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

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Publicado en:Processes vol. 13, no. 3 (2025), p. 811
Autor principal: Wang, Zirun
Otros Autores: Wang, Hao, Du, Zengzhi
Publicado:
MDPI AG
Materias:
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
Acceso en línea:Citation/Abstract
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Resumen: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
Fuente:Materials Science Database