Open AccessCoal blending optimization model for reducing pollutant emission costs based on Support Vector Machine
Author(s) -
Yue Zhao,
Guilin Wang,
Qingbo Hu,
Yigang Zhou
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/300/3/032086
Subject(s) - pollutant , coal , flue gas desulfurization , boiler (water heating) , support vector machine , genetic algorithm , process engineering , waste management , coal water , environmental science , computer science , engineering , chemistry , artificial intelligence , machine learning , organic chemistry
Factors such as the pollutant formation, pollution emission punishment and pollutant control devices are considered to optimize the coal blending method for a 300 MW boiler unit. The support vector machine (SVM) is used to establish the pollutant formation prediction model for the coal–fired boiler. Moreover, the model built above is trained and verified based on the actual operation data. Then the genetic algorithm is applied to optimize the coal blending method with the coal price to achieve the lowest operation cost. It can be concluded from the results that the precision of the prediction model is relatively high and the ammonia consumption, NO x emission punishment, CaCO 3 consumption and desulfurization water consumption have all decreased after optimization, which means both the desulfurization cost and denitration cost are reduced.