Deep Neural Network Model Based on Process Mechanism Applied to Predictive Control of Distillation Processes

Guardat en:

Dades bibliogràfiques
Publicat a:	Processes vol. 13, no. 3 (2025), p. 811
Autor principal:	Wang, Zirun
Altres autors:	Wang, Hao, Du, Zengzhi
Publicat:	MDPI AG
Matèries:	Toluene Accuracy Proportional integral derivative Benzene Optimization techniques Artificial neural networks Predictive control Data processing Dynamic models Prediction models Intelligent manufacturing systems Catalytic cracking Efficiency Machine learning Simulation Production controls Product quality Control algorithms Artificial intelligence Digital twins Neural networks Process controls Variables Distillation Methods Chemical engineering
Accés en línia:	Citation/Abstract Full Text + Graphics Full Text - PDF
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!

Descripció
Resum:	In modern process industries, precise process modeling plays a vital role in intelligent manufacturing. Nevertheless, both mechanistic and data-driven modeling methods have their own limitations. To address the shortcomings of these two modeling methods, we propose a neural network model based on process mechanism knowledge, aiming to enhance the prediction accuracy and interpretability of the model. The basic structure of this neural network consists of gated recurrent units and an attention mechanism. According to the different properties of the variables to be predicted, we propose an improved neural network with a distributed structure and residual connections, which enhances the interpretability of the neural network model. We use the proposed model to conduct dynamic modeling of a benzene–toluene distillation column. The mean squared error of the trained model is 0.0015, and the error is reduced by 77.2% compared with the pure RNN-based model. To verify the prediction ability of the proposed predictive model beyond the known dataset, we apply it to the predictive control of the distillation column. In two tests, it achieves results far superior to those of the PID control.
ISSN:	2227-9717
DOI:	10.3390/pr13030811
Font:	Materials Science Database