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UBC Theses and Dissertations

Adaptive paper coating weight control Chen, Yamei


Paper coating is the process of applying coating colour or functional materials to the surface of paper. The goal of coating is to improve the surface quality of the paper. In this thesis, the bevelled-blade coating process is modeled based on the force equilibrium at the tip of the blade using fluid mechanic principles. The effects of the factors influencing coating weight are analyzed using simulation results. Mill trials have been carried out to investigate the dynamics of the coating process and the interaction of the cross machine profilers. Machine-direction (MD) and cross-direction (CD) variations were estimated using an algorithm previously developed at the University of British Columbia. Coating weight variations were studied and possible achievements were indicated. From the estimated cross-direction profiles, the amplitude and width of the profile response to the bump test were analyzed, and then used to build the interaction model of CD actuators. The goals of both the machine-direction and the cross-direction coating weight controls are to improve the uniformity of coating weight Due to the advantages of Generalized Predictive Control, both MD and CD control loops are designed using adaptive constrained GPC. The machine-direction coating weight process is modeled as a first-order system with a time-varying gain defined by the nonlinear relationship between coating weight and the control variable. The time-varying parameters are estimated by the recursive least-squares (RLS) method with exponential forgetting and resetting factors. The cross-direction coating process is also modeled as a first-order system with a nonlinear gain determined by the relationship between the local blade pressure and the coating weight Based on the interaction of CD actuators, the interaction matrix of the first-order model is defined as a band-diagonal matrix. Multi-input, single-output RLS estimators are developed to perform on-line parameter estimation. Constraints on the control signal are considered in response to the limits of the industrial settings. The optimal solutions to both constrained univariate and multivariable GPC are obtained using the Lagrangian multiplier method. Simulation studies are carried out to evaluate the performance of the adaptive constrained GPC. The results of the simulations show that the controllers can track the set-point changes closely, while rejecting the disturbance and handling the model plant mismatch well.

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