Harnessing Convolutional Neural Networks for The Optimization of Anaerobic Digestion of Sugarcane Bagasse: A Novel Approach to Pretreatment Strategies and Microbial Activity Prediction

Abstract

This paper is aimed at verifying the effectiveness of CNN in optimizing anaerobic digestion with sugarcane bagasse, considering the enhancement of strategies for pretreatment and accurate prediction of microbial activity and biogas yield. The primary objective of this research work is to increase the utilization efficiency of anaerobic digestion systems along with boosted biogas production using convolutional neural networks for better forecasts and optimizing procedures. This paper discusses the prediction of microbial activity and biogas from the anaerobic digestion of sugarcane bagasse using convolutional neural networks. The experiments conducted here are similar to studies that have studied various types of feedstock pretreatments, such as AFEX, steam explosion, and alkaline treatments. Different architectures of CNNs are trained with feedstock characteristics, process operational parameters, and conditions-related information to predict the output of biogas production (Olatunji et al., 2021; Sharma et al., 2023). This also verifies that CNNs outperform classical machine learning models and algorithms, such as SVMs and regression, in the prediction of the biogas yield from sugarcane bagasse at an accuracy of 92% and F1 score of 0.90. It accurately assesses the pretreatment methods, temperature, and operational conditions that have a considerable impact on biogas production. This is an embodiment of AI in making it possible to improve predictability and optimize biogas production processes (Parvane et al., 2022; Gao et al., 2022). A work analysis revealed that CNNs enhance the anaerobic digestion process of sugarcane bagasse by improving better and more accurate forecasting abilities in comparison to the traditional models while handling complex data. Follow-up research studies about follow-up hybrid AI models to amplify further the role of AI in real-time monitoring and optimization of biogas production for sustainable bioenergy solutions may focus on large-scale industrial applications of such models (Blasi et al., 2023; Malik et al., 2020).