Machine vision system for assessment of firing process parameters in rotary kiln

Dmitriy Aleksandrovich Yudin, Valeriy Zalmanovich Magergut

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Article describes developed hardware-software complex of machine vision system for assessment of firing process parameters in rotary cement kilns. Authors developed method of firing process image recognition, allowing automatically real-time assessment of the firing process in three parameters: dust, state of the material and state of the torch.Its program implementation was performed.Authors made the selection of the number of hidden layer neurons of the network trained by extreme learning machine (ELM)provided the maximum average parameters classification accuracy. Advantage of ELM for the classification of the firing process parameters compared to the other methods was showed.Comparison of functionality of the proposed system with analogs was performed and showed its advantages. Developed machine vision system is being tested at the cement kiln CJSC "Oskolcement" (Russia, Stary Oskol).Possible positive effectsof machine vision system applicationwas showed: enhancing work efficiency of kiln operators, reducing the number of accidents and damage of kiln, as well as reducing of fuel consumption.

Original languageEnglish
Pages (from-to)1460-1466
Number of pages7
JournalWorld Applied Sciences Journal
Volume24
Issue number11
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • Extreme learning machine
  • Firing process
  • Image recognition
  • Machine vision system
  • Rotary kiln
  • Self-organizing map

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