A Hybrid Method for Prediction of Ash Fouling on Heat Transfer Surfaces

Abstract

Soot blowing optimization is a key, but challenging question in the health management of coal-fired power plant boiler. The monitoring and prediction of ash fouling for heat transfer surfaces is an important way to solve this problem. This study provides a hybrid data-driven model based on advanced machine-learning techniques for ash fouling prediction. First, the cleanliness factor is utilized to represent the level of ash fouling, which is the original data from the distributed control system. The wavelet threshold denoising algorithm is employed as the data preprocessing approach. Based on the empirical mode decomposition (EMD), the denoised cleanliness factor data is decoupled into a series of intrinsic mode functions (IMFs) and a residual component. Second, the support vector regression (SVR) model is used to fit the residual, and the Gaussian process regression (GPR) model is applied to estimate the IMFs. The cleanliness factor data of ash accumulation on the heat transfer surface of diverse devices are deployed to appraise the performance of the proposed SVR + GPR model in comparison with the sole SVR, sole GPR, SVR + EDM and GPR + EDM models. The illustrative results prove that the hybrid SVR + GPR model is superior to other models and can obtain satisfactory effects both in one-step-and the multistep-ahead cleanliness factor predictions.