Research Information System
5th International Thales Congress On Life, Engineering, Architecture and Mathematics, Cairo, Egypt, 16 - 18 October 2025, pp.233-242, (Full Text)
This study presents a theoretical
framework for optimizing biomass energy conversion processes using Artificial
Intelligence (AI)-based deep learning (DL) and simulation methods. The research
models thermochemical (gasification) and biochemical (anaerobic fermentation)
pathways using open-access datasets and computational tools, offering a data-driven
approach without the need for physical laboratory setups. Building upon
advanced sensing and material principles from the field of Nanotechnology, this
work aims to optimize biomass energy production from energy crops and
agricultural waste (e.g., corn stalk) through theoretical simulations and
data-driven approaches.
The collected and pre-processed
datasets are analyzed using Convolutional Neural Networks (CNN) to perform
predictive modeling of energy yield, gas compositions, and emission profiles.
The AI-based models are applied to simulate various operating conditions and
feedstock variations, providing insights into potential efficiency gains and
emission reductions achievable through process optimization.
This AI-supported optimization is
targeted to increase energy efficiency by 20–30% and reduce CO2 emissions by
15%. The project will utilize open-source datasets and simulation tools to
evaluate Turkey’s annual agricultural waste potential of 50 million tons. Thus,
cost analysis performed in the modeling environment will allow the optimized
resource management to ensure up to 25% environmental sustainability and offer
a roadmap for developing a data-driven model suitable for Turkey's specific
biomass conditions. The findings underscore the role of AI in supporting
cleaner, more efficient, and environmentally conscious renewable energy
strategies, contributing to both academic research and practical policy
development.
Keywords: biomass
energy, nanotechnology, artificial intelligence, deep learning, optimization.