Abstract: Based on the complex process of blast furnace gas (BFG) system in steel industry, a network forecasting method based on data-driven is established in this paper for the prediction problem on BFG output. Since the practical data include a diversity of noises, an empirical mode decomposition approach is employed to decompose the time series signal into a group of independent intrinsic mode functions, and the formed small-scale intrinsic mode functions are de-noised by low-pass filter with an adaptive threshold. Then, the re-constructed signals are used to build the forecasting model, in which an improved echo state network is proposed and the network output weights are obtained by singular value decomposition. Therefore, the ill-conditioned problem of previous linear regression is overcome and the forecasting precision is increased. The prediction results using practical production data show the validity of the proposed method and also provide the scientific decision support for the gas resources scheduling.