Study On Modeling And Control Based PLS Applied To Thermal Process

Industry process control is to mend techniques manipulation, improve automation degree, optimize production process and enhance production manage through studying on description, stimulation, design, control and manage of production process. Finally, it can achieve the object which improve production efficiency, reduce source consume and control deleterious substance release. Now, following the fast improving of process control and computer technique and so on, we can get more data come from production. If the information can be full extracted and analyzed, it may give us useful help to more control production. However, there are many difficult problems such as a great deal of process variable and nonlinear and time-varying of process. The dissertation deeply studied partial Least Squares method and applied it into soft measure modeling and adaptive control of thermal process control. The main contributions of this dissertation can be summarized as followings:By comparing PLS and mult-irecursive least-square with principal component recursive algorithm, the validity which PLS and principal component recursive algorithm effectively deals with relativity problems when data has relativity is tested. Experiments results show that PLS possess better fitting property.Recursive PLS and its improved methods are investigated. A new recursive Nonlinear PLS is proposed which deal with nonlinear problem with Guess kernel function and update model by recursive PLS. The model obtained by this method have better nonlinear manage ability and update model to fit process variety following condition. Radiation heat-receiving side and pollute side of thermal power plant have difficulty problems of dimness mechanism, strongly nonlinear and big time space. Power station forecasting model is building using proposed algorithm. Experiments verify its effect.Discount recursive PLS (DRPLS) is obtained by using discount factor based on recursive PLS. Moreover, combining DRPLS with dynamic matrix forecasting control algorithm, an adaptive control algorithm based on discount recursive least-square is proposed. Because of factor effect, model parameter is time-varying which can be improved precision by update model with DRPLS. The simulation experiments on superheated steam temperature control of power plant verify its effect convenient.Kernel nonlinear partial least square algorithm based on Mercer theory namely KPLS-LSSVM is proposed. Considering the nonlinear relation is not only being data outside but also being inside data between input and output, the method has more strong process ability than normal nonlinear PLS. The method was applied in building carbon content in fly ash soft measure model of thermal power plant. Experiment verify its effect and convenient.