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

Chungen Yin, Zhongyang Luo, Junhu Zhou, Kefa Cen

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

37 Citationer (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.
TidsskriftChemical Engineering Research & Design
Udgave nummer1
Sider (fra-til)118-124
Antal sider7
StatusUdgivet - jan. 2000
Udgivet eksterntJa


Dyk ned i forskningsemnerne om 'A novel non-linear programming-based coal blending technology for power plants'. Sammen danner de et unikt fingeraftryk.