Machine Learning Algorithms for Dynamic System Identification in Wastewater Treatment Plant

Abstract

A comprehensive study on the application of machine learning algorithms for dynamic system identification in wastewater treatment plants (WWTP) is presented. The research focuses on developing a flexible neural network model to predict the behavior of key variables in the aeration process of a pilot-scale water treatment plant. The methodology involves data collection from experimental trials, data preprocessing, neural network model development, validation, and implementation. The results demonstrate the effectiveness of the proposed approach in accurately predicting key variables such as dissolved oxygen, tank temperature, and tank level (mean squared error MSE=0.166 and coefficient of determination R2=0.967). The discussion highlights the importance of variable selection, data preprocessing techniques, model architecture design, and validation procedures. The conclusions emphasize the significance of machine learning techniques in optimizing wastewater treatment processes, improving energy efficiency, and facilitating real-time decision making. Recommendations for future research include scaling up the model to larger treatment plants, incorporating advanced deep learning techniques, and continuous validation and optimization of the model.