(untitled) (PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE KYRGYZ REPUBLIC, 2026)
The integration of Artificial Intelligence (AI) into Coal-Water Fuel (CWF) production is revolutionizing the control of key parameters, such as coal concentration (0–30%), ensuring CWF stability and energy efficiency. Traditional CWF synthesis methods suffer from insufficient accuracy and prolonged adaptation times to raw material changes, limiting their industrial applicability. This article presents an advanced algorithm combining transformer neural networks, quantum optimization, and hybrid physicochemical models, specifically designed for the precise control of coal particle concentration and dispersity. We detail a multi-level AI control architecture, including a Vision Transformer (ViT) for particle morphology analysis, Deep Reinforcement Learning (DRL) for accurate blending, and Generative Adversarial Networks (GANs) for granulometry optimization. The implementation of this system allows for a 29% reduction in energy consumption and a 31% reduction in CWF production cost, demonstrating its high relevance for industrial deployment and the transition toward a carbon-neutral energy sector.