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Research Article Open access CC BY 4.0

Review of Neural Network Algorithm and Its Application in Reactive Distillation

Huihui Wang, Ruyang Mo

Asian Journal of Chemical Sciences · pp. 20–29 · Published 8 Mar 2021

10.9734/ajocs/2021/v9i319073

Abstract

Artificial Neural Networks (ANN) can accurately identify and learn the potential relationship between input and output, and have self-learning capabilities and high fault tolerance, which can be used to predict or optimize the performance of complex systems. Reactive distillation integrates reaction and rectification into one device, so that the two processes occur at the same time and at the same place, but at the same time it also produces highly nonlinear robust behavior, making its process control and optimization unable to use conventional methods. Instead, neural network algorithms must be used. This paper briefly describes the research progress of neural network algorithms and reactive distillation technology, and summarizes the application of neural network algorithms in reactive distillation, aiming to provide reference for the development and innovation of industry technology.

Reactive distillation neural network algorithms BP neural network RBF neural network

References (47)

  1. 1 Chemical Engineering Research and Design [DOI]
  2. 2 Application of neural network algorithm in fault diagnosis of mechanical intelligence [DOI]
  3. 3 Predicting the helpfulness of online reviews using multilayer perceptron neural networks [DOI]
  4. 4 Application of genetic algorithm and BP neural network in supply chain finance under information sharing [DOI]
  5. 5 Artificial neural networks in microgrids: A review [DOI]
  6. 6 Reactive distillation: A review of optimal design using deterministic and stochastic techniques [DOI]
  7. 7 Predicting the citation counts of individual papers via a BP neural network [DOI]
  8. 8 Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks [DOI]
  9. 9 Reactive and membrane-assisted distillation: Recent developments and perspective [DOI]
  10. 10 Development of soft sensor for neural network based control of distillation column [DOI]
  11. 11 Advancement from neural networks to deep learning in software effort estimation: Perspective of two decades [DOI]
  12. 12 Application of an artificial neural network in predicting the effectiveness of trapping mechanisms on CO2 sequestration in saline aquifers [DOI]
  13. 13 Nonparallel Emotional Speech Conversion [DOI]
  14. 14 Prediction of output power with artificial neural network using extended datasets for Stirling engines [DOI]
  15. 15 Glycerol ethers synthesis from glycerol etherification with tert-butyl alcohol in reactive distillation [DOI]
  16. 16 Energy efficiency optimisation for distillation column using artificial neural network models [DOI]
  17. 17 Synthesis of dimethyl carbonate and propylene glycol in a pilot-scale reactive distillation column: Experimental investigation, modeling and process analysis [DOI]
  18. 18 Model predictive control and neural network predictive control of TAME reactive distillation column [DOI]
  19. 19 Accurate and robust neural networks for face morphing attack detection [DOI]
  20. 20 Hybrid artificial neural network based on BP-PLSR and its application in development of soft sensors [DOI]
  21. 21 Design and control of reactive dividing-wall column for the production of methyl acetate [DOI]
  22. 22 Optimisation of alkene epoxidation catalysed by polymer supported Mo(VI) complexes and application of artificial neural network for the prediction of catalytic performances [DOI]
  23. 23 Application of artificial neural networks instead of the orifice plate discharge coefficient [DOI]
  24. 24 An Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detection [DOI]
  25. 25 Distributed Mean-Field-Type Filters for Big Data Assimilation [DOI]
  26. 26 Distributionally Robust Optimization [DOI]
  27. 27 An affine invariant approach for dense wide baseline image matching [DOI]
  28. 28 A Three-Layer Hybrid Model for Wind Power Prediction [DOI]
  29. 29 Design of a Process for Production of Isopropyl Alcohol by Hydration of Propylene in a Catalytic Distillation Column [DOI]
  30. 30 Isobutyl acetate by reactive distillation. Part III. Conceptual design, simulation and optimization [DOI]
  31. 31 Distributed mean-field-type filter for vehicle tracking [DOI]
  32. 32 Distributionally Robust Games [DOI]
  33. 33 Nonequilibrium modelling of reactive distillation: Multiple steady states in MTBE synthesis [DOI]
  34. 34 Application of the artificial neural network for identification of polymers based on their X-ray diffraction curves [DOI]
  35. 35 Simulation and energy consumption evaluation of reactive distillation process for ethanolamine production [DOI]
  36. 36 Application Methods for Genetic Algorithms for the Search of Feed Positions in the Design of a Reactive Distillation Process [DOI]
  37. 37 Energy-saving performance of reactive distillation process for TAME synthesis through multiple steady state conditions [DOI]
  38. 38 Intensification of 2-Methyl-1,3-Dioxolane hydrolysis for recovery of ethylene glycol through reactive distillation: Kinetics and process design [DOI]
  39. 39 Novel application of neural network modelling for multicomponent herbal medicine optimization [DOI]
  40. 40 Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network [DOI]
  41. 41 Identification of a Pilot Plant Reactive Distillation Process using RBF Networks [DOI]
  42. 42 Multiuser interference mitigation in multipath fading channels using a neural network based blind receiver [DOI]
  43. 43 Vapour reactive distillation process for hydrogen production by hi decomposition from hi-i2-h2o solutions
  44. 44 The Application of Neural Networks to Detecting Decay Events of a Neutron [DOI]
  45. 45 International Journal of Distributed Sensor Networks [DOI]
  46. 46 Distributionally Robust Games: f-Divergence and Learning [DOI]
  47. 47 Data on conceptual design and simulation of reactive distillation process [DOI]

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