Aiming at solving the problem of severe slagging and fouling during Zhundong coal burning

a model was established to predict the clean heat-absorption capacity of low temperature superheater

by using support vector machine(SVM) and genetic algorithm(GA). Moreover

the value of clean coefficient was calculated to judge the ash fouling degree for heating surface

thus to instruct the operators to carry out ash operation. This is more reliable and economical than the conventional blow by time. Taking a 330 MW unit as an example

the model uses the data of DCS system to practice and predict the clean absorption heat of low temperature superheater

and the average relative error of the test set is only 1.35%. Furthermore

according to the historical data

the clean curve is obtained

which is consistent with the actual situation. The results show that the prediction model can reflect the pollution of low temperature heating surface well to some extent

thus it provides a reference for developing online monitoring systems of fouling and slagging in large-scale utility boilers.