Development of a dynamic control and dispatching platform for natural gas pipeline emergency situations based on reinforcement learning

Natural gas pipeline network emergencies are frequent. Existing monitoring and dispatching systems are mostly static and rule-driven, making them difficult to adapt to changing environments and coupled risks and lacking self-learning and adaptive optimization capabilities. To address this, this paper constructs a dynamic control system for natural gas pipeline emergencies based on reinforcement learning. First, SCADA (Supervisory Control and Data Acquisition) and sensor data fusion are used to achieve multi-source state perception and establish a temporal state space suitable for training. A double deep Q-network intelligent decision engine is then introduced to learn stable policies through experience replay and a target network. A state-action adaptive mapping mechanism is designed to achieve intelligent adjustment of valves, pressure, and flow under different emergency levels. A multi-objective reward function is constructed by combining safety, timeliness, and energy consumption to achieve dynamic system balance. Finally, a visual control platform based on Python and TensorFlow is developed to complete the closed-loop optimization from data perception to policy execution. Experiments show that the average response time of the proposed method is only 0.96 s, a significant improvement over traditional method. After training, the valve control stability index reaches 0.98, and the adjustment time is shortened to 2.1 s. Under level 6 emergency conditions, the safety retention rate still reaches 91.7%, energy consumption is reduced by 13.2%, and the average reward under complex disturbances is 75.8, verifying its high efficiency and robustness in dynamic regulation.