Annihilation of Pressure Surge in Medium Pressure Vessels Using the Derived Dynamic Filter Model

Pressure vessels in the energy industry, particularly medium-pressure systems for liquefied natural gas (LNG), are vulnerable to surge events—abrupt transient pressure fluctuations that threaten structural integrity, operational efficiency, and safety. Despite their critical role, existing approaches lack robust real-time monitoring and adaptive control strategies for effective surge mitigation. This study aims to leverage a derived dynamic filter model (Kalman filter) for accurate estimation and annihilation of pressure surges in medium-pressure vessels through integration of sensor data and system dynamics. The objectives were to integrate filtering techniques into surge analysis, investigate vessel-fluid dynamics under surge conditions, evaluate the derived filter's capabilities, and incorporate pressure sensor mechanisms for real-time control. Key results from MATLAB/Simulink simulations demonstrated effective surge tracking, with the Kalman filter reducing noisy measurements (±3 bar deviation) to estimation errors within ±1.5 bar, rapidly converging during 15% pressure spikes (e.g., 43.5 to 50 bar) and maintaining stable gains for quick recovery. In conclusion, the derived Kalman filter model provides a reliable tool for real-time pressure monitoring and surge mitigation, enhancing vessel safety and resilience. Implementation in offshore/onshore LNG operations is recommended to fortify energy production systems against transient pressures.