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

Coal blending has now attracted much attention in coal industry of China, a nd 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 avera ge of the corresponding indexes of its component coals at any condition. Th is 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 unrel iable assumption impairs the resulting coal-blending scheme. To remedy this situation, a novel coal blending technology for power plants, i.e. using n onlinear programming (NLP) based on neural network models, was proposed, an d has now been successfully applied at the Hangzhou Coal Blending Center. T he application attests that this new technology is much better than the exi sting linear-programming coal-blending method.