Research And Application Of Soft Sensing Technolgy Of Kappa Number In Pulp Cooking Process

Cooking is an important stage in Kraft pulping process. It is also a verycomplicated chemical industrial process. During the cooking process to stabilize theKappa number is the key to stabilize the quality of paper pulp. The steady Kappanumber is also helpful to decrease the consumption of stream and chemical products,to decrease the environment pollution and enhance production efficiency. In order tocontrol the Kappa number of pulp, it must be measured or estimated online.Regrettably, until now the Kappa number online measurement instrument, which isprecision, dependable, low-cost and commercial, has not been developed throughoutinland and overseas. Therefore, it is significant in theory and application to developsoft sensing technology of Kappa number in cooking process.As a rising industrial technology, soft sensing has great developing space. Itemploys easily measured variables (auxiliary variables) and their relationship (softsensing model) with process variables to be measured (primary variables), which ishard to measure directly, by computation and estimation models.From the point of Data-mining, soft sensing technology is an importantmethodology to analyze available information and find out rules. To act as an exactand sensitive soft sensor for the design of the advanced control system based on softsensing technology, during the soft sensing modeling process, it is necessary tointegrate different theories and methods to dig out useful information from theoriginal data.This dissertation concentrated on the research work listed below and achievedsome creative results.1) Based on the technical analysis and the condition of actual product process of thebatch pulp cooking, the dissertation points out the limits of single model for the wholecooking process, since the process of delignification is linearization for differentphase. A new subsection model is presented based on the simplified Hatton model.2) After analyzing the composing of prediction error of soft sensing model, a methodof abnormal data discovery for data processing of Kappa number soft sensing ispresented. The new data processing method digs out incompatible data based on dataclustering and mechanism analysis, as well as finds out the outlier data by regressionanalyzing and statistical analysis. It also can explain the impact of abnormal data onsoft sensing. The method is validated by data analyzing from actual factory cookingprocess. Since there are many problems hiding in the measured data from the actual...