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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.