Artificial Neural Network with extreme learning machine-based Wastewater Treatment systems

The rapid increase in the number of people living in different regions of the globe has led to a significant problem: there is not enough freshwater accessible. The bulk of the world's water resources come from the ocean, and this includes both brackish water and saltwater from the ocean. Desalination and other practises for aquatic behaviour that make it feasible to meet the needs for water in both residential and business settings are two examples of processes that make this possible. In order to treat water and wastewater, membrane processes like microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), membrane bioreactor (MBR), and membrane distillation (MD) are utilised. MF, UF, and NF stand for microfiltration, ultrafiltration, and nanofiltration, respectively. In recent years, models of artificial intelligence, such as artificial neural networks (ANN), have emerged as a generic alternative for the purpose of modelling these methods. This may be explained by the many advantages that these models have over the conventional model in contrast to which they were based. In this paper, an overview of artificial neural network (ANN) models that have been created over the last two and a half decades for membrane methods that have been employed in the action of wastewater and purification is provided. In order to make accurate forecasts of these features' evolution over time, we used ANN and ELM, two strong artificial neural networks. It is possible to conduct corrective measures in order to guarantee that the operations will be carried out in accordance with the requirements. This technique may be beneficial to water treatment facilities because to its complex process, which can modify its characteristics in a manner that is both dynamic and inconsistent. The ultimate objective is to increase the degree of accuracy that can be achieved by wastewater treatment models when making forecasts. This research estimates the concentration of COD in the effluent and the input of wastewater treatment systems using ANN and ELM algorithms. Accurate methods may be obtained by the ANN-ELM systems. Using artificial intelligence to model difficult system.