To equip data-driven dynamic chemical process models with strong interpretability, we develop a light attention–convolution–gate recurrent unit (LACG) architecture with three sub-modules—a basic module, a brand-new light attention module, and a residue module—that are specially designed to learn the general dynamic behavior, transient disturbances, and other input factors of chemical processes, respectively. Combined with a hyperparameter optimization framework, Optuna, the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process. The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models, including the feedforward neural network, convolution neural network, long short-term memory (LSTM), and attention-LSTM. Moreover, compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1, the LACG parameters are demonstrated to be interpretable, and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM. This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling, paving a route to intelligent manufacturing.

中文翻译：

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一种可解释的光注意-卷积-门循环单元架构，用于实际化学动态过程的高精度建模


为了使数据驱动的动态化学过程模型具有强大的可解释性，我们开发了一种光注意-卷积-门循环单元（LACG）架构，该架构具有三个子模块：一个基本模块、一个全新的光注意模块和一个残留模块——专门设计用于分别学习化学过程的一般动态行为、瞬态扰动和其他输入因素。结合超参数优化框架 Optuna，通过分布式控制系统数据驱动的建模实验对实际脱乙烷过程的排放流量进行了测试，测试了所提出的 LACG 的有效性。与其他模型（包括前馈神经网络、卷积神经网络、长短期记忆（LSTM）和注意力LSTM）相比，LACG模型在预测精度和模型泛化方面具有显着优势。此外，与使用Aspen Plus Dynamics V12.1建立的脱乙烷化模型的模拟结果相比，LACG参数被证明是可解释的，并且与传统的可解释模型相比，可以从模型参数中观察到更多关于变量相互作用的细节注意力-LSTM。这一贡献丰富了可解释的机器学习知识，为实际化工过程建模提供了一种可靠、高精度的方法，为智能制造铺平了道路。