Machine Learning Algorithms for Temperature Management in the Anaerobic Digestion Process

Process optimization is no longer an option for processes, but obligation to survive in the market any industry. This argument also applies anaerobic digestion biogas plants. The contribution of plants renewable energy can be increased through more productive systems with less waste, which brings common goal minimizing costs and maximizing yields processes. With help data science predictive analytics, it possible take conventional process operational excellence methods, such as statistical control Six Sigma, next level. advanced aspect, transparent responsive systems. In this study, seven different machine learning algorithms—linear regression, logistic K-NN, decision trees, random forest, support vector (SVM) XGBoost—were compared laboratory results define predict impacts wide range temperature fluctuations on stability. SVM provided best accuracy 0.93 according metric precision models calculated using confusion matrix.