Machine Learning Methods for Prediction of Energy Production in a Wastewater Treatment Plant: Case Study

Over the next few years, prediction of biogas production using machine learning techniques at municipal wastewater treatment plants may become an indispensable tool for ensuring the energy self-sufficiency of such facilities. The objective of this study was to develop and compare predictive models capable of estimating energy management indicators for a wastewater treatment plant based on technological and physicochemical parameters for the wastewater treatment plant in Iława (Poland). In the first stage, the process parameters were subjected to principal component analysis (PCA). Next, predictive models were built based on four approaches: a multilayer perceptron (MLP), a random forest, support vector regression (SVR), and the XGBoost algorithm, and their predictive capabilities were compared using test data. The results of the study showed that a multilayer neural network allows for very accurate modeling of selected energy factors related to the operation of a wastewater treatment plant. The use of model optimization techniques—cross-validation (base models) and hyperparameter tuning (neural network)—provide evidence that the multilayer neural network has the greatest potential for accurate and reliable prediction, with a final performance result of R2 = 0.9752 for biogas production.