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Online Model Maintenance in Real-time Optimization Methods
Computers & Chemical Engineering ( IF 3.9 ) Pub Date : 2020-10-27 , DOI: 10.1016/j.compchemeng.2020.107141
José Matias , Johannes Jäschke

The performance of model-based optimization methods, like Real-time Optimization (RTO), relies on the model accuracy and adequacy. However, features of the process may be unknown and/or the system behavior can drastically change with time (e.g. system degradation). Therefore, even if we have a perfect model in the beginning, we may end up making decisions based on a poor model. This paper proposes a method that adapts the model structure online, based on an available model set, while simultaneously estimates the model parameters. The problem is presented in a superstructure framework and solved using a mixed-integer nonlinear formulation. Then, the updated model is combined with Output Modifier Adaptation, an RTO variant, for economic optimization. Our method is tested in a continuous stirred-tank reactor and a gas lifted oil well network. The results show that we can select the correct model structure, update its parameters and, simultaneously, converge to the plant optimum.



中文翻译:

实时优化方法中的在线模型维护

基于模型的优化方法(如实时优化(RTO))的性能取决于模型的准确性和充分性。但是,该过程的特征可能是未知的,并且/或者系统行为可能随时间急剧变化(例如,系统降级)。因此,即使我们在一开始就拥有一个完美的模型,也可能最终会基于一个糟糕的模型做出决策。本文提出了一种基于可用模型集的在线适应模型结构,同时估计模型参数的方法。该问题在上层建筑框架中提出,并使用混合整数非线性公式解决。然后,将更新后的模型与RTO变体“输出修改器适配”结合使用,以实现经济优化。我们的方法在连续搅拌釜反应器和气举油井网络中进行了测试。

更新日期:2020-10-30
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