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Research Article Open access CC BY 4.0

Review of Neural Network Algorithm and its Application in Temperature Control of Distillation Tower

Ningrui Zhao, Jinwei Lu

Journal of Engineering Research and Reports · pp. 50–61 · Published 22 Mar 2021

10.9734/jerr/2021/v20i417294

Abstract

Distillation process is a complex process of conduction, mass transfer and heat conduction, which is mainly manifested as follows: The mechanism is complex and changeable with uncertainty; the process is multivariate and strong coupling; the system is nonlinear, hysteresis and time-varying. Therefore, traditional control methods are difficult to accurately control, but neural networks can greatly improve this problem. This article introduces the basic concepts of distillation tower temperature control, comprehensively introduces the application of various neural network algorithms in distillation tower temperature control, and compares their advantages and disadvantages and their effect. At present, there are many researches on neural network control of distillation tower temperature. The methods are different and each has its own merits. This article has carried out a systematic review to provide reference for the development of related industries.

Neural networks control process distillation tower temperature parameter

References (16)

  1. 1 Particle swarm optimization [DOI]
  2. 2 Neural Network Design
  3. 3 A novel genetic algorithm based on immunity [DOI]
  4. 4 Neural-network predictive control for nonlinear dynamic systems with time-delay [DOI]
  5. 5 Improved accelerated PSO algorithm for mechanical engineering optimization problems [DOI]
  6. 6 Adaptive fuzzy control of a class of SISO nonaffine nonlinear systems [DOI]
  7. 7 A New Particle Swarm Optimization Method Enhanced With a Periodic Mutation Strategy and Neural Networks [DOI]
  8. 8 Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild [DOI]
  9. 9 The use of particle swarm optimization for dynamical analysis in chemical processes [DOI]
  10. 10 Decentralized PID neural network control for five degree-of-freedom active magneticbearing [DOI]
  11. 11 Distributionally Robust Optimization [DOI]
  12. 12 A Three-Layer Hybrid Model for Wind Power Prediction [DOI]
  13. 13 Distributionally Robust Games [DOI]
  14. 14 Medical Self-Diagnostic System Using Artificial Neural Networks [DOI]
  15. 15 Application of self tuning PID controller based on RBF network [DOI]
  16. 16 A design of model driven cascade PID controllers using a neural network [DOI]

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