Modeling and Predicting Petroleum Wastewater Treatment Outcomes with Neural Networks

This study investigates AI techniques for improving petroleum wastewater treatment. Using MATLAB, it develops two machine learning models, multilayer perceptron neural networks (ANNs) and support vector machines (SVMs), to predict pollutant levels and optimize treatment processes. Data from 26 weekly samples, including chemical oxygen demand (COD), ammonia (NH3), and nitrate (NO3), are utilized. The dataset is split into training and testing sets, with values normalized for consistency. ANN models are trained with various algorithms, while SVM models use Quadratic Programming and Gaussian kernel functions. Results indicate that SVMs surpass ANNs in prediction accuracy, particularly with limited data. A user-friendly graphical interface is created to facilitate quick and accurate predictions, enhancing the practical application of these models in real-world scenarios. This software aids efficient management of treatment processes in the petroleum industry, showcasing the potential of AI-driven solutions for sustainable wastewater treatment.