The multi-delays of the whole equipment unit in the process industry are indicated in the form of a sequence of integers. A restructured data matrix is generated based on the sequence and the original data. The time-correlation analysis is defined to describe the correlation between the columns in the data matrix and H∞ norm is used to quantify the correlation of the matrix so that the multi-delays identification problem can be converted to calculating the biggest H∞ norm. At last

the discrete state transition algorithm is adopted to quickly search the biggest H∞ norm and the multi-delays are identified. The proposed method is used for multi-delays identification of alumina evaporation process and the result of multi-delays identification is applied to the data preprocessing of a prediction model. The accuracy of the prediction model is improved by 34.4% and the proposed identification method is effective.