Application of model and algorithms in improving methane yield in an industrial biogas plant

With the ever-growing application of data science and machine learning in the fourth industrial revolution era, many challenges faced within the energy sector in past decades have now been receiving timely interventions through the proper application of programming and machine learning tools coupled with the implementation and utilization of modern technology. In recent years, balancing in real-time the demand and supply of energy generated from renewable sources such as wind and solar has gained much improvement because of its ability to forecast the quantity of energy that could be produced from the renewable sources using historical data. Likewise, the application of model and algorithms has also helped to predict accurately, the amount of energy that could be produced from a batch of anaerobic digestion process to produce biogas or biomethane of acceptable quality. In this research work, a set of data was collected from an industrial biogas plant and based on the variables from the data set, Design Expert Software version 11 was used to develop mathematical models and algorithms to optimize the production process of the plant based on the feedstock fed into the digesters. The result of the optimization proves that the biogas currently produced from the post-digester tank with methane (CH₄) content of about 68.8% can be upgraded to biomethane with methane content of 78.22% without any adjustment to the digesters or production process.