Artificial Intelligence–Based Energy Optimization Framework for Industrial Air Compressor Systems

Abstract

Industrial air compressor systems are among the major energy-consuming components in manufacturing and process industries, significantly contributing to operational costs and energy inefficiencies. Conventional compressor management approaches often operate under fixed control settings and limited monitoring capabilities, resulting in excessive energy consumption, pressure fluctuations, and reduced system performance. This research presents an artificial intelligence–based energy optimization framework for industrial air compressor systems to enhance operational efficiency, energy utilization, and intelligent process control. The proposed framework integrates artificial intelligence algorithms, machine learning techniques, IoT-enabled sensors, and real-time monitoring systems to continuously analyze compressor operating conditions, pressure demand, airflow characteristics, and energy consumption patterns. Intelligent predictive models are employed to optimize compressor scheduling, load balancing, pressure regulation, and maintenance planning based on dynamic industrial demand conditions. The system further incorporates anomaly detection and adaptive control mechanisms to identify inefficiencies, minimize idle running, and improve equipment reliability. Experimental and simulation-based evaluations are conducted using parameters such as energy consumption reduction, pressure stability, response time, operational efficiency, maintenance optimization, and system reliability. The results demonstrate that the proposed AI-driven optimization framework significantly reduces energy wastage, improves compressor performance, and enhances operational stability compared with conventional rule-based compressor control systems.