Research And Application Of Soft-sensing Technology Based On Support Vector Machine

Due to the fact that large-lag, large inertia, strong nonlinearity, time-varying and multivariable coupling existed in papermaking process.It is difficult to model, control and optimize papermaking process. Thedevelopment of all kinds of intelligent technologies provide a feasibleapproach to solve the problem.This paper describes the application status of intelligent controltechniques, such as artificial neural network, fuzzy logic, expert systemand its integration. Then its applications and research developmentprospects are evaluated, and pointed out that support vector machine willexhibit great potentialities in pulping and papermaking process.Papermaking is made of two process—pulping process andpapermaking process, pulping process include cooking, washing, screening and bleaching process, papermaking process include Preparation of stock, Approach of stock and so on. White water system iscomplex, white water consistency is measured difficulty.The nerve network has structure variability, fault-tolerant, non-linearity, self-organizing and self-learning etc, but all the technologybased on artificial neural network must first choose the model structure, easily getting a local optimum solution, also cannot guarantee optimality.Moreover the basic reason lie in these technologies based on theexperience risk, is unable to guarantee the full sample quantity. Thereforeproposed the support vector machine, a new kind of machine learningmethod. This machine learning method is basis of the statistical theoriesand the structure risk minimum principle, it is difference in essential withthe traditional study method based on the experience risk. Overcame thenerve network training to sink into the local optimum solution; The smallsample study may enable the support vector machines having favorablecapabilities of universality and generalization, does not need excessivelyto rely on the quantity and the quality of sample. The vector machine liein dose not need the artificial design model structure, is different from thenerve network, avoids artificial influence in the modeling process. It canbe seen from the simulation results that the support vector machine canmake better predictions of regression than the nerve network.But so far, parameter of support vector machine choose was mainlydepended on their experience, without a theoretical basis, Therefore proposed Genetic Algorithm carried on the parameter choice. In view ofthe complex papermaking technological process, Least square supportvector machines was proposed. It lost its original sparseness, so animproved Least square support vector machines regression method wasproposed simplified complex degree of model. This article use the leastsquares support vector machine to establish soft sensing model of whitewater consistency, Study Influential factors of white water consistency, In-depth Analysis on main influential factors, choose main influentialfactors which could acquire data in actual production, Establishingcorresponding relationship between data acquisition of main influentialfactors (flow of pulp, consistency of pulp and stock consistency on wire)and white water consistency, realization white water consistency on-lineestimate in paper making process. The test show that the methodpossesses high prediction precision and excellent forecasting effect, it canoptimize parameter of white water consistency in the papermakingprocess.An effective research approach about modeling and optimization ofthe papermaking process by using intelligent technology is provided. Theresults might be helpful to correlative study (advanced control, on-lineoptimization, diagnosis, etc.) in papermaking process. The achievementspointed out that intelligent technology will exhibit great potentialities inpapermaking process, and provide an instantiation for the application of intelligent technology in chemical process.