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This paper studies automated tuning of cross-directional model predictive control for industrial paper-making processes under user-specified model parameter uncertainties. Automated parameter tuning algorithms are developed to reduce the variability of the actuator and measurement profiles in the spatial domain and to achieve satisfactory performance in terms of worst case settling times and worst case control signal overshoots in the temporal domain for given parametric uncertainties. Due to decoupling properties of the spatial and temporal frequency components, the controller design and parameter tuning can be realized separately. For the spatial design and parameter tuning in the presence of parametric uncertainties, the undesirable high-frequency components in the actuator profile are suppressed via an appropriate design of the weighting matrix $S_{b}$ using the real-valued Fourier matrix approach. For the temporal design, a temporal filter is adopted to smooth the reference trajectory, where the parameter in the temporal filter is carefully tuned to achieve a tradeoff between the worst case settling time and the worst case control signal overshoot. Finally, the effectiveness of the proposed tuning algorithms is verified using a system model extracted from the pulp and paper industry. [ABSTRACT FROM AUTHOR]

Paper machines are very complex production systems, but their scope is simple: they consume materials and resources, called factors, to produce paper, which in turn can be described by its characteristics. In this paper, a decision support system is developed in cooperation with an industrial partner to help them with operational decision making when tuning a paper machine. The decision support system was developed in two phases. Firstly, the knowledge of experts is collected and stored in the form of a fuzzy ontology. Secondly, this knowledge is made usable so that a user of the decision support system can specify what characteristics of the produced paper to increase or to decrease and be returned with a recommendation on what factors to change. In this paper, we will work out the optimization problems on which the system is based. Additionally to a basic goal programming model, two extensions are explored, accounting for uncertainty and non-linearity, respectively. [ABSTRACT FROM AUTHOR]

This paper solves the controller tuning problem of machine-directional predictive control for multiple-input–multiple-output (MIMO) paper-making processes represented as superposition of first-order-plus-dead-time (FOPDT) components with uncertain model parameters. A user-friendly multi-variable tuning problem is formulated based on user-specified time domain specifications and then simplified based on the structure of the closed-loop system. Based on the simplified tuning problem and a proposed performance evaluation technique, a fast multi-variable tuning technique is developed by ignoring the constraints of the MPC. In addition, a technique to predict the computation time of the tuning algorithm is proposed. The efficiency of the proposed method is verified through Honeywell real time simulator platform with a MIMO paper-making process obtained from real data from an industrial site. [ABSTRACT FROM AUTHOR]

Model-based controllers based on incorrect estimates of the true plant behaviour can be expected to perform poorly. This work studies the effect of model plant mismatch on the closed loop behaviour and system performance for a certain class of MIMO systems. Performance is measured using a minimum variance index and a closely related user-specified criterion. We study the effect of model plant mismatch on the output variance and performance indices. Under mild assumptions, the performance of each output in a MIMO system can be analysed independently. Moreover, we propose an approach to distinguish the effect of model–plant mismatch from the effect of changes in disturbance characteristics on closed-loop performance. We define a sensitivity measure that relates system performance to model–plant mismatch, and use it to explore this sensitivity for three realistic types of parametric modelling errors. Next, we suggest a quantitative method that compares a system's actual output to its desired response in a transient setting. The performance of the transient response is demonstrably more sensitive to the model–plant mismatch than the steady state performance. The results are illustrated on industrial paper machine data. [ABSTRACT FROM AUTHOR]

The objective of this paper is to provide the physical bases that allow more accurate diagnosis of the conditions of paper machines for better maintenance. The diagnostic of the mechanical condition of press felts and rolls in the press section of paper machines using the classical vibration analysis is complex, so maintenance of these elements is done in a preventive and not predictive way. In this work a mathematical model that describes the dynamic behavior of the presses in paper machines is developed. Through numerical simulations, the motion equation of the proposed model is solved and compared with measurements done in industrial machines. It is concluded that the proposed model properly describes the vibratory behavior of presses and provides the vibratory symptoms that allow the diagnosis of the problems that they may have. [ABSTRACT FROM AUTHOR]

Formation of biofilms causes severe problems in paper machines, and hence financial costs. It would be preferable to prevent attachment of the primary-colonizing bacteria than to control the growth of secondary communities, which are sheltered by exopolysaccharide slime layers. We have therefore investigated the early succession of paper-machine biofilms by incubating stainless-steel test coupons in the process water-flow lines in two paper machines operating in slightly alkaline conditions in temperatures (45 and 49°C) supporting thermophilic microbes. Microbial succession was profiled using length heterogeneity analysis of PCR-amplified 16S rRNA genes (LH-PCR) and linking the sequence data of the created 16S rRNA gene libraries to the dominant LH-PCR peaks. Although the bacterial fingerprints obtained from the attached surface communities varied slightly in different samples, the biomarker signals of the dominating primary-colonizing bacterial groups remained high over time in each paper machine. Most of the 16S rRNA gene copies in the early biofilms were assigned to the genera Rhodobacter, Tepidimonas, and Cloacibacterium. The dominance of these sequence types decreased in the developing biofilms. Finally, as phylogenetically identical primary-colonizers were detected in the two different paper mills, the machines evidently had similar environmental conditions for bacterial growth and potentially a common source of contamination. [ABSTRACT FROM AUTHOR]

Abstract: Good cross-directional control of sheet variables in a paper machine depends on a number of factors. One such factor is the correct alignment of actuators with their corresponding responses. The alignment changes with time either locally or across the whole paper sheet due to a variety of reasons. In this paper, we provide an online mapping technique based on estimating the cross-correlation between actuator movement and the measurements. This procedure involves two steps–in the first step (called alignment monitoring) we determine if the current mapping is wrong at any CD position, and in the second step (called re-mapping), the alignment of actuators is corrected. The main advantage of this method is in its ability to re-align under closed loop conditions with minimal operational disruption. [Copyright &y& Elsevier]

Abstract: The heat recovery system (HRS) is a vital part in a paper machine when it comes to the overall energy economy in papermaking. For a typical newsprint machine more than 60% of the exhaust energy from the dryer section can be recovered, resulting typically in a regain of about 30MW. The synthesis task of a HRS is a decision process where the target is on the one hand to achieve maximal energy recovery and on the other hand to obtain it with minimal investment costs. These goals are contradictory and thus the problem is to find a solution minimizing the overall costs, considering simultaneously both energy and investment costs. One further challenge is how to take into account known variations in the parameters so that the design is robust, i.e. it is capable of handling all evolving situations and furthermore the design should be the most economical one when also considering the duration of all expected operational situations. In this work a hybrid method based on evolutionary programming and non-linear programming for the synthesis of robust and optimal heat recovery systems (HRS) for paper machines is presented. Variations and uncertainties on process parameters are modeled with probability distributions and the cost of the utilities are thus obtained as an integrated value of all different situations expected to evolve. The importance of obtaining a robust design is shown in a case study. [Copyright &y& Elsevier]