AI-driven Digital Twin Framework for Predictive Maintenance, Asset Integrity Management, and Energy Optimization in Smart Oil and Gas Industrial Systems
Henry Inkum
Department Petroleum Engineering, University of Alaska Fairbanks, Alaska, United State.
Chidiebere Anastacia Ezeh
Department of Civil, Construction and Environmental Engineering, North Dakota State University, North Dakota, United States.
Vanessa Grant
Department of Business, Prairie View A&M University, School of Business, Texas, United States.
Urhiofe God'sfavour Oghenerabome
Department of Computer Engineering, University of Lagos, Lagos, Nigeria.
Lawal Adeshina Muhammed
Department of Civil Engineering, University of Ilorin, Ilorin, Nigeria.
Adedokun Abdulrahman Adegoke
Department of Mechanical Engineering, University of Ilorin, Ilorin, Nigeria.
Lawal Sulaimon Abiodun
Mechanical Engineering Department, Ladoke Akintola, University of Technology Ogbomoso, Oyo State, Nigeria.
Confidence Adimchi Chinonyerem *
Abia State Polytechnic, Abia State, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Advances in AI, physics-informed modelling, and Digital Twin technologies offer the potential to enhance predictive maintenance, asset-integrity assessment, and energy management within oil and gas processing systems. However, these frameworks are most useful when they are based on a common data source, use a consistent model configuration, and are tested under a defined set of operating conditions. This study presents and evaluates an AI-assisted, physics-informed DT for an integrated crude distillation unit and hydrotreating system based on a 180-day synthetic operating dataset. The framework integrates a bidirectional Long Short-Term Memory (BiLSTM) network for equipment-anomaly detection and remaining useful life (RUL) estimation, a Physics-Informed Neural Network (PINN) to predict the wall thickness of process piping, and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to optimise energy. During the independent test period, the precision and recall of the BiLSTM were 0.9654 and 0.9941, respectively, with a false-alarm rate of 3.85% and a RUL mean absolute error of 6.24 days. The RMSE and MAE for the independent evaluation period of the wall-thickness were 0.00585 mm and 0.00526 mm, respectively, for the PINN. It was found that a feasible operating condition could be obtained from NSGA-II using a load setpoint of 0.88 and a recycle fraction of 0.02. The Digital Twin strategy reduced simulated electricity consumption by 2.24% and fuel-energy consumption by 2.14%, equivalent to an estimated reduction of 452.67 t CO₂e over the 180-day simulation period when compared to the defined baseline. The results show that predictive maintenance, physics-informed asset-integrity prediction and energy optimisation can be integrated computationally within a single Digital Twin framework in a viable manner. The results do not constitute field validation, as the data are synthetic and the energy and environmental estimates are model-based. Further assessment using plant-specific sensor, maintenance, inspection, and energy-consumption data is needed before industrial implementation.
Keywords: Digital twin, predictive maintenance, BiLSTM, physics-informed neural network, NSGA-II, asset integrity, energy optimization, oil and gas processing, synthetic data