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Soft Measurement Method Based on Conditional Mutual Information and Autoregressive Neural Network

In the actual industrial process, some parameters have a great influence on the production results but it is difficult to measure directly. The only way to solve this kind of problem is to use soft measurement to predict the target value. The two factors that have the greatest influence on the accuracy of soft measurement are the auxiliary variable selection and modeling methods. In this paper, a soft measurement modeling method based on conditional mutual information and autoregressive neural network was proposed. First, a mechanism analysis of the production process was performed to determine a set of candidate auxiliary variables that affect the measurement. Then used the conditional mutual information to further filter the auxiliary variables from the candidate auxiliary variable set., and determined the minimum input variables set for the soft-measurement model while ensuring measurement accuracy. This paper taken the measurement process of NOx generation of a 600MW coal-fired power plant as an example, and established a soft-measurement model by using autoregressive neural network. The experimental results showed that the prediction accuracy of the model is significantly improved and had better generalization ability after the input variables are filtered by the conditional mutual information.