A novel non-linear programming-based coal blending technology for power plants

Chungen Yin, Zhongyang Luo, Junhu Zhou, Kefa Cen

Research output: Contribution to journalJournal articlepeer-review

37 Citations (Scopus)


Coal blending has now attracted much attention in coal industry of China, and has been investigated extensively to meet the often conflicting goals of environmental requirements and reliable and efficient boiler operation in power plants. However, most of the existing blending projects are guided by experience, or linear-programming (LP), whose main assumption is that all the quality parameters of a blend can be approximated as the weighted average of the corresponding indexes of its component coals at any condition. This has been proved incorrect for some blend properties. Now, more and more evidence
indicates that a strong non-linearity exists between some quality parameters of a coal blend and those of its component coals. Thus the unreliable assumption impairs the resulting coal-blending scheme. To remedy this situation, a novel coal blending technology for power plants, i.e. using nonlinear programming (NLP) based on neural network models, was proposed, and has now been successfully applied at the Hangzhou Coal Blending Center. The application attests that this new technology is much better than the existing linear-programming coal-blending method.
Original languageEnglish
JournalChemical Engineering Research & Design
Issue number1
Pages (from-to)118-124
Number of pages7
Publication statusPublished - Jan 2000
Externally publishedYes


  • Coal blending
  • Nonlinear programming
  • Neural networks
  • Back propagation
  • Optimization design
  • Linear programming


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