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

Yin, Xiong; Wen, Kai; Yan, Weisheng; Han, Xu; Mukhtar, Yasir; Gong, Junbo

doi:10.1016/j.jngse.2021.104384

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…Natural gas pipelines and compressors, as critical components of natural gas pipeline systems, can significantly impact the gas supply capacity of the system when they fail stochastically [4,5]. However, the natural gas pipeline system operates as an open system, and significant difficulties are encountered in determining the repair times of units, which arise from uncertainties associated with unit failures, user demand, and resource availability [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Methodology to Determine the Maximum Allowable Repair Time for Critical Units of Natural Gas Pipeline Systems Using Gas Supply Reliability Theory

Xiangying,

Shan,

Wang

et al. 2024

Preprint

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As an essential part of the natural gas supply chain, the safe and reliable operation of the natural gas pipeline system is vital to energy supply security. When accidents occur in the natural gas pipeline system, determining the maximum allowable repair time of the failed unit is a crucial factor in balancing maintenance resources against risks of gas shortage. Therefore, this paper proposes a methodology to determine the maximum allowable repair time for critical units of natural gas pipeline systems using gas supply reliability theory, considering system uncertainties as well as the line-pack effect. The methodology is composed of three parts. Firstly, the supply reliability indicators are proposed from the perspective of gas shortage. Secondly, the gas supply reliability evaluation model for the natural gas pipeline system is developed considering system uncertainties and the line-pack effect. Finally, the maximum allowable repair time is determined based on gas supply reliability and target reliability. Furthermore, a real natural gas pipeline system located in China is applied to confirm the feasibility of the methodology. The results demonstrate that the proposed methodology can serve as part of a Reliability Centered on Maintenance (RCM) analysis and provide guidance to support maintenance managers in making data-driven decisions.

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Section: Introductionmentioning

confidence: 99%

A Methodology to Determine the Maximum Allowable Repair Time for Critical Units of Natural Gas Pipeline Systems Using Gas Supply Reliability Theory

Xiangying,

Shan,

Wang

et al. 2024

Preprint

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“…It can be calibrated by the standard meter method gas flow standard device. During calibration, it is necessary to compare the indication error between the gas flowmeter and the flow standard device at a specific flow point [1]. In the calibration process, the airtightness of the gas flow standard device is the core factor that affects the reliability of the calibration results [2].…”

Section: Introductionmentioning

confidence: 99%

Similarity evaluation method of single flow point energy consumption mapping based on Fréchet distance

Chen,

Lan,

Yan

et al. 2023

Meas. Sci. Technol.

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Aiming at the problem of inaccurate measurement results caused by factors such as gas leakage during the calibration of the standard meter gas flow standard device of the same type and small diameter, this paper proposes a similarity assessment method of single flow point energy consumption mapping based on Fréchet distance (FD). First, through the statistical analysis of the historical energy consumption data of gas flow standard device, a discrete standard energy consumption mapping is formed. Next, the energy consumption mapping to be tested is standardized and the similarity of the energy consumption mapping is determined by the binary matrix accelerated search of the FD between the energy consumption mapping to be tested and the standard energy consumption mapping. Finally, the similarity threshold is calculated by combining the standard deviation of the FD and the reliability of the gas flowmeter calibration process is evaluated accordingly. The test shows that the method in this paper can effectively determine the reliability of the gas flowmeters calibration results.

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“…Safizadeh [ 20 ] built an ANN model to improve ultrasonic flowmeter calibration accuracy at two points. Xiong Yin et al [ 21 ] built a hybrid AI model, which included a classic proportional integral differential (PID) controller and a genetic algorithm (GA) model, to optimize the verification process of a flowmeter; a radial-based artificial neural network was established [ 22 ] to improve the measurement accuracy of ultrasonic flowmeters and reduce the influence of flow field disturbances on measurement accuracy.…”

Section: Introductionmentioning

confidence: 99%

Research on a Hybrid Intelligent Method for Natural Gas Energy Metering

Dong

Song

He³

et al. 2023

Sensors

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In this paper, a Comprehensive Diagram Method (CDM) for a Multi-Layer Perceptron Neuron Network (MLPNN) is proposed to realize natural gas energy metering using temperature, pressure, and the speed of sound from an ultrasonic flowmeter. Training and testing of the MLPNN model were performed on the basis of 1003 real data points describing the compression factors (Z-factors) and calorific values of the three main components of natural gas in Sichuan province, China. Moreover, 20 days of real tests were conducted to verify the measurements’ accuracy and the adaptability of the new intelligent method. Based on the values of the Mean Relative Errors and the Root Mean Square errors for the learning and test errors calculated on the basis of the actual data, the best-quality MLP 3-5-1 network for the metering of Z-factors and the new CDM methods for the metering of calorific values were experimentally selected. The Bayesian regularized MLPNN (BR-MLPNN) 3-5-1 network showed that the Z-factors of natural gas have a maximum relative error of −0.44%, and the new CDM method revealed calorific values with a maximum relative error of 1.90%. In addition, three local tests revealed that the maximum relative error of the daily cumulative amount of natural gas energy was 2.39%.

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

Cited by 10 publications

References 29 publications

A Methodology to Determine the Maximum Allowable Repair Time for Critical Units of Natural Gas Pipeline Systems Using Gas Supply Reliability Theory

A Methodology to Determine the Maximum Allowable Repair Time for Critical Units of Natural Gas Pipeline Systems Using Gas Supply Reliability Theory

Similarity evaluation method of single flow point energy consumption mapping based on Fréchet distance

Research on a Hybrid Intelligent Method for Natural Gas Energy Metering

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