Michael S. Davies

Estimation of moisture variations on paper machines

An improved estimation algorithm for use in processing data generated online by a scanning sensor is described. The algorithm is to be used as part of a paper machine control system to maintain the moisture content of the sheet at a target value. The algorithm makes use of a previously published model to rapidly estimate, in the presence of noise, cross-direction and machine-direction (CD and MD) moisture profiles. The basic algorithm consists of a modified least-squares (LS) parameter identifier for estimating CD profile deviations and a Kalman filter for estimating MD disturbances. Simulation results showing the effectiveness of the algorithm in estimating known profiles are given. Results of the offline application of the algorithm to industrial data are also given. Online tests have been performed to demonstrate the improvements in accuracy and speed of detection of process upsets. The algorithm can be extended to other measurement models. >

Estimation in paper machine control

The problem of online estimation of basis weight and moisture content in paper machines is discussed, and algorithms for separating cross machine and machine direction (MD) variations using scanned data are proposed. Because of its inherent nonlinearity, the moisture scheme uses a bootstrap algorithm, assuming known MD dynamics. For basis weight, the model linearity can be used to develop an extended Kalman filter to estimate the more complicated MD dynamics. Both algorithms have been tested on industrial data. Results from the basis-weight algorithm when applied to industrial scanned and stationary data collected from an operating paper machine show that a second order autoregressive moving average (ARMA) model gives the best fit to the data in terms of sum of squares of prediction errors and in terms of the whiteness of the residual. Furthermore, there is a very good agreement between the MD models estimated with the scanned data and the single point data.<<ETX>>

Modelling and identifications of basis weight variations in paper machines

A dynamic model for basis weight variations in paper machines is established and estimated recursively from scanned measurements using a combination of an exponential forgetting and resetting least squares estimator and an extended Kalman filter. The algorithm provides online estimation of basis weight variations in both cross-machine direction and machine direction, as well as estimates of the dynamic parameters and noise characteristics of the process. >

Adaptive basis weight control in paper machines

A dynamic model for basis weight variations in paper machines has been developed. The model is identified recursively from scanned measurements, and so provides an online estimation of basis weight variations in both cross-machine direction and machine direction, as well as estimates of the dynamic parameters and noise characteristics of the process. Here the approach is extended to include the adaptive generalized prediction control (GPC) algorithm for machine direction variations of the basis weight process. The adaptive controller is designed to overcome shortcomings of the nonadaptive traditional controllers being used.<<ETX>>

An Algorithm for Estimating Cross and Machine Direction Moisture Profiles for Paper Machines

Abstract In this paper we develop an algorithm for estimating, in the presence of noise, cross and machine direction (CD and MD) moisture profiles based on a model described in the literature. The algorithm consists of a least squares parameter identifier for estimating CD profile deviations and a Kalman filter for estimating MD profiles. Simulation results of the algorithm are given. The preliminary results of the application of the algorithm to industrial data are also given. The algorithm developed can be extended to other measurement models

An improved algorithm for estimating paper machine moisture profiles using scanned data

An improved estimation algorithm for use in processing data generated online by a scanning sensor is described. The algorithm is to be used as part of a paper machine control system to maintain the moisture content of the sheet at a target value. The algorithm rapidly estimates, in the presence of noise, cross and machine direction moisture profiles. The basic algorithm consists of a modified least-squares parameter identifier for estimating cross direction profile deviations and a Kalman filter for estimating machine direction disturbances. Simulation results showing the effectiveness of the algorithm in estimating known profiles are given. Results of the offline application of the algorithm to industrial data are also given. Online tests have been performed to demonstrate the improvements in accuracy and speed of detection of process upsets.<<ETX>>

Brief A controller perturbation technique for transferring closed-loop stability between systems

This brief paper presents a perturbation technique with which a controller stabilizing one plant may be modified so that it stabilizes a second, related plant. The proposed technique produces an internally stable loop for a broad class of linear systems without requiring any further calculations on the part of the designer. Four seemingly different examples are described in terms of this result.

Stability-preserving modification of paper machine cross-directional control near spatial domain boundaries

The optimal controller design for spatially distributed dynamical systems is greatly simplified when spatially invariant models are used. However, the true spatial boundary conditions of many practical systems, including paper machine cross-directional processes, disrupt the spatial invariance and potentially destabilize the control system if not taken into account. This paper considers the design of stability preserving spatial boundary conditions for paper machine cross-directional controllers originally computed for idealized spatially invariant processes.

Paper machine data compression using wavelets

The paper describes the analysis of paper machine process data using discrete wavelet transforms. The techniques have been adapted from a general signal analysis theory. The authors previously showed (1996) that wavelets are an effective representation for the detection of basis weight and moisture process variations in noisy data and lead to improved estimation and visualization of the machine direction and cross machine variations. This paper discusses data storage using the wavelet representation, and shows that the method also allows significant compression of the scanned data without diminishing the accuracy with which profiles can be reconstructed. It is shown that the compression method can be embedded into the estimation algorithm, producing excellent results without major expense in computation time. The ability to reduce data storage requirements is of increasing importance in mill-wide process monitoring systems and quality assurance.

CD response modelling for control of a paper machine

This article considers wet end modelling of a Fourdrinier table. Cross direction (CD) response to an actuator adjustment is the principal requirement for a good CD basis weight profile control. The physical basis of the model is a surface wave generated by the deflected slice on the surface of the slurry. The wave model combined with the drainage from the wire side establishes the mat formation process. This approach to modelling eliminates the need for estimating a large matrix when a purely time series parametrization of the CD response is considered.