Prediction of the Methane Production in Biogas Plants Using a Combined Gompertz and Machine Learning Model

Abstract

Biogas production is a complicated process and mathematical modeling of the process is essential in order to plan the management of the plants. Gompertz models can predict the biogas production, but in co-digestion, where many feedstocks are used it can be hard to obtain a sufficient calibration, and often more research is required in order to find the exact calibration parameters. The scope of this article is to investigate if machine learning approaches can be used to optimize the predictions of Gompertz models. Increasing the precision of the models is important in order to get an optimal usage of the resources and thereby ensure a more sustainable energy production. Three models were tested: A Gompertz model (Mean Absolute Percentage Error (MAPE) = 9.61%), a machine learning model (MAPE = 4.84%), and a hybrid model (MAPE = 4.52%). The results showed that the hybrid model could decrease the error in the predictions with 53% when predicting the methane production one day ahead. When encountering an offset in the predictions the reduction of the error was increased to 66%.