Machine Learning Algorithms for Temperature Management in the Anaerobic Digestion Process

Abstract

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