State estimation of a biogas plant based on spectral analysis using a combination of machine learning and metaheuristic algorithms

The continuous monitoring of the state variables of a biogas plant remains a challenge due to the necessity of an appropriate measuring device. The collection, transportation, and laboratory measurement of the biogas sample are required, and regularly performing these activities is expensive and time-consuming. The objective of this study is to investigate a potential solution for the real-time monitoring of the state variables of a biogas plant, which involves the integration of spectral data from near-infrared (NIR) sensors with advanced machine learning (ML) and metaheuristic algorithms. A total of 635 samples were prepared in a laboratory and subsequently analyzed using portable NIR sensors mounted in sensor brackets that were 3D-printed for this study. The resulting spectral data were subjected to several preprocessing processes, and this study combines numerous methods rather than selecting the optimal one. Several ML models were then trained on the processed data, which have a realistic range as in the actual biogas plant. The developed algorithm exhibits an accuracy level greater than 82 % when classifying the volatile fatty acids (VFA)/total alkalinity (TA) ratio and acetic acid concentration of biogas samples. Furthermore, the dry matter content of biogas samples can be accurately predicted with an RMSE of approximately 1.6 %. In addition, the significance of selecting suitable hyperparameters is demonstrated, which may substantially influence the outcome. These findings indicate that NIR sensors are a realistic option for monitoring the operational status of biogas plants.