveraging machine learning techniques, such as Artificial Neural Networks (ANN) and Response Surface Methodology (RSM). Specifically, an ANN and RSM employing a central composite design (CCD) were performed to analyze the influence of key parameters, including flow rate (30–70 m3/hr), initial conductivity (2000–4000 µs/cm), feed pressure (13–17 bar), and solution temperature (11–39 °C), on the reduction of total dissolved solids (TDS) represent by conductivity in wastewater treatment. The outcomes derived from the RSM-CCD analysis demonstrated that the optimal conditions for achieving the lowest conductivity of 35 ± 10 µs/cm included a solution temperature of 31.6 °C, feed pressure of 16 bar, flow rate of 40 m3/hr, and an initial conductivity of 3500 µs/cm. Five ANN models have been suggested to evaluate the plant’s performance. Model-5 with two hidden layer, eleven hidden layer nodes (20 and 30 nodes) for first and second layers respectively. Moreover, ANN exhibited excellent performance, with a low (MSE) of < 0.0003 and a high (R2) of more than 0.99. These findings highlight the valuable utilization of RSM and ANN methodologies in the modeling and optimization procedures of the RO process.