ARTIFICIAL INTELLIGENCE-SUPPORTED SMART SIMULATION OF BIOMASS CONVERSION WITH NANOSENSORS

Creative Commons License

Kalay M.

5th International Thales Congress On Life, Engineering, Architecture and Mathematics, Cairo, Mısır, 16 - 18 Ekim 2025, ss.233-242, (Tam Metin Bildiri)

Özet

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.