AI-POWERED FEEDSTOCK OPTIMIZATION IN SUSTAINABLE AVIATION FUEL PRODUCTION: ENHANCING EFFICIENCY AND REDUCING COSTS
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Abstract
This research investigates the integration of advanced artificial intelligence (AI) techniques for optimizing feedstock combinations in Sustainable Aviation Fuel (SAF) production. The study employs a multi-layered model incorporating machine learning (ML), deep learning (DL), and reinforcement learning (RL) to enhance fuel conversion efficiency, reduce greenhouse gas (GHG) emissions, and lower production costs. Initial data collection from diverse sources, including AWS Data Exchange, Elsevier's Data API, and NASA AIRS, informs the model's development. Key machine learning algorithms such as Random Forest and Support Vector Machines (SVM) are utilized to predict energy yield and emissions, while CNNs and RNNs analyze complex relationships and time-series data for feedstock availability. The RL component dynamically optimizes feedstock blends in real-time, significantly improving operational efficiency. The model demonstrates an increase in energy output by 15-20%, a reduction in production costs by 15-25%, and a decrease in GHG emissions by 10-15%. The findings highlight the potential of AI-driven approaches to transform SAF production, ensuring both economic viability and environmental sustainability. This research contributes to the growing body of knowledge on sustainable energy solutions and offers a scalable framework for future biofuel optimization efforts
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