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Air compressors are widely used in industrial fields. Compressed air systems aggregate air flows and then supply them to places of demand. These huge systems consume a significant amount of energy and generate heat internally. Machine components in compressed air systems are vulnerable to heat, and, in particular, a radiator to cool the heat of the overall air compressor is the core component. Dirty radiators increase energy consumption due to anomalous cooling. To reduce the energy consumption of air compressors, this mechanism emphasizes a machine learning-based radiator fault detection, using features such as RPM, motor power, outlet pressure, air flow, water pump power, and outlet temperature with slight true fault labels. Moreover, the proposed system adds an LSTM-based motor power prediction model to point out the initial judgment of radiator fault possibility. Via the rigorous analysis and the comparison among machine learning models, this meticulous approach improves the performance of radiator fault prediction up to 93.0%, and decreases the mean power consumption of the air compressor around 2.24%. [ABSTRACT FROM AUTHOR]

The air compressor holds paramount importance due to its significant energy consumption when compared to other Balance of Plant components of polymer electrolyte membrane (PEM) fuel cells. The air supply system, in turn, plays a critical role in ensuring the stable and efficient operation of the entire fuel cell system. To enhance system efficiency, the impact of varying the stoichiometric ratio of air and air pressure was observed. This investigation was carried out under real loading conditions, replicating the conditions experienced by the power module when fuel cells are in use within a forklift. The air compressor can be operated at different pressure and excess air ratios, which in turn influence both the fuel cell's performance and the overall efficiency of the power module system. Our research focused on assessing the performance of PEM fuel cells under different load cycles, adhering to the VDI60 requirements for forklift applications. This comprehensive examination encompassed the system's minimum and maximum load scenarios, with the primary goal of optimizing excess air and pressure ratio parameters, especially under dynamic load conditions. The results revealed that higher air pressures and lower excess air ratios were conducive to increasing system efficiency, shedding light on potential avenues for enhancing the performance of PEM fuel cell systems in forklift applications. [ABSTRACT FROM AUTHOR]