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Chance Constrained Model Predictive Control and Application to SAGD Process Open Access


Other title
Chance Constrained Model Predictive Control
Robust Optimization
Type of item
Degree grantor
University of Alberta
Author or creator
Shen, Wenhan
Supervisor and department
Huang, Biao (Chemical and Materials Engineering)
Li, Zukui (Chemical and Materials Engineering)
Forbes, Fraser (Chemical and Materials Engineering)
Examining committee member and department
Forbes, Fraser (Chemical and Materials Engineering)
Liu, Jinfeng (Chemical and Materials Engineering)
Han, Jie (Electrical and Computer Engineering)
Li, Zukui (Chemical and Materials Engineering)
Huang, Biao (Chemical and Materials Engineering)
Department of Chemical and Materials Engineering
Process Control
Date accepted
Graduation date
2017-06:Spring 2017
Master of Science
Degree level
Model Predictive Control (MPC) is widely applied in the process industry nowadays. Chemical processes are corrupted by all kinds of uncertainties, such as measurement noises, disturbances and parameter uncertainties. Without consideration of uncertainties, conventional MPC will cause various problems, for example, violation of constraints and sub-optimal results. Chance constrained MPC (CCMPC) is introduced to generate a safe and optimal control strategy to minimize the effect of uncertainties. In this thesis, two types of uncertainties in the state space model are considered: system noises and parameter uncertainties. Robust optimization (RO) approximation, a novel method dealing with joint chance constraints, is investigated to solve CCMPC problem. This method leads to results close to the true optimal and is not restricted to certain types of distribution. This work is further applied on the steam assisted gravity drainage (SAGD) process. Constraint violations are greatly reduced by using the RO method. For system noises, the RO method can be directly applied with the inclusion of uncertainty sets. The type of uncertainty set is selected based on the distribution. Two-layer optimization is proposed, one layer guarantees probability satisfaction and the other layer deals with optimizing the cost. Compared with traditional analytical methods, RO method is not limited to specific distribution and shows better performance in objective function. For parameter uncertainties, random variables are multiplied with each other, increasing the difficulty to solve the problem. Stochastic tubes help to get rid of multiplicative uncertainty by requiring the tubes to satisfy the constraints. The problem is solved by the RO based tube method. Exponential growth in computation time is avoided. It also guarantees recursive feasibility and stability but results in conservative solutions. Finally, the RO method is applied to CCMPC of the SAGD process. A state space model is obtained as a proxy model from the production data. The control strategy is calculated based on the proxy model. The developed control strategy is then applied to a reservoir simulator and gives satisfactory results. RO based CCMPC greatly reduces violations due to uncertainties. The SAGD performance is improved by avoiding operating close to critical conditions.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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