Machine learning techniques have demonstrated their capability in capturing dynamic behavior of complex, nonlinear chemical processes from operational data. As full state measurements may be unavailable in chemical plants, this work proposes two machine-learning-based state estimation approaches. The first approach integrates recurrent neural networks (RNN) within the extended Luenberger observer framework to develop data-based state estimators. The second approach utilizes a hybrid model that integrates feed-forward neural networks with first-principles models to capture process dynamics in the state estimator. Then, an output feedback model predictive controller is designed based on the state estimates provided by the machine-learning-based estimators to stabilize the closed-loop system at the steady-state. A chemical process example is utilized to illustrate the effectiveness of the proposed machine-learning-based state estimation and control approaches.

机器学习技术已证明其具有从操作数据中捕获复杂的非线性化学过程的动态行为的能力。由于化工厂可能无法获得完整的状态测量，因此这项工作提出了两种基于机器学习的状态估计方法。第一种方法是在扩展的Luenberger观察器框架内集成递归神经网络（RNN），以开发基于数据的状态估计器。第二种方法利用混合模型，该模型将前馈神经网络与第一原理模型集成在一起，以捕获状态估计器中的过程动态。然后，基于由基于机器学习的估计器提供的状态估计来设计输出反馈模型预测控制器，以将闭环系统稳定在稳态。