Prediction of solid holdup in a gassolid circulating fluidized bed riser by artificial neural networks

Hanbin Zhong, Zeneng Sun, Jesse Zhu, Chao Zhang

Research output: Journal PublicationArticlepeer-review

6 Citations (Scopus)

Abstract

The artificial neural network (ANN) method was applied to predict the solid holdup in a gassolid circulating fluidized bed (CFB) riser. All the possible ANNs were first developed by looping the hidden neurons from the minimum (3) to the maximum (number of training data) and performing 500 independent runs for the same ANN structure. Then, an improved rule for finding the best ANN was proposed with the help of the expected range of the predicted solid holdup based on the existing data under training conditions. The accuracy of the prediction for test conditions was significantly enhanced by using the improved rule. The reproducibility and applicability of the proposed ANN development process were fully examined by repeating several times on the same sample and applying to different samples, respectively.

Original languageEnglish
Pages (from-to)3452-3462
Number of pages11
JournalIndustrial & Engineering Chemistry Research
Volume60
Issue number8
DOIs
Publication statusPublished - 3 Mar 2021
Externally publishedYes

ASJC Scopus subject areas

  • Chemistry (all)
  • Chemical Engineering (all)
  • Industrial and Manufacturing Engineering

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