International Journal of Waste Resources

Abstract

Extended Convolutional Neural Networks for Synergistic Optimization of Biogas Production Integrating Multi-Stage Pre-treatment and Trace Element Supplementation for Enhanced Methane Yield from Lignocellulosic Feedstocks

Srinivas Kasulla, S J Malik, Asadi Srinivasulu, Fredrick Kayusi and Salman Zafar

Biogas production from lignocellulosic feedstocks presents a promising pathway for sustainable renewable energy generation. However, the structural complexity of lignocellulosic biomass and the demand for effective pretreatment methods remain significant obstacles. This research proposes an Extended Convolutional Neural Network (ECNN) framework to optimize biogas production synergistically. The methodology incorporates multi-stage pretreatment techniques and trace element supplementation to improve methane yield. The research leverages a comprehensive dataset that includes parameters such as lignin content, pretreatment methods, trace element supplementation, methane yield, biogas yield, pH level, temperature, enzyme addition, and COD reduction. The ECNN model analyzes these inputs with exceptional precision, achieving 100% prediction accuracy and a validation loss reduced to 0.006 over 100 epochs. The model effectively determines the optimal combinations of pretreatment methods and supplementation strategies to maximize biogas output. Furthermore, the research emphasizes the importance of multi-stage acid, enzymatic, and thermal pretreatments in deconstructing lignocellulosic structures, thereby enhancing microbial accessibility. The addition of trace elements like iron and cobalt further boosts anaerobic digestion performance. The ECNN framework capitalizes on these innovations, serving as a powerful predictive tool for biogas optimization and demonstrating scalability for various feedstocks. Future research will aim to broaden the dataset to incorporate real-world conditions, explore additional pretreatment methods, and enhance the ECNN model's adaptability to diverse feedstock compositions. This work introduces a novel, data-driven approach to optimizing biogas production, providing valuable contributions to the renewable energy sector and advancing the field of sustainable energy research.

Published Date: 2025-12-22; Received Date: 2024-11-30