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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.

Inspired by the acrobatics of the lizard, we present a novel robot platform capable of performing a barrel roll from a ramp. The system is modeled using Euler-Lagrange mechanics, followed by controller design and numerical simulation. A robotic platform is then designed to perform the experiments. Finally, we show that purely by the use of the actuated tail, the robot can rapidly performing a 360 degree roll rotation in under a second.

By focusing on core competencies and the utilization of international location advantages such as access to low-wage labor and local sales markets, global production networks are becoming increasingly complex. Additionally, turbulent times with great uncertainty forces globally acting enterprises to adapt their production networks. Therefore, it is crucial to determine the right point in time for change and also to identify the beneficial production network alternatives. In other words, globally acting enterprises have to answer the two questions of what to change and when to change. The presented method describes an approach for the strategic planning of global changeable production networks, based on future scenarios and a multi-objective optimization to identify the most favorable network configuration during the planning horizon. The multi-objective optimization is modeled as a mixed-integer MODM problem (multiobjective decision making) including seven criteria i.e. costs, delivery time, quality, flexibility, customer proximity, coordination and plant qualification.

In this paper, an observer-based fault detection scheme is developed for one special kind of distributed parameter systems (DPS) of hyperbolic type. An alternative order reduction approach is utilized to handle the infinite-dimensional nature of DPS for fault detection purpose, compared with the existing modal analysis based method which relies on the existence of eigen-decomposition of the spatial differential operator. The developed fault detection scheme is finally implemented on a simulation model of the paper drying process in paper production industry, and the achieved results demonstrate the effectiveness of the proposed scheme.

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).

Continuous-time nonlinear consensus algorithms with heterogeneous bounded delays are considered. The agents are modeled with single-integrator dynamics, the network topology is undirected but may switch as time progresses and the couplings may be uncertain, but supposed to satisfy a conventional sector condition with known sector slopes. We offer effective analytic condition for the delay bound which guarantees the convergence of such consensus protocols.

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 presents the results of a project aimed at minimising fuel usage while maximising steam availability in the power and steam plant of a large newsprint mill. The approach taken was to utilise the better regulation and plant wide optimisation capabilities of Advanced Process Control, especially Model Predictive Control (MPC) techniques. These have recently made their appearance in the pulp and paper industry but are better known in the oil and petrochemical industry where they have been used for nearly 30 years. The issue in the power and steam plant is to ensure that sufficient steam is available when the paper machines require it and yet not to have to waste too much steam when one or more of the machines suffers an outage. This is a problem for which MPC is well suited. It allows variables to be kept within declared constraint ranges, a feature which has been used, effectively, to increase the steam storage capacity of the existing plant. This has resulted in less steam being condensed when it is not required and in significant reductions in the need for supplementary firing. The incidence of steam being dump-condensed while also supplementary firing the Combined Heat & Power (CHP) plant has been reduced by 95% and the overall use of supplementary firing is less than 30% of what it was. In addition the plant runs more smoothly and requires less operator time. The yearly benefit provided by the control system is greater than £200,000, measured in terms of 2005 gas prices

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.