Methodology for nonlinear predictive control of oil and gas treatment complexes
DOI: 10.21293/1818-0442-2026-29-2-129-134
Abstract: Relevance. The nonlinearity, inertia, and cross-couplings inherent in oil and gas treatment processes, combined with strict technological constraints, reduce the efficiency of conventional automatic control systems and necessitate the transition to predictive control algorithms based on digital twins. Purpose of the study. Development of a methodology for nonlinear predictive control of oil and gas treatment complexes based on a digital twin implemented in the domestic MARS simulation environment. Methods. Parametric identification is performed using retrospective SCADA data via gradient correction; linearization at the operating point is carried out using Jacobian matrices followed by discretization. The optimal control problem is reduced to quadratic programming on a receding horizon. Stability, controllability, and observability of the closed-loop system are analyzed. Model verification is conducted against nameplate data and through numerical experiments in MATLAB. Scientific novelty. For the first time, a methodology is proposed for integrating a digital twin built by the component chain method into the predictive control loop, which accounts for the specifics of identification based on industrial SCADA data, explicit technological constraints, and Lyapunov stability verification. An algorithm is developed for converting a nonlinear differential-algebraic model into a quadratic optimization problem solvable in real time. Results. The adequacy of the heat exchanger digital twin has been confirmed: the deviation of the calculated steady-state characteristics from the nameplate values does not exceed 13.5%, and the transient responses agree with the verification computation performed in MATLAB. The correctness of the linearization and discretization procedures is substantiated by verifying the Kalman controllability and observability criteria and by solving the Riccati equation. The problem statement for constrained receding-horizon predictive control has been formulated. Practical significance. The methodology is intended for implementation in the industrial control systems (ICS) of oil and gas field treatment facilities. Its application is expected to enhance energy efficiency and control reliability, reduce the oscillations of process parameters, and can be used in the design of advanced control systems for heat exchange equipment.
Keywords: predictive control, simulation modeling, digital twin, MARS simulation environment, component chain method, oil and gas treatment, nonlinear systems, optimization, heat exchangers, industrial control systems
For citation:
Zaharov M. E., Chernyshov M. N., Dmitriev V. M., Zaharov K. E., Uliev L. M. Methodology for nonlinear predictive control of oil and gas treatment complexes. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2026, vol. 29, no. 2, pp. 129–134. DOI: 10.21293/1818-0442-2026-29-2-129-134
Authors and copyright holders:
- Zaharov M. E. , Tomsk State University of Control Systems and Radioelectronics (TUSUR), LLC "RN-Upstream Design
- Chernyshov M. N. , LLC «RN-Upstream Design», National Research Tomsk Polytechnic University (TPU)
- Dmitriev V. M. , Tomsk State University of Control Systems and Radioelectronics (TUSUR)
- Zaharov K. E. , National Research Tomsk Polytechnic University (TPU)
- Uliev L. M. , National Research Tomsk Polytechnic University (TPU)
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