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.

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。