Green Innovation: Leveraging Convolutional Neural Networks for Enhanced Biogas Production from Hybrid Napier Grass and Co-Digestion Processes

Optimal biogas production remains a critical step in increasing renewable energy output from biomass resources. Hybrid Napier Grass is one of the promising substrates to produce biogas, mainly due to its high yield potential and adaptability, though achieving optimal output in this case still lags due to the variability of substrates, nutrient imbalance problems, and the complexity of co-digestion processes of various materials such as cattle slurry and chicken manure. For the first time in this study, CNN will be used as an optimization approach to condition anaerobic digestion, in which parameters are tuned in real-time to get the maximum yields of biogas. With an exhaustively prepared dataset of the Napier Grass and its co-substrates, CNN models are developed for inferring substrate composition, moisture, and nutrient ratios in real-time. Some key findings from the experimental results include: Accuracy of the CNN model reaches 100% on training data by about epoch 9, but the validation accuracy plateaued at 83.33%, which is overfitting, capturing of training-specific noise-affecting generalization to unseen data. Validation accuracy and loss stabilize around epoch ranges 10-20, but the training loss continued to decrease, demonstrating the power of the CNN in learning the training data. The validation loss of the model was also improving gradually but at a diminishing rate, which indicated some generalization of the current architecture of the dataset. This work can stand as a testament for unlocking optimization through CNNs in biogas production processes; this research has already shown an increase up to 20% more than conventional methods. Of course, further refinements will be needed for generalization purposes, but the AI-driven approach represents a significant advance in optimization and supports scalable and sustainable biogas development in bioenergy. This proposed CNN model was theoretically efficient and superior as far as classification accuracy in predicting biogas production was concerned, with an accuracy of 83.33% with consistent improvement across training rounds and moderate time complexity compared to the traditional models discussed above; thus, it will become a competitive tool for optimizing process parameters and improving the operational decisions to maximize biogas yield.