Study on prediction method of clinker f-CaO content based on working condition classification
In order to achieve continuous and stable prediction of free calcium oxide(f-CaO)content,this paper uses ensemble learn-ing algorithm to study the soft measurement methods.For the complex working conditions in the firing system,the classification of work-ing conditions is firstly carried out,and then an integrated learning prediction model is constructed for each type of working condition,at the same time,the introduction of online modeling improves the generalization ability and time effectiveness of the model,solving the problems of short-term and repetitive modeling.The integrated learning algorithm is based on the idea of bagging to construct models for various weak learners,and it is found that the effect of the model is significantly better than that of a single model through inspec-tion.The algorithm combines the characteristics of process production and multiple regression algorithms,has good stability and ad-vanceness,realizes real-time control of cement quality,and helps cement plants to produce high-quality and stable products.
f-CaOensemble learningsoft measurementclassification of working conditionsonline modeling
NETL
NSTL
万方数据
