Introduction  The paper aims to establish a sparseness approach based sample distribution for LS-SVM models to solve the problem of excessive computation in the application of classical iterative shearing sparseness algorithm for the soft measurement model of the carbon content in flying ash.

  Method  On the basis of calculating the feature space distance between the samples, global representative indicator is constructed by mixing together the density and dispersion. The original samples were sorted and pruned and the sparsenseness was realized according to the indicator. The LS-SVM soft measurement model of the carbon content in fly ash was applied to a 1 000 MW coal-fired power plant, the original training sample set was taken from the field operation data of the unit.

  Result  The results show that the proposed algorithm can greatly reduce the capacity of the training set with tiny loss of the error performance and it can reduce the training and online prediction calculation work during the LS-SVM soft measurement model of the carbon content in fly ash.

  Conclusion  The LS-SVM sparse algorithm proposed in this paper reduces the sample space from 90 to 30, which not only reduces the calculation scale, but also guarantees the calculation accuracy, while guaranteeing that the error is reduced by 0.01%. The algorithm can realize on-line soft measurement of carbon content in fly ash in industrial controllers with limited computing performance such as PLC, and can be extended to other parameters soft measurement systems in power plants.