Research On Stochastic Distribution Control Based Control Methods For Refining Process

As one of the most important steps in the papermaking industry,the refining process mainly provides pulps meeting the corresponding physical properties for the subsequent papermaking process,and is also a prerequisite for ensuring the final paper quality.Pulp is the key raw material for the entire paper industry,and its quality plays a decisive role in the final paper properties,such as the stretch ability,tensile strength and tear resistance of the paper.The pulp quality in the refining process is mainly characterized by fiber length and water filtration performance(CSF).Specifically,the short fiber length or the low pulp freeness makes the refining process display high energy consumption and low dehydration rate.When the water volume contained in the fiber is too high,the energy consumption of the paper drying process in the subsequent papermaking process also high.On the contrary,if the fiber length is too long or the freeness is high,it is advantageous to form large porosity in the paper sheet,thus increasing the dewatering efficiency in the paper production process and reducing the energy consumption of the drying process.However,the excessive porosity will seriously affect the quality indexes of the paper products,such as the uniformity,the strength and the smoothness.Therefore,the performance of the refining process,especially the control of fiber length and pulp freeness,is not only directly related to both the energy consumption and the paper quality in the subsequent papermaking process,but also directly affects the uniformity and stability of the pulp quality.However,due to the existence of uncertainties such as the mutual entanglement,extrusion,friction,fibrillation between fibers and the process equipment aging,the fiber length distribution shape characterizing the pulp quality shows strong non-Gaussian stochastic distribution dynamics.Using the mean and variance of fiber lengths are not sufficient to fully describe all the statistical information of the fiber length distribution shape.In order to improve the pulp quality,reduce the operating energy consumption and production cost,it is necessary to study control methods for the refining process with non-Gaussian stochastic distribution dynamic characteristics.In view of the shortcomings and the existing problems in the current refining process control methods,this research relies on the National Natural Science Foundation’s key project"Optimal Operation and Control of Pulping Process for Energy Saving and Fiber Morphology Distribution(61333007)".Targeting at the typical refining process under the current popular Chemi-Thermo-Mechanical Pulping(CTMP)method in paper industry,we conduct research on the control methods of the refining process based on stochastic distribution control(SDC)method.The contribution of this thesis is summarized in the following aspects:1)Since controlling the mean of the fiber length is difficult to effectively control the shape of the fiber length distribution in the refining process with non-Gaussian stochastic distribution characteristics.We combine the SDC with the data-driven control methods,and then study a series of control methods for Probability Density Function(PDF)shaping based on the SDC for fiber length distribution in the refining process.It includes the geometric analysis double closed-loop iterative learning control(ILC)method and the data-driven prediction PDF control method.A.In order to address the challenge that the mathematical model of refining processes is impossible,a data-driven modeling and control method for maintaining the shape of the fiber length distribution of the refining process is proposed,consisting of data-driven subspace parameter identification,geometric analysis-based double closed-loop ILC and SDC.Different from the double closed-loop control structure in the traditional sense,it is a double closed-loop control structure designed with the two-dimensional feature of spatial-domain and time-dimension for the refining process with non-Gaussian stochastic distribution dynamic.The double closed-loop is based on the ILC principle,tracking the error to realize the update of the output PDF model and the control variables.We construct the PDF model of the fiber length distribution shaping in the refining process based on the ILC principle in the inner-loop,including the linear subspace parameters identification of weight vector and the adaptive adjustment of the basis function parameters based on the iterative learning law.In the outer-loop,the control effects(such as the disc gap,the dilution water flow rate)are updated by introducing the ILC method based on the geometric analysis,which further improves the convergence rate of the closed-loop system.The proposed method combines the data-driven modeling method for the output PDF and ILC,and realizes the dynamic control of the PDF shaping of fiber length distribution in the refining process through the hybrid dynamic control of the spatial-domain variable(shape of basis function)and the time-domain variable(weight vector).The simulation experimental results using data show the effectiveness of the proposed method.B.Concerning the time-varying operation condition in the refining process,the dynamic PDF model of the fiber length distribution shaping using the traditional linear subspace identification often has problems such as weak generalization ability and low precision.Based on the intelligent modeling method and the data-driven predictive control as well as the SDC method,the control method for the shape of fiber length distribution in the refining process is proposed based on the data-driven predictive PDF control.Firstly,utilizing the data-driven intelligent modeling method,the nonlinear model characterizing the dynamic relationship between the control input and the weight vector is constructed.Secondly,the basis function parameters are adjusted adaptively based on the dynamic PDF model error by introducing the iterative learning update mechanism of the basis function parameters.Finally,based on the dynamic PDF model of the fiber length distribution in the refining process,the controller design is transformed into solving the optimization problem with constraints so as to achieve the optimal control of the shape of fiber length distribution in the refining process.The simulation experimental results using data show the effectiveness of the proposed method.2)In view of the problem that pulp fibers satisfying specific requirements(such as shape of fiber length distribution and pulp CSF)need to be obtained in the current refining process,the operation energy consumption and production cost need to be reduced as much as possible.By Combining the SDC with multiobjective optimal control methods,the multiobjective optimal control methods for the refining process oriented to the pulp quality and the operation energy consumption optimization are studied.A.In order to address the problem that current refining process only focuses on the control of the filtration performance parameters(CSF)of the pulp quality and ignores the operating energy consumption and the production cost,a multiobjective optimal control method for the refining process based on the pulp CSF and the operation energy consumption optimization is proposed.Based on the nonlinear mechanism model between the pulp CSF and the operating energy consumption,the dynamic model between the refiner load,throughput and the control variables is constructed by the least squares parameter estimation,and a nonlinear hybrid dynamic model combining the mechanism of the pulp CSF with data-driven is established.On the basis of the constructed hybrid dynamic model,the multiobjective optimal control method for the refining process aiming at optimizing the pulp CSF and the operating energy consumption is proposed.The simulation experimental results using data show that the proposed method not only makes the output pulp CSF obtain satisfactory setpoint tracking performance,but also can effectively reduce the operating energy consumption,thus achieving the multiobjective optimal control of the refining process for the output CSF and the operation energy optimization.B.To handle with the difficulties existed in fully realizing effective control of the pulp quality indexes through individually controlling the CSF or the shape of the fiber length distribution in the refining process,the multiobjective optimal control method for the pulp quality indexes of the CSF and the fiber length distribution is proposed.By controlling the shape of fiber length distribution instead of the traditional average of the fiber length,we overcome the shortcomings of using the average of the fiber length as a measure of pulp quality index.On the other hand,based on the hybrid dynamic prediction model of the pulp CSF and the PDF shaping of the fiber length distribution utilizing the data-driven modeling method,we propose the nonlinear multiobjective predictive optimal control method for the pulp quality indexes in the refining process.It includes the optimal control of PDF shaping of the fiber length distribution with spatiotemporal features and the optimal control of pulp CSF with pure time-domain.The simulation experimental results using data show the effectiveness of the proposed method.