Non-linear control of a fuel gas blending benchmark problem with added consumer dynamics

• Published in: Journal of process control Vol. 154; p. 103527
• Main Authors: Sibiya, M.D., Wiid, A.J., le Roux, J.D., Craig, I.K.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2025
• Subjects: Fuel gas blending; Model–plant mismatch; Moving horizon estimator; Non-linear model predictive control; Moving horizon estimator; Non-linear model predictive control; Fuel gas blending; Model–plant mismatch
• ISSN: 0959-1524
• DOI: 10.1016/j.jprocont.2025.103527

This paper contributes to existing literature on fuel gas control by providing a feasible control solution with improved economic performance for an existing fuel gas control benchmark problem. Improved economic performance is achieved by implementing a non-linear model predictive controller (NMPC) that uses state estimates provided by a moving horizon estimator (MHE) and extended Kalman filter (EKF) for the fuel gas composition and flame speed index (FSI) to provide continuous inputs for the controller. Furthermore, the original fuel gas benchmark model is expanded to include consumer dynamics affecting fuel gas demand due to changes in the fuel gas heating value, making the model more representative of real industrial plants. The behaviour of an NMPC that neglects consumer dynamics (NMPC1) was compared against an NMPC that includes consumer dynamics (NMPC2). The aim of the benchmark problem is to reduce the time-weighted average cost of fuel gas for three 46-hour cases, accounting for purchase costs and penalties for fuel gas specification violations. An optimal cost for each case is determined assuming ideal conditions and perfect control. The benchmark controller is a conventional multi-loop feedforward/feedback system and has an average cost for the three cases which is 38.5% higher than the optimal cost. The NMPC1 controller has an average cost which is 33.9% higher than the optimal cost and better than the benchmark controller. A new benchmark scenario was developed which includes the consumer dynamics. For the new scenario, NMPC1 could not find a feasible solution, resulting in oscillations and specification violations. The oscillations would result in site-wide instabilities for all equipment using fuel gas. NMPC2 was able to keep the process stable during these scenarios and maintain all specifications. This shows the necessity to include consumer dynamics for effective fuel gas blending control. •Show improved economic performance for an existing fuel gas control benchmark problem.•Provide continuous estimates of fuel gas composition and flame speed index to an NMPC.•Consumer dynamics are included, making the model more representative of industry.•An NMPC that neglects consumer dynamics is compared against one that does not.•Show the necessity to include consumer dynamics for effective fuel gas blending control.