Operational Optimization Based on Model Adaptation for Compressed Air System

With the energy problem becoming more and more prominent, high energy consumption of the compressed air system (CAS) is widely concerned. Therefore, it is of great significance to reduce its energy waste and power consumption for energy saving and emission reduction. In this study, an online optimization method for CAS with the identification model and output modifier is proposed. Firstly, a new operational optimization method is proposed, which involves two modules, an Auto-Regressive with Extra Inputs (ARX) approach with an adaptive learning strategy and an adaptive optimization strategy with the output modifier adaptation (OMA). Moreover, a steady state identification strategy is employed for gradient optimization. Finally, a case of CAS in a steel industrial park is selected to verify the effectiveness of the proposed method. The simulation results show that the proposed method can significantly reduce the energy consumption of CAS.