An Inverse-Hybrid-Modeling Digital Twin System for Natural Gas Energy Metrology

LIU Bin; ZHONG Lu; FENG Quanyuan; CHEN Yihong

doi:10.11999/JEIT260289

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LIU Bin, ZHONG Lu, FENG Quanyuan, CHEN Yihong. An Inverse-Hybrid-Modeling Digital Twin System for Natural Gas Energy Metrology[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260289

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LIU Bin, ZHONG Lu, FENG Quanyuan, CHEN Yihong. An Inverse-Hybrid-Modeling Digital Twin System for Natural Gas Energy Metrology[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260289

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An Inverse-Hybrid-Modeling Digital Twin System for Natural Gas Energy Metrology

doi: 10.11999/JEIT260289 cstr: 32379.14.JEIT260289

LIU Bin^{1, 2},
ZHONG Lu²,
FENG Quanyuan^{1
,
,},
CHEN Yihong¹

1.
College of Information Science and Technology, Southwest Jiaotong University, Chengdu 611700, China
2.
Chengdu Qinchuan IoT Technology Co., Ltd., Chengdu 610100, China

Funds: The Project supported by the National Natural Science Foundation of China, (62531021), Sichuan Province Central Universities and Research Institutes Science and Technology Achievement Transformation Project, (2025ZHCG0008)

Received Date: 2026-03-16
Accepted Date: 2026-04-23
Rev Recd Date: 2026-04-22

Available Online: 2026-05-23

Abstract

Abstract

Objective Global natural gas consumption continues to increase at an average annual rate of 3.2%. A 0.1% reduction in energy measurement error can reduce trade disputes by approximately $750 million per year. Traditional studies mainly use indirect methods for energy measurement. Among these methods, chromatographic analysis and acoustic velocity correlation are the most widely used, but both have clear application limits. Chromatographic analysis has a low interference error, but it shows delayed dynamic response at high flow rates and limited dynamic calibration capability. It also has poor adaptability to multi-gas-source switching, requires manual calibration, and has high operation and maintenance costs. The lack of interoperability standards for energy networks further increases the difficulty of system integration. Acoustic velocity correlation provides a low-latency dynamic response for flow measurement, but it has a high interference error. This error may increase when the content of a single component changes, such as when the hydrogen content increases from 5% to 10%. The method may even fail under complex operating conditions, such as multi-gas-source mixing and dynamic pressure fluctuations. To address these issues, new mechanism-modeling-oriented methods have been developed. The two most representative directions are mechanism-modeling-driven methods and hybrid-modeling methods. Both methods combine multi-source data fusion with virtual-physical interaction to establish mechanism models that link flow rate, other parameters, and energy. These methods provide a new approach for accurate energy measurement, but new challenges remain. Mechanism-modeling-driven methods are usually based on static flow modeling using Computational Fluid Dynamics (CFD). However, their dynamic parameter updates are slow, with delays of more than 30 s. They also have difficulty adapting to real-time operating-condition changes, rely on large labeled datasets, and have limited interpretability. Hybrid-modeling methods still face unresolved problems in collaborative optimization across multiple modules. In addition, existing studies lack support from industrial-grade verification platforms. These limits restrict their ability to solve the dynamic response delay, parameter identification difficulty, excessive physical simplification, and weak interference resistance of traditional natural gas energy metrology methods under complex conditions. Based on recent progress in mechanism-modeling-driven and hybrid-modeling methods, this study proposes an inverse-hybrid-modeling-driven digital twin system. The system introduces a Variational AutoEncoder (VAE)-based operating-condition feature extraction algorithm and a Dynamic Bayesian Network (DBN)-based parameter calibration mechanism. It also uses a Variational Expectation-Maximization (VEM) algorithm for offline calibration. The proposed system aims to improve the accuracy, adaptability, and interference resistance of natural gas energy metrology under complex operating conditions. Methods A natural gas energy metrology digital twin system based on inverse hybrid modeling is proposed. The system is built on a three-tier “algorithm-system-scenario” architecture. It integrates calorific value, flow, and energy mechanism models with multi-source real-time data streams. The VAE is used for unsupervised mining of operating-condition features. A parameter self-correction loop is then constructed by combining the DBN with VEM-based system calibration. Industrial-grade devices, including ultrasonic flowmeters and gas chromatographs, are integrated to ensure real-time data transmission and closed-loop control. The system covers key operating conditions, including dynamic pressure fluctuations, hydrogen-blended gas mixtures, and multi-gas-source switching. This design ensures strong adaptability between the model and practical applications. The system was continuously verified for 25 weeks on a full-scale industrial-grade experimental platform. The results show an operational delay of ≤3.8 s, data transmission jitter of ≤0.5 s, average daily energy consumption per device of ≤1.2 kW·h, Mean Time Between Failures (MTBF) of ≥4 100 h, energy measurement error of ≤0.25%, calorific value error of ≤0.12%, and flow indication error of ≤0.2%. The system also meets security requirements through industrial Ethernet encryption and hierarchical access control. It provides engineering support for intelligent pipeline-network optimization and standardized integration. Results and Discussions First, a multi-level hybrid modeling framework is established. Modular hybrid modeling is achieved through the algorithm-system-scenario three-tier architecture. Numerical methods combined with data are more flexible than purely analytical models and can represent complex multiphysics systems with fewer lumped physical parameters. These parameters may change during energy measurement under mechanical, energy, and hydrodynamic effects. The VAE and DBN are used to deeply integrate mechanism models with real-time data. This reduces the parameter synchronization delay to 3.8 s and supports fluid-acoustic co-simulation and rapid response under complex operating conditions, such as hydrogen-blended natural gas. Second, an integrated algorithm for inverse hybrid modeling and system calibration is proposed. By incorporating the VAE, DBN, and VEM algorithm, the inverse hybrid modeling algorithm forms a self-supervised, adaptive intelligent system with an internal closed-loop operation. The VAE encoder compresses high-dimensional operating-condition data into low-dimensional feature vectors. This enables unsupervised feature extraction without large labeled datasets. Based on the learned internal data distribution, the VAE can also generate perturbed data similar to the input data. These data are used to simulate abnormal operating conditions and verify interference resistance. The DBN constructs a continuous “prior-evidence-posterior” iterative cycle to support system self-correction and adaptive response to operating-condition changes. The VEM algorithm compensates for systematic errors that are difficult for the DBN to capture, thereby overcoming the limits of traditional static models. Conclusions This study describes and validates a hybrid digital twin system that combines experimental data-driven methods with physical models. The system successfully simulates the physical characteristics of natural gas energy metrology. A full-scale test platform was constructed, and the main system parameters were validated using experimental measurement data and compared with industry benchmarks. Each independent module in the algorithm-system-scenario three-tier hybrid modeling architecture, including calorific value measurement, flow calculation, and energy conversion, was continuously verified for 25 weeks. The results confirm strong consistency between model predictions and actual measurements. On the natural gas energy metrology digital twin experimental platform, systematic validation was performed for three core functions: flow measurement under dynamic conditions, multi-component calorific value determination, and energy accumulation. The results show that the output of the digital twin model matches the physical device measurement data with an accuracy of more than 99.5%. Under complex operating conditions, such as pressure pulsations and hydrogen-blended gas mixtures, the system maintains the measurement error within 0.5%. This performance is better than that of traditional methods and meets the Class A accuracy requirements for natural gas measurement. By introducing a multi-tier hybrid modeling framework, this study addresses the parameter identification difficulty and excessive physical simplification of traditional natural gas energy metrology methods. The integration of the VAE, DBN, and VEM algorithm enables unsupervised feature extraction under complex operating conditions and adaptive calibration of model parameters. This reduces dependence on prior physical knowledge and large labeled datasets. The experimental results show that the proposed method maintains high precision and strong stability under complex scenarios, including pressure pulsations and hydrogen-blended gas mixtures, where traditional models have difficulty providing accurate descriptions.
- Natural gas energy metrology,
- Digital twin,
- Inverse hybrid modeling,
- Variational AutoEncoder (VAE),
- Dynamic bayesian network

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