International Journal of Waste Resources

Department of Environmental Science, School of Environment and Earth Science, Pwani University, Kilifi, Kenya

Publications

• Research Article   
  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
  Author(s): 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 ov.. View more»
  

  Abstract PDF