Artificial Intelligence for Energy Efficiency and Emission Reduction in Industrial Systems: Insights from High-Pressure Air Audits, Flue Gas Treatment, And Solar Plant Operations

Industrial energy systems are central to global energy consumption and environmental emissions. Artificial Intelligence (AI) now provides new pathways for optimizing energy use, automating diagnostics, reducing emissions, and enhancing sustainability across industrial environments. This study presents a comprehensive analysis of AI-driven energy efficiency methods applied to three key industrial domains: (1) high-pressure compressed air systems, (2) flue gas desulphurization (FGD) via calcium carbonate injection in industrial boilers, and (3) solar photovoltaic (PV) plant operation in arid regions. The research integrates practical experience from energy audits conducted at Carbon Corporation (now Graphite India Ltd.), operational improvements implemented at 300MW power plant., and commissioning insights from a solar PV plant in Bikaner, Rajasthan. The study introduces an AI-based real-time monitoring and optimization framework using machine learning models, predictive maintenance algorithms, and IoT-enabled data acquisition layers. Results demonstrate that integrating AI-driven diagnostics can reduce compressed air leakage losses from 28% to as low as 11%, enhance sulphur reduction efficiency in flue gas by 10–15%, and increase solar plant performance by 6–10% under harsh environmental conditions. This paper proposes a unified AI framework suitable for diverse industrial applications and highlights pathways for future adoption.