Electrical energy recovery from wastewater: prediction with machine learning algorithms

Wind, solar, biomass, tidal, etc. are renewable energy sources obtained from natural sources. Among these resources, biomass can be characterized as a significant energy source. Today, the process of producing biogas from waste and turning it into electrical energy has become more popular. So, clean, sustainable, and eco-friendly energy is generated as the waste is managed and converted into electrical energy. The estimation of the electrical energy that will be produced by wastewater recovery using machine learning (ML) algorithms is vital and has not yet been investigated. Thus, this study fills this gap. In this study, it is aimed to predict the electrical energy recovery potential of the sewage sludge of Kahramanmaraş Advanced Biological Wastewater Treatment Plant (KABWWTP) (Turkey), through incineration and anaerobic digestion. For this aim, 6 distinct ML algorithms including linear regression (LR), extreme gradient boosting (XGB), Gaussian process regression (GPR), ridge regression (RR), Lasso regression (LASReg), and Bayesian ridge regression (BR) have been used. Another novelty in this study is the restricted number of input parameters. That is, the electrical energy (output parameter) is predicted using only 3 distinct input parameters (gas flow, conductivity, and TSS). With a MAPE value of 1.032, the XGB method has been determined as the most successful model. Heat mapping and correlation analyses are used to evaluate the relationship between these parameters. Performance results are presented in tables and graphs.