Plant-Scale Biogas Production Based on Integrating of CEEMDAN Decomposition with PSO Optimized Multilayer Perceptron Neural Network

Accurate and dependable forecasting of biogas production is vital for optimizing process parameters and maintaining stable operation in large-scale anaerobic digestion projects. In this study, a novel hybrid approach (CEE-PMLP) integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a multilayer perceptron (MLP) neural network optimized by particle swarm optimization (PSO) were proposed for predicting biogas production in large-scale anaerobic digesters (ADs). The methodology involves extracting Intrinsic Mode Function (IMF) components using CEEMDAN and subsequently employing MLP optimized by particle swarm optimization (PSO) to predict each component. The performance of the models was evaluated using root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), and fitting determination coefficient (R2). The findings revealed that the prediction errors of the proposed CEE-PMLP model were consistently lower than those of other comparative models. Notably, the model achieved the highest R2 value of 98%, indicating an exceptionally high accuracy in prediction. The validation experiment confirmed the high accuracy of the CEE-PMLP model, further demonstrating its superiority in biogas production prediction.