Optimization of effluents using a neural network in the treatment of industrial wastewater

Abstract

The growth of the planet's population leads to an increase in the problem of access to fresh water. The main sources of water on Earth are brackish and sea water. In connection with the water crisis, water purification becomes an extremely important process, and its achievement is carried out through desalination and various methods of water treatment. In this context, research into the possibility of using neural networks to improve the operation of sewage treatment plants is necessary. The purpose of the research was to optimize and analyze the efficiency of the work of treatment facilities in the treatment of industrial wastewater. Soft computing methods were used to optimize the proposed models. In this study, the exact results of the application of the neural network were determined using analytical and comparative approaches. Treatment of all wastewater and waste generated in the treatment industry involves a number of processes including air flotation, chemical coagulation, settling and biological treatment using fully mixed activated sludge. Various learning functions have been considered, including forward-propagation artificial neural networks (ANNs) such as multilayer perceptron (MLP), cascaded forward-propagation ANNs, and support vector regression (SVR) models. The learning process includes the use of Levenberg-Marquardt optimization algorithms and sequential minimum. The article also provides graphical images illustrating the different types of pollutants, the costs associated with treatment plants, and the color changes in wastewater observed after the treatment process. The obtained results show a high degree of similarity between the predicted and experimental data, which emphasizes the effectiveness of the backpropagation ANN model for accurate predictions. In addition, the integration of machine learning into the production of detergents can be extremely effective in promoting the efficient and sustainable use of water resources. Overall, the paper provides valuable insights into the use of machine learning to address freshwater scarcity