Skip to content
Research Article Open access CC BY 4.0

Application of Artificial Neural Networks in Chemical Process Control

Haonan Wang, Yijia Chen

Asian Journal of Research in Computer Science · pp. 22–37 · Published 27 Jun 2022

10.9734/ajrcos/2022/v14i130325

Abstract

An important data-driven model is the artificial neural network. Artificial neural networks have been widely used in many domains of chemical processes due to its robustness, fault tolerance, self-adaptive capability, and self-learning ability. For the chemical process with nonlinearity and strong coupling, artificial neural networks can model and control the process well and make up for the lack of traditional PID control technology. As a result, ANN has emerged as a significant positive trend for chemical process control. In this paper, the principle, development history, and common structure of artificial neural networks are first outlined. Then the role of artificial neural networks in chemical process control is introduced in three aspects: improved PID control, improved model predictive control, and for hybrid models. The important effect of artificial neural networks in chemical process control is reflected by comparison. Finally, it is proposed that chemical process control can be more developed by applying more deep learning algorithms and developing multiple neural networks and hybrid models in chemical process control.

Artificial neural network process control chemical process model predictive control hybrid model

References (42)

  1. 1 Support-Vector Networks [DOI]
  2. 2 Learning representations by back-propagating errors [DOI]
  3. 3 Reducing the Dimensionality of Data with Neural Networks [DOI]
  4. 4 Neural networks and physical systems with emergent collective computational abilities. [DOI]
  5. 5 A logical calculus of the ideas immanent in nervous activity [DOI]
  6. 6 The perceptron: A probabilistic model for information storage and organization in the brain. [DOI]
  7. 7 Backpropagation through time: what it does and how to do it [DOI]
  8. 8 The Organization of Behavior: A Neuropsychological Theory [DOI]
  9. 9 Radial basis functions theory and implementations
  10. 10 Learning and Relearning in Boltzmann Machines [DOI]
  11. 11 Artificial neural network models of knowledge representation in chemical engineering [DOI]
  12. 12 Applications of artificial neural networks in chemical engineering [DOI]
  13. 13 Artificial neural networks: applications in chemical engineering [DOI]
  14. 14 Neural Networks and Deep Learning [DOI]
  15. 15 Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms [DOI]
  16. 16 Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks [DOI]
  17. 17 Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care [DOI]
  18. 18 Grey-box modelling and control of chemical processes [DOI]
  19. 19 Artificial Neural Networks: Theoretical Background and Pharmaceutical Applications: A Review [DOI]
  20. 20 On Clinical Event Prediction in Patient Treatment Trajectory Using Longitudinal Electronic Health Records [DOI]
  21. 21 Neuro fuzzy estimation of the most influential parameters for Kusum biodiesel performance [DOI]
  22. 22 Distributionally Robust Optimization [DOI]
  23. 23 An affine invariant approach for dense wide baseline image matching [DOI]
  24. 24 The Efficacy of Neural Networks in Predicting Returns on Stock and Bond Indices* [DOI]
  25. 25 Embedded PWM Predictive Control of DC-DC Power Converters Via Piecewise-Affine Neural Networks [DOI]
  26. 26 Simulation of artificial neural network controller for automatic generation control of hydro electric power system [DOI]
  27. 27 Application of Neural Network Algorithm in Propylene Distillation [DOI]
  28. 28 Application of Artificial Neural Network in Distillation System: A Critical Review of Recent Progress [DOI]
  29. 29 Review of Artificial Neural Network and Its Application Research in Distillation [DOI]
  30. 30 Using Artificial Intelligence in Chemistry and Biology: A Practical Guide
  31. 31 Analysis of the convergence properties of self-normalized source separation neural networks [DOI]
  32. 32 High-dimensional neural-network artificial intelligence capable of quick learning to recognize a new smell, and gradually expanding the database [DOI]
  33. 33 Research on Chemical Process Optimization Based on Artificial Neural Network Algorithm [DOI]
  34. 34 Optimization of Extraction Process Based on Neural Network [DOI]
  35. 35 Prediction of Chemical Production Based on Neural Network [DOI]
  36. 36 Combining First Principles with Black-Box Techniques for Reaction Systems [DOI]
  37. 37 Korean Journal of Chemical Engineering [DOI]
  38. 38 Journal of Engineering Research and Reports [DOI]
  39. 39 Asian Journal of Research in Computer Science [DOI]
  40. 40 Asian Journal of Applied Chemistry Research [DOI]
  41. 41 Distributionally Robust Games: f-Divergence and Learning [DOI]
  42. 42 A review of neural network algorithms and their applications in supercritical extraction [DOI]

Cited by 21

Research Progress of Artificial Neural Network and Its Application in Fault Diagnosis of Chemical Industry

Zhihui Zhao, Jiying Li · Journal of Engineering Research and Reports · 2022

Artificial Neural Network and Its Application in Image Recognition

Chunyu Wang, Lujie Wang · Journal of Engineering Research and Reports · 2023

An Unsupervised Fault Detection Model Enhanced by an Agentic LLM Layer for Industrial Alarm Management

A.Eren Vedin, Hasan Şıldır · 2025 13th International Conference on Control, Mechatronics and Automation (ICCMA) · 2025

Evolutionary artificial neural network for temperature control in a batch polymerization reactor

Francisco Javier Sánchez-Ruiz, Elizabeth Argüelles Hernandez, José Terrones-Salgado · Ingenius · 2023

Mathematical Modeling and Advanced Control of the Refinery Processes: A Review

Laith S. Mahmood, Khalid Alzobai, Salam K. Al-Dawery · Al-Nahrain Journal for Engineering Sciences · 2025

Artificial intelligence perspectives: A systematic literature review on modeling, control, and optimization of fluid catalytic cracking

Mustapha K. Khaldi, Mujahed Al-Dhaifallah, Othman Taha · Alexandria Engineering Journal · 2023

Hybrid meta-heuristics neural network-based PID tuning for robust and fault-tolerant control of integrating processes

Achu Govind KR · Transactions of the Institute of Measurement and Control · 2025

A Dual Neural Network Approach with PID Control and Reference Tracking to Enhance Oil Recovery

Keyvan Ahangar Darabi, Majid Ahmadlouydarab · Advanced Theory and Simulations · 2025

Showing 8 of 21 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

32

Page views

0

PDF downloads

3

Outbound clicks

21

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.