| There exist a good many variables in the industrial process. At present, it is difficult, sometimes impossible, to check and measure the variables, which is closely related to the quality of production, through the sensor due to technological or economic restriction. It will affect the stability of the system, which will bring about large economic losses. Therefore, soft sensing comes into being.This paper goes deep into several important aspects of soft sensing technology, creating multiple models of soft sensing based on partial least square (PLS) and neural networks (NN). Then it compares the result of prospect. Combining the actual application of industrial polypropylene (PP) process, soft sensing models have been created with the data collected from the field and used to make prospects, which brings about good effects. Contents in this paper are as follows: 1 .It covers basic conceptions and their development of soft sensing technology andanalyzes the model-creation idea, the four factors and implement of soft sensing.The production process of industrial PP is briefly introduced. 2.The theories of PCA, PLS, and ANN are introduced to pave the way for laterapplication of soft sensing technology. 3.Soft sensing models are set up according to PLS and RBF and tested through thedata sample which is collected from the simulation of double evaporation. ThenPLS-RBFNN is gained by combining the model PLS with RBFNN. Finallycomparison is made among the three models. 4.Reaction mechanism of PP is introduced, multiple factors influencing melt index(MI) of PP are analyzed and process variables of equal polymerization andmultiple polymerization are set up. Model PLS and Model PLS-RBFNN are set upwith the data collected from the field and used to make prospects of other data.Good results have been received.Finally, conclusion is made, as well as the expectation for the future development of the soft sensing. |
