Over the past few decades, several data‐driven methods have been developed for identifying a model that accurately describes the process dynamics. Lately, sparse identification of nonlinear dynamics (SINDy) has delivered promising results for various nonlinear processes. However, at any instance of plant‐model mismatch or process upset, retraining the model using SINDy is computationally expensive and cannot guarantee to catch up with rapidly changing dynamics. Hence, we propose operable adaptive sparse identification of systems (OASIS) framework that extends the capabilities of SINDy for accurate, automatic, and adaptive approximation of process models. First, we use SINDy to obtain multiple models from historical data for varying input settings. Next, using these models and their training data, we build a deep neural network that is incorporated in a model predictive control framework for closed‐loop operation. We demonstrate the OASIS methodology on the identification and control of a continuous stirred tank reactor.

中文翻译：

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系统的可操作自适应稀疏识别：在化学过程中的应用

在过去的几十年中，已经开发了多种数据驱动的方法来识别能够准确描述过程动态的模型。最近，对非线性动力学（SINDy）的稀疏识别已为各种非线性过程带来了可喜的结果。但是，在任何工厂模型不匹配或过程异常的情况下，使用SINDy重新训练模型在计算上都是昂贵的，并且不能保证赶上快速变化的动态。因此，我们提出了可操作的系统自适应稀疏识别（OASIS）框架，该框架扩展了SINDy的功能，以实现过程模型的准确，自动和自适应近似。首先，我们使用SINDy从历史数据中获取用于不同输入设置的多个模型。接下来，使用这些模型及其训练数据，我们构建了一个深层神经网络，该神经网络被纳入用于闭环操作的模型预测控制框架中。我们证明了OASIS方法论对连续搅拌釜反应器的识别和控制。