Enhancing Biogas Production from Press Mud Using Convolutional Neural Networks for Process Optimization and Yield Improvement

Abstract

This paper discusses the application of Convolutional Neural Networks in improving biogas production from byproducts of sugar industries, particularly press mud. It identifies the optimal conditions that predict high yield for the enhancement of biogas production. Biogas is a renewable source of energy. Increasing production of biogas from press mud provides an ecologically friendly approach to waste management. The optimized key process parameters would comprise moisture content, organic loading rates, retention time, pH, pre-treatment methods, and metal catalysts, such that maximum yield and efficiency of biogas production was obtained. For the CNN model, several samples of press mud are all characterized by defined process parameters and their respective bio-gas yields as assigned to either 'Optimal' or 'Suboptimal' configurations based on the respective efficiencies. Experimental results show that CNN achieved excellent accuracy, where, at the starting step, training and validation accuracies reached saturation to 100%. Validation loss had dramatically reduced from 0.5765 at the first epoch to 0.0002 at the 50th epoch, which showed the availability of strong learning capability along with generalization capability on validation data. These results assure the usability of the proposed CNN model for predicting biogas's optimal production conditions. The data shows an order-of-magnitude betterment over conventional trial-and-error optimization techniques that focus on optimizing biogas production from press mud, promising data-driven methods that better the incumbent optimum conditions and improve sustainability in bioenergy systems.