ARTIFICIAL INTELLIGENCE-SUPPORTED SMART SIMULATION OF BIOMASS CONVERSION WITH NANOSENSORS

<p class="MsoNormal" align="center" style="margin-left:5.4pt;text-align:center"><span lang="EN-US">This study presents a theoreticalframework for optimizing biomass energy conversion processes using ArtificialIntelligence (AI)-based deep learning (DL) and simulation methods. The researchmodels thermochemical (gasification) and biochemical (anaerobic fermentation)pathways using open-access datasets and computational tools, offering a data-drivenapproach without the need for physical laboratory setups. Building uponadvanced sensing and material principles from the field of Nanotechnology, thiswork aims to optimize biomass energy production from energy crops andagricultural waste (e.g., corn stalk) through theoretical simulations anddata-driven approaches.<o:p></o:p></span> <p class="MsoNormal" align="center" style="margin-left:5.4pt;text-align:center"><span lang="EN-US">The collected and pre-processeddatasets are analyzed using Convolutional Neural Networks (CNN) to performpredictive modeling of energy yield, gas compositions, and emission profiles.The AI-based models are applied to simulate various operating conditions andfeedstock variations, providing insights into potential efficiency gains andemission reductions achievable through process optimization.<o:p></o:p></span> <p class="MsoNormal" align="center" style="margin-left:5.4pt;text-align:center"><span lang="EN-US">This AI-supported optimization istargeted to increase energy efficiency by 20–30% and reduce CO2 emissions by15%. The project will utilize open-source datasets and simulation tools toevaluate Turkey’s annual agricultural waste potential of 50 million tons. Thus,cost analysis performed in the modeling environment will allow the optimizedresource management to ensure up to 25% environmental sustainability and offera roadmap for developing a data-driven model suitable for Turkey's specificbiomass conditions. The findings underscore the role of AI in supportingcleaner, more efficient, and environmentally conscious renewable energystrategies, contributing to both academic research and practical policydevelopment.<o:p></o:p></span> <p class="MsoNormal" align="left" style="margin-left: 5.4pt;"><b><span lang="EN-US">Keywords: </span></b><span lang="EN-US">biomassenergy, nanotechnology, artificial intelligence, deep learning, optimization.<o:p></o:p></span>