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Enforcing Model Adequacy in Real-Time Optimization via Dedicated Parameter Adaptation ?

机译:通过专用参数自适应在实时优化中加强模型的充分性。

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Iterative real-time optimization schemes that employ modifier adaptation add bias and gradient correction terms to the model that is used for optimization. These affine corrections lead to meeting the first-order necessary conditions of optimalityof the plantdespite plant-model mismatch. However, since the added terms do not include curvature information, satisfaction of the second-order sufficient conditions of optimality is not guaranteed, and the model might be deemedinadequatefor optimization. In the context of modifier adaptation, this paper proposes to include a dedicated parameter-estimation step such that also the second-order optimality conditions are met at the plant optimum. In addition, we propose a procedure to select the best parameters to adapt based on a local sensitivity analysis. A simulation study dealing with product maximization in a fed-batch reactor demonstrates that the proposed scheme can both select the right parameters and determine their values such that modifier adaptation can drive the plant to optimality fast and without oscillations.
机译:采用修饰符自适应的迭代实时优化方案会向用于优化的模型添加偏差和梯度校正项。这些仿射校正导致满足植物尽管植物模型不匹配的最优性的一阶必要条件。但是,由于添加的项不包括曲率信息,因此不能保证满足二阶充分条件的最优性,并且该模型可能被认为不足以进行优化。在修改器适应的情况下,本文建议包括一个专用的参数估计步骤,以便在工厂最优时也满足二阶最优条件。此外,我们提出了一种程序,用于根据局部敏感性分析选择最佳参数以进行调整。对补料分批反应器中产品最大化进行的模拟研究表明,所提出的方案既可以选择正确的参数,又可以确定其值,从而使调节剂的适应性能够将设备快速优化至最佳状态而不会出现振荡。

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