The carbon content in fly ash is difficult to predict correctly

which has been a problem in a long term.Thus

from such aspects as improving the prediction precision and self-adaptive ability of the model

a model prediction performance evaluation based self-adaptive correction WLSSVM soft-sensing model was proposed.In order to enhance the sparsity of the WLSSVM model and reduce the calculation amount during training process

the maximal linearly independent group based training sample set of the soft-sensing model was constructed.The data similarity weighting factor based WLSSVM soft-sensing model was established

and the the double population differential evolution algorithm was employed to optimize the model parameters.By online evaluation and recursive correction of the model prediction performance

the online self-adaptive correction of the model was realized.The simulation test was conducted on a 300 MW coal-fired boiler.The results showed that the proposed soft-sensing model had high prediction accuracy and good self-adaptive ability

and can predict the carbon content in fly ash effectively.