Prediction of Surge Control Valve Opening for Centrifugal Compressors in Natural Gas Pipelines Based on GWO-Optimized BP Neural Network
Year: 2026
Type: article
Abstract: The centrifugal compressor is the heart that drives the operation of natural gas pipeline systems. Under low-throughput conditions, natural gas often returns back to the compressor through the surge control valve to increase the flow rate and avoid surge. However, how to reasonably determine the opening of the surge control valve is still an important problem in production. To predict the opening of the surge control valve in a centrifugal compressor, this work proposes a BP neural network optimized by the grey wolf optimizer (GWO). Five key parameters, including compressor shell vibration, power turbine speed, compressor inlet pressure, compressor outlet temperature, and gas turbine power, are determined to be key factors correlated to the opening of the surge control valve, and the relationships of these parameters are physically analyzed from a physical perspective. Compared with the other five parallel models, the GWO–BP method effectively optimizes the initial weights and thresholds of the neural network, reduces the probability of falling into a local optimum, and significantly improves prediction accuracy and stability. The root mean square error (RMSE), determination coefficient (R-square), and mean absolute error (MAE) of the GWO–BP model are all the best fit, and the predicted and actual openings of the surge control valve match well, with the average relative deviation being 4.65%, indicating that the GWO–BP model proposed in this paper has a good ability to predict the opening of surge control valves.
Source: Algorithms
DOI: 10.3390/a19040271
Authors: Qingfeng Sun, Jinxin Tang, Weidong Li, Xingguang Wu
Institution: Fuzhou University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7147591080