Research on a Hybrid Intelligent Method for Natural Gas Energy Metering
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 b...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/14/6528 |
| _version_ | 1797587496750546944 |
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| author | Jingya Dong Bin Song Fei He Yingying Xu Qiang Wang Wanjun Li Peng Zhang |
| author_facet | Jingya Dong Bin Song Fei He Yingying Xu Qiang Wang Wanjun Li Peng Zhang |
| author_sort | Jingya Dong |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-11T00:39:46Z |
| format | Article |
| id | doaj.art-2631582596324f2192e299e393fb1ac0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:39:46Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2631582596324f2192e299e393fb1ac02023-11-18T21:18:57ZengMDPI AGSensors1424-82202023-07-012314652810.3390/s23146528Research on a Hybrid Intelligent Method for Natural Gas Energy MeteringJingya Dong0Bin Song1Fei He2Yingying Xu3Qiang Wang4Wanjun Li5Peng Zhang6Natural Gas Research Institute, PetroChina & Southwest Oil and Gas Field Company, Chengdu 610213, ChinaNatural Gas Research Institute, PetroChina & Southwest Oil and Gas Field Company, Chengdu 610213, ChinaNatural Gas Research Institute, PetroChina & Southwest Oil and Gas Field Company, Chengdu 610213, ChinaNatural Gas Research Institute, PetroChina & Southwest Oil and Gas Field Company, Chengdu 610213, ChinaNatural Gas Research Institute, PetroChina & Southwest Oil and Gas Field Company, Chengdu 610213, ChinaNatural Gas Research Institute, PetroChina & Southwest Oil and Gas Field Company, Chengdu 610213, ChinaSchool of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, ChinaIn 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%.https://www.mdpi.com/1424-8220/23/14/6528natural gasenergy meteringartificial neural networkultrasonicaccuracy |
| spellingShingle | Jingya Dong Bin Song Fei He Yingying Xu Qiang Wang Wanjun Li Peng Zhang Research on a Hybrid Intelligent Method for Natural Gas Energy Metering Sensors natural gas energy metering artificial neural network ultrasonic accuracy |
| title | Research on a Hybrid Intelligent Method for Natural Gas Energy Metering |
| title_full | Research on a Hybrid Intelligent Method for Natural Gas Energy Metering |
| title_fullStr | Research on a Hybrid Intelligent Method for Natural Gas Energy Metering |
| title_full_unstemmed | Research on a Hybrid Intelligent Method for Natural Gas Energy Metering |
| title_short | Research on a Hybrid Intelligent Method for Natural Gas Energy Metering |
| title_sort | research on a hybrid intelligent method for natural gas energy metering |
| topic | natural gas energy metering artificial neural network ultrasonic accuracy |
| url | https://www.mdpi.com/1424-8220/23/14/6528 |
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