Research On Industiral Process Identification Based On Multiple Model Approach

Industrial processes have seen an astonishing increase in their requirements, and mostof the research on industrial process control and optimization are based on the processmathematical models. Therefore, researches on industrial process identifcation have im-portant theoretical signifcance and practical values, and have drew more attentions byindustrial practitioners as well as academic researchers.Industrial processes usually exhibit nonlinearity, uncertainty, time delay, missing dataand diferent sampling rates between the inputs and outputs, and traditional identifcationmethods based on single linear model have some limitations for identifying the process-es. Therefore, this thesis focuses on the research on the industrial process identifcationproblems; the process models are derivatived, and several simulation examples as wellas pilot-scale experimental study are considered to illustrate the efcacy of the proposedmethod. The major work of this thesis includes:(1) Identifcation of dual-rate linear system with time delay. The slow-rate model of thedual-rate system with time delay is derived by using the discretization technique.The states are estimated by using Kalman flter, and the parameters are estimatedbased on the stochastic gradient algorithm or recursive least squares algorithm.When concerning state estimate of the dual-rate system with time delay, the stateaugmentation method is employed with lower computational load than that of theconventional one. Simulation examples and an experimental study on a pilot-scalemultitank system are given to illustrate the proposed algorithm.(2) Identifcation of nonlinear systems with an uncertain scheduling variable. A multiplemodel approach is developed; wherein, a set of local auto regressive exogenous(ARX) models are frst identifed at diferent process operating points, and are thencombined to describe the complete dynamics of a nonlinear system. An expectation-maximization (EM) algorithm is used for simultaneous identifcation of local ARXmodels, and for computing the probability associated with each of the local ARXmodels taking efect. A smoothing algorithm is used to estimate the distributionof the hidden scheduling variables in the EM algorithm. If the dynamics of thescheduling variables are linear, Kalman smoother is used; whereas, if the dynamicsare nonlinear, sequential Monte-Carlo method is used. Several simulation examples,including a continuous stirred tank reactor and a distillation column, are consideredto illustrate the efcacy of the proposed method. Furthermore, to highlight thepractical utility of the developed identifcation method, an experimental study on apilot-scale hybrid tank system is also provided. (3) Identifcation of nonlinear systems with multiple and correlated scheduling variables.Multiple ARX models are identifed on diferent process operating conditions, and anormalized exponential function as the probability density function associated witheach of the local ARX models taking efect is then used to combine all the localmodels to represent the complete dynamics of a nonlinear system. The parametersof the local ARX models and the exponential functions are estimated simultaneouslyunder the framework of the EM algorithm. A numerical example of two correlatedscheduling variables is applied to demonstrate the proposed identifcation method.(4) Identifcation of nonlinear processes in the presence of noise corrupted and corre-lated multiple scheduling variables with missing data. The dynamics of the hiddenscheduling variables are represented by a state-space model with unknown parame-ters. To assure generality, it is assumed that multiple correlated scheduling variablesare corrupted with unknown disturbances and the identifcation data-set is incom-plete with missing data. A multiple model approach under the framework of theEM algorithm is proposed to formulate and solve the identifcation problem of non-linear systems. The parameters of the local process models and scheduling variablesmodels as well as the hyperparameters of the weighting function are simultaneouslyestimated. The particle smoothing technique is adopted to handle the computationof expectation functions. Identifcation of a numerical example and a distillationcolumn are considered to demonstrate the efciency of the proposed method. Theadvantages of the proposed method are further illustrated through an experimentalstudy on a pilot-scale multitank system.(5) Identifcation of nonlinear systems with a noisy scheduling variable, and the mea-surement of the system has an unknown time delay. ARX models are selected as thelocal models, and multiple local models are identifed along the process operatingpoints. The dynamics of a nonlinear system are represented by associating a nor-malized exponential function with each of the ARX models; therein, the normalizedexponential function is acted as the probability density function. The parameters ofthe ARX models and the exponential functions as well as the unknown time delay areestimated simultaneously under the EM algorithm using the retarded input-outputdata. A continuous stirred tank reactor example is given to verify the proposedidentifcation approach.