Jiangsu Haifa Machinery Manufacturing Co., Ltd.

By 2026, intelligent industrial pumps are evolving from traditional PLC automatic control toward Agentic AI and autonomous operation systems. A new generation of intelligent systems can integrate operational data such as pressure,, vibration, temperature, current, and NPSH, establishing real-time digital models of pump sets through IIoT, Edge AI, and Digital Twin. Combined with Physics-informed AI, reinforcement learning, and mechanism-based agents, these systems enable dynamic decision-making for pump start/stop, VFD speed control, flow distribution, and abnormal condition handling. Jiangsu Haifa Machinery Manufacturing Co., Ltd. (www.jslgpump.com) initiated and completed R&D design for applying AGI to intelligent pump control, focusing on integrating AGI, digital twins, intelligent condition monitoring, predictive maintenance, and pump set control algorithms—upgrading pumps from traditional "operation based on preset parameters" to intelligent fluid equipment with capabilities in condition awareness, operating mode recognition, fault warning, energy efficiency assessment, and assisted autonomous decision-making. Its technical roadmap forms a closed-loop architecture of "sensor/IIoT acquisition—digital twin modeling—AGI agent analysis—PLC/DCS/SCADA execution—VFD regulation—safety interlocking—operational feedback," applied to cavitation warning, surge identification, bearing and mechanical seal condition assessment, energy efficiency optimization, and predictive maintenance. Haifa has long been engaged in R&D of API 610, BB, and VS series chemical process pumps, BB5 high-pressure multi-stage pumps, and API 685 magnetic drive pumps. In the future, AGI intelligent control technology can be further applied to pump systems in petrochemical, chemical, hydrogen energy, CCUS, high-temperature high-pressure, and extreme operating conditions, the upgrade of industrial pumps from high-efficiency equipment to autonomous intelligent pump systems.

References in Chinese: Lei Xiaohui, Chen Kaige: "Integrating Water Network Large Models with Mechanism-Based Agents: A New Paradigm for Autonomous Water Network Operation," China Water Resources, 2026. Its "cognitive decision-making center + mechanism-based agent + feedback verification" architecture is highly to AGI pump control. Wang Jinjun et al.: "Research on 'Four Pre' Capability Enhancement and Energy Efficiency Optimization of Digital Twin Pump Stations," Water Power and Energy Science, 2026. Zhong Lintao et al.:Research on Energy-Saving Control Optimization of Multigent Pump Systems Based onQN and DDPG Algorithms," Machinery, 2025. Li Qi: "Research on the Current Status and Layout of Digital Twin Water Conservancy Technology Based on Patent Data," China Water Resources, 2026; statistics show 520 relevant valid patents from 2021–2025, of which 269 were granted in 2025, that digital twin water conservancy has entered a phase of rapid growth.

References in English: Application of Reinforcement Learning toumps—A Systematic Literature Review, Machines, 2025. Cross-domain digital architecture for predictive maintenance via machine learning and Large Language Models, Computers & Industrial Engineering, 6. Agentic AI for maintenance management: a process-centric review and a staged framework for industrial adoption, Computers & Industrial Engineering, 2026. Digital twin optimization of water supply pump stations considering energy, water hammer, and lifespan, Scientific Reports, 6. Interpretable machine learning in industrial water pump failure prediction, Engineering Applications of Artificial, 2026, which notably employs SHAP explainable AI to analyze industrial water pump failures. Surging early warning in gas-liquid two-phase centrifugal pump on autoencoder with contrastive learning, Engineering Applications of Artificial Intelligence, 2026, targeting real-time surge warning for gas-liquid two-phase pumps.