A study of model adaptation in iterative real-time optimization of processes with uncertainties
A study of model adaptation in iterative real-time optimization of processes with uncertainties
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DOI：
10.1016/j.compchemeng.2018.08.001
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发表时间：
2019-03
期刊：
影响因子：
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通讯作者：
Afaq Ahmad;Weihua Gao;S. Engell
中科院分区：
文献类型：
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作者：
Afaq Ahmad;Weihua Gao;S. Engell
在实时优化中，可以通过在基于模型的优化问题中添加偏差和梯度校正项来处理对象模型失配，以满足最优性的一阶必要条件。然而，由于这些校正项不能确保收敛时满足二阶最优条件，因此优化中使用的模型可能不充分。在迭代修正自适应的框架下，本文提出仅使用有效的模型参数更新来确保并加速收敛到过程最优。此外，本文表明模型充分性可以而且应该在模型参数自适应中明确强制执行。通过在补料分批反应器中最大化产物产量的模拟研究，我们证明了所提出的模型自适应程序计算模型参数，这使得具有改性剂自适应的迭代实时优化更快、更可靠地收敛到工厂最优值。
In real-time optimization, plant-model mismatch can be handled by adding bias and gradient correction terms to the model-based optimization problem in order to meet the first-order necessary conditions of optimality. However, since these correction terms do not ensure the satisfaction of the second-order condition of optimality upon convergence, the model that is used in the optimization can be inadequate. In the framework of iterative modifier-adaptation, this paper proposes to only use effective model parameter updates to ensure and to speed up the convergence to the process optimum. Additionally, this paper shows that model adequacy can and should be enforced explicitly in model parameter adaptation. By means of a simulation study of maximizing the product yield in a fed-batch reactor, we demonstrate that the proposed model adaptation procedure computes model parameters which make the iterative real-time optimization with modifier-adaptation converge faster and more reliably to the plant optimum.