Optimizing Biogas and Biofuel Production Using CNNs: Tackling Pretreatment and Process Efficiency Challenges

Biogas and biofuel production from lignocellulosic biomass face notable challenges, particularly related to substrate properties, pretreatment processes, and overall efficiency, resulting in high costs and limited scalability, which hinder broader adoption. This study investigates the use of Convolutional Neural Networks (CNNs) as an innovative approach to optimize biogas and biofuel production. By leveraging comprehensive datasets that include variables such as substrate type, pretreatment strategies, initial moisture content, and enzyme loading, CNNs have proven effective in predicting and enhancing critical performance metrics like biogas yield and methane concentration. The experimental findings demonstrate that training a CNN model on the available dataset yields encouraging results. The model reached a test accuracy of 50%, with an F1 score of 0.60, precision at 0.75, and recall at 0.50. Throughout 50 training epochs, the model showed significant gains in accuracy and a decrease in loss, achieving perfect training accuracy and a peak validation accuracy of 100% before stabilizing at 75%. The results also pointed out areas for improvement, such as managing class imbalances to enhance predictive reliability. This research highlights the potential of CNNs to address the pretreatment and process efficiency obstacles in biogas and biofuel production. The integration of CNNs offers promising benefits, including better process optimization, reduced energy use, and improved yield predictability, which contribute to more cost-effective and sustainable biofuel production. Future studies should aim to strengthen model robustness, scale up data experiments, and incorporate advanced feature engineering to further advance the role of CNNs in this domain.