Outlet Temperature Prediction and Hierarchical Energy-Saving Regulation for Air-Cooling Systems of Underground Gas Storage Injection Compressors

The air-cooling system for injection compressors in underground gas storage (UGS) facilities is critical to operational safety. Its control must balance outlet temperature safety with fan energy savings. However, traditional empirical methods struggle to balance these dual objectives, especially for parallel multi-unit operations where quantitative scheduling guidelines are lacking. To address this, we take the eight parallel air-cooling units at the Chongqing Xiangguosi UGS facility as the research object. We propose an integrated framework that covers multi-condition outlet temperature prediction, heat transfer performance evaluation under a unified baseline, and hierarchical fan speed control. First, an adaptive sample selection mechanism is established using joint evaluation criteria that include gradient uncertainty and diversity metrics. By selecting samples with high information density, a deep neural network (DNN) model is trained to predict the outlet temperature of the air-cooling system. Second, a two-dimensional operating-condition zoning system is constructed based on ambient temperature and natural gas volumetric flow rate. This prediction model is used to fill data gaps under various conditions, enabling a performance ranking of all units under the same baseline. This ranking provides a quantitative basis for optimal unit selection at the system level. Furthermore, for individual units, multi-condition fan speed sweep simulations are conducted using HTRI Xchanger Suite. These simulations establish the mapping between thermal margin and fan speed. Consequently, a hierarchical control strategy is developed that coordinates system-level scheduling with equipment-level speed regulation. This strategy covers both normal energy-saving scenarios and over-temperature emergency scenarios, while clearly defining engineering safety constraints for low-speed operation. The application results show that the proposed method significantly reduces fan energy consumption while ensuring process safety. It offers a valuable technical reference for intelligent and energy-efficient operation of similar multi-unit heat exchange systems.