A machine learning-based surrogate model for the rapid control of piping flow: Application to a natural gas flowmeter calibration system

Abstract

With the notion of intelligent pipelines gaining ground in natural gas pipe networks, the requirements for fully automated daily operations are increasing. The flow control problem, especially in the complex station-level process piping network (SLPPN), has become the main challenge. This paper proposed a machine learning-based surrogate model that used the precise SLPPN flow control. A hybrid modeling method that uses data-driven method to surrogate numerical model was adopted to reveal the flow characteristics in the SLPPN, retaining the accuracy of the numerical simulation model while increasing its calculation speed simultaneously. Then, a two-step controller combining open-loop optimal control and closed-loop feedback control was designed to replace the classic proportional-integral-differential (PID) controller. First, genetic algorithms (GA) were used to determine the optimal control scheme of the surrogate model for fast simulation. Second, the follow-up PID controller was employed to compensate for the gap between the setpoint and the flow after the first control step. The controller was verified using the actual calibration station of a natural gas flowmeter in Guangzhou, China. The results showed that this two-step control method based on the surrogate model outperformed the conventional PID control technique regarding control duration and time, highlighting its potential in automating and intellectualizing the natural gas transportation industry.