Integration of Industrial Internet of Things (IIoT) and Digital Twin Technology for Intelligent Multi-Loop Oil-and-Gas Process Control
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| Publicado en: | Machines vol. 13, no. 10 (2025), p. 940-968 |
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| Otros Autores: | , , , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Allahloh Ali Saleh |u Department of Electrical Engineering, Zakir Husain College of Engineering and Technology (ZHCET), Aligarh Muslim University, Aligarh 202002, India; msarfraz@zhcet.ac.in | |
| 245 | 1 | |a Integration of Industrial Internet of Things (IIoT) and Digital Twin Technology for Intelligent Multi-Loop Oil-and-Gas Process Control | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value (<inline-formula>μ</inline-formula>) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations. | |
| 653 | |a Nyquist plots | ||
| 653 | |a Proportional integral derivative | ||
| 653 | |a Routh-Hurwitz criterion | ||
| 653 | |a Visual Basic | ||
| 653 | |a Ethernet | ||
| 653 | |a Optimization | ||
| 653 | |a Predictive control | ||
| 653 | |a State estimation | ||
| 653 | |a Manufacturing | ||
| 653 | |a Dynamic models | ||
| 653 | |a Digital to analog converters | ||
| 653 | |a Kalman filters | ||
| 653 | |a Differential pressure | ||
| 653 | |a Fault diagnosis | ||
| 653 | |a Industrial Internet of Things | ||
| 653 | |a Man-machine interfaces | ||
| 653 | |a Digital twins | ||
| 653 | |a Sensors | ||
| 653 | |a Process controls | ||
| 653 | |a Structured singular values | ||
| 653 | |a Pneumatic control | ||
| 653 | |a Variables | ||
| 653 | |a Separators | ||
| 653 | |a Stability | ||
| 653 | |a Anomalies | ||
| 653 | |a Performance indices | ||
| 653 | |a Real time | ||
| 653 | |a HyperText Markup Language | ||
| 653 | |a Software | ||
| 700 | 1 | |a Sarfraz Mohammad |u Department of Electrical Engineering, Zakir Husain College of Engineering and Technology (ZHCET), Aligarh Muslim University, Aligarh 202002, India; msarfraz@zhcet.ac.in | |
| 700 | 1 | |a Ghaleb, Atef M |u Department of Industrial Engineering, College of Engineering &amp; Advanced Computing, Alfaisal University, Riyadh 11533, Saudi Arabia; aghaleb@alfaisal.edu | |
| 700 | 1 | |a Dabwan Abdulmajeed |u Industrial Engineering Department, College of Engineering, Taibah University, Al Madinah Al Munawwarah 42353, Saudi Arabia | |
| 700 | 1 | |a Ahmed, Adeeb A |u Department of Control Science and Engineering, School of Electro-Mechanical Engineering, Xidian University, Xi’an 710071, China | |
| 700 | 1 | |a Al-Shayea, Adel |u Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia | |
| 773 | 0 | |t Machines |g vol. 13, no. 10 (2025), p. 940-968 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3265918948/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3265918948/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3265918948/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |