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Disturbance Management is a major issue in process industries like the pulp and paper (P&P) industry. In this paper, a case study in an integrated P&P mill is examined. Production plans for the whole mill need not only to be optimized concerning company's indicators, but also to be robust so that disturbances can be avoided. We present a simulation-optimization approach that generates plans, correctly weighting their quality (regarding various indicators) and robustness. A discrete-event simulation model replicates the dynamics of implementation and adaptation of production plans in practice. The simulation model gives then feedback to optimization, in order to enhance the analytical model, which is thus able to generate robust plans.

During paper manufacture, system actuators need to control the properties of the entire sheet based on a restricted set of data measured by a scanning sensor that traverses the moving sheet. Cross direction variations (CD) are those along an axis perpendicular to the motion of the sheet, while machine direction (MD) variations are those along the axis of motion, and are assumed uniform in CD. Current industrial practice is to separate the relatively slow variations of the CD profile from the higher bandwidth MD variations using low pass filtering, although the spacing and timing of the scanned data measurements makes it inevitable that some process variations will be distorted or lost to aliasing in the filtered data. In this paper, a novel approach to estimation of MD and CD variations is proposed – compressive sensing. In this approach, knowledge of the process is used to help characterize the expected process variations, allowing accurate reconstruction of the true process variations from far fewer measurements than would be indicated by simple bandwidth-based uniform sampling theory. Instead, a random sampling protocol is used to accurately reconstruct the sheet properties. The approach is found to be effective, using simulated and actual industrial process data.

Temporal reasoning is a very valuable tool to diagnose and control slow processes. For control system software it is a difficult problem to detect such patterns including trend extraction and similarity analysis. In this study, an intelligent trend index is developed from scaled measurements of a wastewater treatment plant. The scaling is based on monotonously increasing, nonlinear functions, which are generated with generalised norms and moments. The monotonous increase is ensured with constraint handling. Triangular episodes are classified with the trend index and the derivative of it. Severity of the situations is evaluated by a deviation index which takes into account the scaled values of the measurements. Modelling and simulation of biological wastewater treatment in pulp and paper industry requires hybrid models since the operating conditions can fluctuate drastically. A compact dynamic simulation is realized with linguistic equation (LE) models, which consist of two parts: interactions are handled with linear equations, and nonlinearities are taken into account by membership definitions. The same scaling approach is used in trend analysis and modelling. The LE based trend episodes and deviation indices provide good tools for detecting changes in operating conditions to be used in data and model selection and model adaptation.

This paper presents an enhanced dynamic causal digraph reasoning method for fault diagnosis and its application to the short circulation process of a paper machine. In order to improve the isolation capability of the original method, an inference mechanism between the arcs of the graph is proposed to locate process faults on the arcs. Application of the proposed method to the paper machine short circulation process is presented at the end of the paper. The results show that the proposed method is able to identify the responsible arcs when the system is affected by a process fault.

In this contribution, the dynamical behavior of a paper-making system is presented and an advanced controller is developed to minimize the amount of recycled paper used in the paper making process. The advanced controller is a model based control strategy, and therefore, a model is developed in order to evaluate the loss of paper during the transition between two points of operation. The wastage model is adapted to be used in the cost function of an in-house developed predictive control algorithm. Performance evaluation is described and the corresponding results are presented.

A general strategy to perform water and element balances was developed and implemented for a pulp and paper mill to identify the flow profiles of water and key elements. The materials covered in the balance were water, sodium (Na), sulfur (S), calcium (Ca), potassium (K), and chlorine (Cl). A sampling strategy for the whole plant was first developed and then a number of analytical techniques were chosen, taking into account the special/difficult characteristics of the samples found in a typical Kraft pulp and paper mill. To perform the balances, boundaries around each section of the plant were selected and additional information was created by incorporating extra knowledge/relationships within each section, thus providing redundant information. Error analysis and data reconciliation were performed to evaluate the reliability of the results and correct them, to fmally obtain an adjusted set of data consistent with the balance equations. The reconciled data obtained, as expected, showed better consistency in regards to the balance equations and reduced uncertainties. The final mass balance simulation toolkit developed enhanced the calculation and data processing activities, allowing material balances to be perfonned continuously to provide up-to-date results to serve for “on time” monitoring and “on time” decision making by mill staff.

Typically in pulp and paper processes raw material quality variation due to seasonal deviations as well as measurement drifts cause difficulties in setting the tight alarm thresholds for quality and control measurements. By using adaptive thresholds, more sensitive measurement range and thus reduced quality variation can be achieved. In this paper, new adaptive classification algorithm is proposed and validated using simulated and real mill data.

In this study an indicator for the prediction of paper machine runnability was developed as a Case-based reasoning (CBR) type application with linguistic equation (LE) approach and fuzzy logic. The foundation of this application is a case base containing models of various operating situations with different amount of breaks. The indicator compares online measurements to the examples in case base and uses the information of the best fitting case to identify the current situation. This paper presents the application with some test results.

Recently, La Papelera del Plata implemented the Paper Machine IT project in zarate, Buenos Aires. The integration of control system with supervisory sensor based systems, actuator systems, PLC systems and drives was considered the most important. True system integration requires system level interoperability. ABB has achieved this by offering a wide range of pulp & paper solutions built upon a common system platform. This paper describes how this project was executed and presents the results achieved by La Papelera del Plata paper mill through the application of integrated system solutions, on the Advant Open Control System (OCS) with Master software foundation.

Since most of the chemical processes are represented typically by nonlinear models, the use of nonlinear geometric control in chemical processes is straightforward. This paper presents a decoupling application in a paper machine headbox. The nonlinear geometric control is implemented with blocks for reference trajectory, tracking error, integrators of error feedback and with an anti-windup scheme. The results show a clear implementation scheme and good dynamical responses.

The topics of recovering of materials, quality control, and environmental impact have gained a raising attention in industrial activities, in order to enforce the quality parameters imposed by the new laws. An innovative approach to control and optimisation of the manufacturing process becomes necessary to cut the industrial costs. For this reason, the interest for the intelligent methods for monitoring and control several process phases has been growing since 80's. This paper deals with the development a Neuro-Fuzzy (NF) system for the corrosion control in Pulp & Paper production. Furthermore, the performance of the NF expert system has been compared with that of the classical Neural Network MLPs (NN).

Because of tightened quality requirements of the control of CD (Cross Direction) profiles dilution control headboxes are getting more common. Dilution control utilizing white water makes the short circulation more complicated compared to the traditional short circulation. This paper discusses control problems related to the short circulation of the paper machine having a dilution headbox. A list of the most common disturbance sources is given based on the experience gained from production machines. Consistency control applications based on Kajaani RMi consistency analyzer are presented with results. Control applications are dilution water consistency control, break time headbox ash consistency control and thick stock ash content feedforward control. These applications are used to improve the stability of the machine during normal operation and to recover faster from web breaks.

This paper describes a money paper quality control system based on image processing techniques. The system inspects paper sheet dimensions and verifies whether the money paper has any surface quality or dimensional defect (holes, stains, correct mark positioning,…) while it is moving at a very high speed 20-100 m/min. Due to this high advancing speed of the paper, the high number of inspections to accomplish and the minimum size of the defects to detect (1 mm 2 ), there is no other solution to automate this process but the use of an image processing system based on a high resolution (2048 pixels) lineal camera working at 20 Mhz.

The pressurized headbox is one of the most important pieces of equipment on a modern paper machine. It is a special two-inputs-twooutputs process, and proper matching of input and output of control loops is very important. Relative gain analysis and computer simulation based on a mathematics model of the pressurized headbox shows that the common used configuration in paper industry is not the best. For this kind of configuration, a multi variable predictive control algorithm is designed. Computer simulation shows that this kind of control algorithm can deal with the headbox control very well. A control system with this kind of controller for full scale pressurized headbox has a good performance with excellent control precision, very short response time and very small offsets.

In order to prepare a research project a study was conducted in the pulp and paper industry. The identified problems were around the risks associated with the design of control systems. The investigation suggested that a training program was the best mean to improve the design in order to reduce the risks associated with the considered machinery. This training program would be oriented toward personnel who design or prepare specifications for control systems. This paper will present the result of the study, which will be materialized in the project currently carried out throughout the pulp and paper industry in Quebec.

In papermaking, as in other industrial activities, increasingly stringent customer requirements for product quality and consistency necessitate tighter control of the manufactwing processes. This paper describes a neural network vibrationbased condition monitoring system for providing advance warnings of malfunctions in the paper machine. Spectral analysis of the raw vibration signals involves the technique of enveloping for isolating the spectral components that reflect the process defects. Feature extraction is then carried out by a suitably designed peak detection algorithm. This is followed by a neural network classifier for discriminating between the fault and the no-fault conditions. The system's performance is assessed on industrial data from a major paper manufacturer.

Two methods for instrumental measurement of the dry line have been developed. They are based on innovative arrangements of illumination and related observation under which the pulp on wire is seen by video camera as a composition of dark and light fields. The complete dry line is extracted from the image and used industrially for feedback control of the dry line in machine direction (MD). Recorded correlations between changes of dry line and dry basis weight profile justify conclusions on their control in cross direction (CD) and on incorporation of dry line measurement into conventional MD and CD controls which are handicapped by a long process delay.

This paper is concerned with the task of sensing in web-forming process control loops. Conventional and newly proposed measurement techniques are discussed and a new type of measurement is suggested. The new measurement method adresses the problem of paper machine 'wet-end' sensing and is based on a CCD camera system which can detect the dry-line. Both the theoretical and practical aspects of the invention are investigated and presented using non-parametric techniques based on analysis of variance. Compared to traditional scanning gauge systems the dry-line detector allows for a large reduction in the transport delay, higher data density, and better machine directional (MD) and cross directional (CD) signal decomposition.

Production rates in modern paper machines are increasing all the time. This poses challenging requirements for control of different subprocesses in the paper machine, e.g. for the headbox. Currently, scalar controllers are used for headbox control in industrial practice almost everywhere. Headbox control has been a favorite example of multivariable control in many simulation and laboratory studies but not in industry. This paper presents an application of multivariable PI controller to the paper machine headbox. The procedure takes into account the limited tests that can be performed in practice, simplicity in structure and tuning, and realization using modern distributed automation systems.

This work proposes an expert management system of winding as linked with paper machine production planning and coordination. The integrated management system operates at three levels which receives the information of deckle buckets sent by paper machIne production planning. The main goal of deckle optimization at the highest level is to minimize the paper waste in arranging the reels in a bucket and provide the deckle information to a lower level. The winder optimization at the lower level receives the deckle information. The general goal of winding optimization is to maximize the roll quality and to provide the winding rate equal to the paper machine production. Corresponding to the generated optimum results, the winder optimization block generates the supervisory set point values such as: for web tension, and the target diameter of finished roll, to provide a high quality roll with the desired final diameter.

Retention control has largely remained unsolved, although it is one of the most important control loops still to be closed in the paper machine. In this paper retention modelling is done using multivariable time-series approach. Based on careful selection of vanables and model structure, several new control strategies are developed. Different feedback controls are successfully tested on a pilot paper machine. It is believed to be the first time when successful retention control has been reported in the literature.