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Biomethane Yield Modeling Based on Neural Network Approximation: RBF Approach
Biogas production plays a key role in the development of renewable energy systems; however, forecasting biomethane yield remains challenging due to the nonlinear nature of anaerobic digestion. The objective of this study was to develop a predictive model based on Radial Basis Function Neural Networks (RBF-NN) to approximate biomethane production using operational data from the Przybroda biogas plant in Poland. Two separate models were constructed: (1) the relationship between process temperature and daily methane production, and (2) the relationship between methane fraction and total biogas flow. Both models were trained using Gaussian activation functions, individually adjusted neuron parameters, and a zero-level correction algorithm. The developed RBF-NN models demonstrated high approximation accuracy. For the temperature-based model, root mean square error (RMSE) decreased from 531 m3 CH4·day−1 to 52 m3 CH4·day−1, while for the methane-fraction model, RMSE decreased from 244 m3 CH4·day−1 to 27 m3 CH4·day−1. The determination coefficients reached R2 = 0.99 for both models. These results confirm that RBF-NN provides an effective and flexible tool for modeling complex nonlinear dependencies in anaerobic digestion, even when only limited datasets are available, and can support real-time monitoring and optimization in biogas plant operations.
Using soap waste from biodiesel production to intensify biogas generation during anaerobic digestion of cow dung
The aim of the work is to increase the yield of biogas and the generation of electricity at biogas plants due to the joint fermentation of cattle manure with the addition of soap stock obtained from soap waste from biodiesel production. To achieve this goal, the following tasks were solved: the yield of biogas from cattle manure was determined with the addition of soap stock for a periodic mode of loading the substrate, taking into account the data obtained, a mathematical model of biogas output for a quasi-continuous mode of loading the substrate into the digester was developed and its adequacy was confirmed. The novelty of the work lies in the fact that according to the data of experimental studies of biogas yield at a periodic loading mode using this model, it is possible to predict the maximum biogas yield for a quasi-continuous mode of loading the digester. The significance of the research results lies in the fact that when soap stock is added to the substrate with a periodic mode of loading the digester, a general increase in the biogas yield without diauxy is observed by about 2 times. The optimal content of soap stock in the substrate for a quasi-continuous mode of loading the digester, at which the biogas yield will be maximum, is 1.32%. When electricity is sold at a feed-in tariff, the payback period of a biogas plant is reduced from 8.7 years to 5.0 years.