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Generalized Parameter Estimation Method for Model-Based Real Time Optimization
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AI
DOI：10.1016/j.ces.2022.117754.png)
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摘要
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Real-time optimization is the technology to find the optimal setpoint of a plant under uncertainty. Iterative two-step approach with alternating parameter estimation and optimization is popular and intuitive in practice. However, this approach does not guarantee convergence if structural plant-model mismatches exist. Using the modifier adaptation principle, we improve the iterative two-step approach by the proposed generalized parameter estimation method. The flexibility and estimation capability of the generalized parameters enable optimality in the presence of structural plant-model mismatches. The synergy between model parameters and modifiers is visualized and demonstrated by three examples. The convergence of the proposed algorithm and its relation with existing work are analyzed theoretically. Simulation shows that the proposed method converges to a plant optimum in the presence of structural plant-model mismatch in a relatively noise-insensitive way, and the model parameters are properly adapted during the iteration.(c) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Real-time optimization
Generalized parameter estimation
Modifier adaptation
Plant-model mismatch
Iterative two-step approach
期刊
IF:
4.3
论文数:
2.2W
被引数:
5.5W
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