In the biogas plants, organic material is converted to biogas under anaerobic conditions through physical and biochemical processes. From supply of the raw material to the arrival of the products to customers, there are serial processes which should be sufficiently monitored for optimizing the efficiency of the whole process. In particular, the anaerobic digestion process, which consists of sequential complex biological reactions, requires improved monitoring to prevent inhibition. Conventional implemented methods at the biogas plants are not adequate for monitoring the operational parameters and finding the correlation between them. As Artificial Intelligence has been integrated in different areas of life, the integration of it into the biogas production process will be inevitable for the future of the biogas plant operation. This review paper first examines the need for monitoring at the biogas plants with giving details about the process and process monitoring as well. In the following sections, the current situation of implementations of Artificial Intelligence in the biogas plant operation and in the similar industries will be represented. Moreover, considering that all the information gathered from literature and operational needs, an implementation model will be presented.

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.

Process optimization with Lean Six Sigma (LSS) has become more popular every day for years in almost every kind of industry. This integration has brought an even wider variety of possible application areas for industries and research institutes. Recently, the use of LSS for process optimization in biological fields has become more and more common. In this study, LSS methodology is used for process optimization in an industrial scale biogas plant in Hamburg, Germany. The methodology used includes all the DMAIC cycle and related tools. Hypothesis tests were used to calculate the p-value of each experiment for the LSS interpretation. Due to the experimental factors, one-way ANOVA and 1-sample Z-test were used to determine the p-values. By conducting hypothesis testing after the analysis phase of this study, it was found that particle size, freshness of the substrate, and the amount of sand content in the substrate had a significant effect on the desired amount of biogas produced with a p-value of less than 0.01. These root causes led to approaches that focused on high quality feedstock and sufficient pretreatment methods. This paper represents a pioneering example of integrating Lean Six Sigma into biogas plant operation.

Although the benefits of the Lean Six Sigma (LSS) methodology have been proven for more than 20 years, it is still underutilized in environmental science, (e.g., in anaerobic digestion and biogas production). In order to obtain a structural and data-oriented perspective for process optimization in the renewable energy sector, LSS application must be considered one of the most valuable tools. To inform this paper, the LSS analysis phase was conducted in a scaled-down environment through detailed laboratory experiments. Results showed not only the feasibility of LSS application in biogas technology, but also some useful findings such as possible root causes for low production, such as impurities, waiting time, and existing pre-treatment methods for the defined problem. The results of the experiments show that the use of old substrates can reduce the biogas production up to half of the production with fresh substrates, and that even a 10% sand content can reduce the production up to 14.2%, which shows the need for a solution to these two issues.