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  • 1.
    Björkqvist, Olof
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences, Engineering and Mathematics.
    Engstrand, Per
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences, Engineering and Mathematics.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences, Engineering and Mathematics.
    Concepts of Steam Recovery from LC-Refining by Increase Temperature2009In: Proceedings - 2009 International Mechanical Pulping Conference, IMPC 2009, 2009, p. 190-194Conference paper (Refereed)
    Abstract [en]

    In a mechanical pulping process, (TMP) wood is refined to pulp in a process with very high wood utilization. However, the power demand in the process is high. Thus efficient energy recovery, especially steam recov-ery, is very important. In high consistency (HC) refining the pulp wood is refined at high temperature (140°C) and pressure. The high temperature makes it possible to recover process heat with usable steam properties.

    One strategy to decrease the power consumption is to split the refining into two stages, one HC-stage and one low consistency (LC) refining stage. This kind of sys-tem is quite common today. One drawback with LC-refining is that it operates at a low temperature normally below 100°C. Hence, the steam recovery potential from conventional LC-refining is limited.

    In this project, we analyse three concepts of steam re-covery in LC-refining by increasing the temperature in the LC-stage. Two base cases: Conventional HC refin-ing only and conventional HC/LC refining is compared with three steam recovery cases: Pulp/Pulp heat ex-changing, Screw Press Dewatering combined with proc-ess water re-circulation and finally Pulp/Water Heat Exchanging.

    The study shows that it is possible to recover steam from the LC-stage and, hence, increase the energy effi-ciency of a combined HC/LC refining system. The screw press case has the highest steam recovery poten-tial of the HC/LC configurations. An initial economic estimate indicates that steam recovery in LC-refining is profitable compared to a conventional HC/LC-configuration.

  • 2.
    Björkqvist, Olof
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences, Engineering and Mathematics.
    Engstrand, Per
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences, Engineering and Mathematics.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering and Sustainable Development.
    Energiåtervinning vid LC-raffinering – Förstudie2008Report (Other academic)
    Abstract [en]

    Normally, steam recovery from a conventional low consistency (LC) mechanical pulprefining system is not possible. This is due to the fact that the temperature level in theLC-refiner is less than 100°C. The steam with such a low temperature and associatedpressure has limited value in the mill. In this project, we study a concept of increasingthe temperature in the refiner to a level were process steam with higher quality can berecovered. The temperature level can be increased by transferring heat from outgoingpulp or drainage to incoming pulp or water. This makes it possible to recover heat fromthe process.An initial estimate indicates that steam recovery from LC-refining systems may have agood economic potential. Three cases have been analyzed: Case A: Steam recovery incombination with pulp/pulp heat exchanging, Case B: Steam recovery in combinationwith a pressurized screw press and finally Case C: steam recovery in combination withpump/water heat exchanging.Case B show the best specific steam recovery, 87% kWh recovered steam per kWh usedelectricity. This concept has a lower technological uncertainty compared to cases A andC as it does not need heat exchanging from pulp.The specific heat recovery from case A and C is 78% and 82% respectively. However,the suggested heat exchangers used in these cases do not exist on the market today.There is hence a need for development of exchangers that can handle pulp with highviscosity. The technological risk associated with the screw press scenario is lower and itis likely that this concept is easier to implement.

  • 3.
    Friden, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    The hydrodynamics of the cerebrospinal fluid in man. Instrumentation, computer methods, investigation procedures and physiological modelling1994Doctoral thesis, monograph (Other scientific)
  • 4.
    Fridén, Hakan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Virtual Control Room (VCR) - NPI - Status October 20042004Report (Other (popular science, discussion, etc.))
  • 5.
    Fridén, Häkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Process modelling - a future part in an operator's guide: Automatiseringsdagarna 2004 in Storlien2004Conference paper (Other scientific)
  • 6.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Model based process monitoring, control and optimization in real time: ÅF:s Automationsdagar 20052005Conference paper (Other academic)
  • 7.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Multivariate batch modelling of baker's yeast production: CAC96 - International Conference Chemometrivs in Analytical Chemistry, Tarragona1996Conference paper (Other scientific)
  • 8.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    On-line multivariate statistical process control for continuous and batch processes2000In: Proceedings of ACHEMA 2000, 2000Conference paper (Other scientific)
  • 9.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Process modelling - a future part in an operators guide.: The FlowMac Conference year 2004, Kolmården, June 15-162004Conference paper (Other scientific)
  • 10.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Process Modelling in Real time with Multivariate Models: ProcessTeknik 2004, Göteborg 5-7 oktober2004Conference paper (Other scientific)
  • 11.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Virtual Control Room (VCR) : NPI - Project Report Sep 20052005Book (Other scientific)
  • 12.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Virtuellt Kontrollrum (VKR) - NPI - Status mars 20042004Report (Other (popular science, discussion, etc.))
    Abstract [sv]

    Rapporten beskriver driftsättningen av VKR hos NPI. Den redogör för installerad hård- och mjukvara, samt vilka tillämpningar som är aktuella.

  • 13.
    Fridén, Håkan
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Näslund, Henrik
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Stattin, Andreas
    Löfgren, Göran
    Online decision support and optimization for kraft paper production2006In: Control systems 2006 : Measurement and control: applications for the operator, Tampere, Finland, 6-8 June 2006, Helsinki: Kopio Niini - Painoporssi, , 2006, p. 313-Conference paper (Other scientific)
    Abstract [en]

    The MondiPackaging Dynäs pulp & paper mill in Sweden produces strong sack and kraft paper from chemical softwood pulp. Despite good quality a project was initiated by a need for less variability in paper quality, and less energy consumption. It was known that there is variability in incoming stock, and that the HC and LC refining strategies could be improved. The aim of the project is to reduce energy consumption by 10%, and to reduce quality variation by installing a new online fibre analyzer, PulpEye, and implementing modern modelling and control methodology. We have used multivariate subspace models in combination with optimization to create an OPC based interactive application for on-line operator support and optimization of kraft paper production. The application is now installed and is under evaluation. The specific refiner energy has so far been reduced with at least 25%. The decision support application is indicating even more reduction potential.

  • 14.
    Fridén, Håkan
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering and Sustainable Development.
    Stattin, Andreas
    Löfgren, Göran
    Online operator decision support and optimization for sack paper production by PAT based process automation2008In: Third pan-European PAT conference: Oct 7-8, Gothenburg, Sweden, 2008Conference paper (Other academic)
  • 15.
    Fridén, Håkan
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Tano, Kent
    Using PLS models with both controlled and uncontrolled X variables for "Waht if..." prediction2005In: The 9th Scandinavian Symposium on Chemometrics, Reykjavik, Iceland 2005-09-30, Ornsköldsvik: NPI , 2005, p. 6-Conference paper (Other scientific)
    Abstract [en]

    In real-time industrial process applications, there is often a need to predict the effects of adjustments before actually effectuated. Multivariate prediction models based on historical data works fine for predictions as long as the new X data have the correlation structure preserved. This paper shows that such can anyway be used for �What if �� decision support in presence of correlated X variables, whenever it is possible to create a help model that predicts the influence of the manipulated X variables on the rest of the X variables.

  • 16.
    Fridén, Håkan
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Wikström, Conny
    Modellering ger kontroll över batchprocesser1999In: Kemisk Tidskrift - Kemivärlden, ISSN 1104-2788, no 5, p. 41-47Article in journal (Other academic)
  • 17.
    Fridén, Håkan
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Wikström, Conny
    Multivariate process monitoring and forcasting by projection methods1994In: Proceedings of the Third IEEE Conference on Control Applications Glasgow, 24-26 aug 1994: Vol. 2, Piscataway: IEEE Control Systems Society , 1994, p. 1345-1346Conference paper (Refereed)
    Abstract [en]

    Immense amounts of data are collected into today's modern process monitoring systems. There are, however, few methods that have the capability to grasp the essentials in these, usually heavily correlated, data. The multivariate statistical techniques, principal components (PC) modelling and modelling by projection to latent structures (PLS) are two methods that have a great potential for process monitoring and forecasting in these situations

  • 18. Kattaneh, Nouna
    et al.
    Albano, C
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Data pre-processing, wavelets, and OSC2000In: International Meeting on Chemical Engineering, Environmental Protection and Biotechnology. ACHEMA 2000, Frankfurt am Main, 22. – 27. 5. 2000, 2000Conference paper (Other scientific)
  • 19. Kettaneh, Nouna
    et al.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Lindgren, Å
    Multivariate modelling of batch processes2003In: Proceedings. 8th Scandinavian symposium on chemometrics Mariehamn, Sweden, 14–18 June 2003, 2003Conference paper (Other academic)
  • 20.
    Pavlenko, Tatjana
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Scoring Feature Subsets for Separation power in Supervised Bayes Classification2006In: Advances in Intelligent and Soft Computing, ISSN 1867-5662, E-ISSN 1867-5670, Vol. 37, p. 383-391Article in journal (Refereed)
    Abstract [en]

    We present a method for evaluating the discriminative power of compact feature combinations (blocks) using the distance-based scoring measure, yielding an algorithm for selecting feature blocks that significantly contribute to the outcome variation. To estimate classification performance with subset selection in a high dimensional framework we jointly evaluate both stages of the process: selection of significantly relevant blocks and classification. Classification power and performance properties of the classifier with the proposed subset selection technique has been studied on several simulation models and confirms the benefit of this approach.

  • 21. Wikström, Conny
    et al.
    Albano, Christer
    Eriksson, Lennart
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Johansson, Erik
    Nordahl, Åke
    Rännar, Stefan
    Sandberg, Maria
    Kettaneh-Wold, Nouna
    Wold, Svante
    Multivariate process and quality monitoring applied to an electrolysis process.: Part I - Process supervision with multivariate control charts1998In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, Vol. 42, no 1-2, p. 221-231Article in journal (Refereed)
    Abstract [en]

    Multivariate statistical process control MSPC.is applied to an electrolysis process. The process produces extremely pure copper, and to monitor its quality the levels of eight metal impurities were recorded twice a day. These quality data are analysed adopting an 1. ‘intuitive’ univariate approach, and 2. with multivariate techniques. It is demonstrated that the univariate analysis gives confusing results with regards to outlier detection, while the multivariate approach identifies two types of outliers. Moreover, it is shown how the results from the multivariate principal component analysis PCA.method can be displayed graphically in multivariate control charts. Multivariate Shewhart, cumulative sum CUSUM.and exponentially weighted moving average EWMA.control charts are used and compared. Also, an informationally powerful control chart, the simultaneous scores monitoring and residual tracking SMART.chart, is introduced and used.

  • 22. Wikström, Conny
    et al.
    Albano, Christer
    Eriksson, Lennart
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Johansson, Erik
    Nordahl, Åke
    Rännar, Stefan
    Sandberg, Maria
    Kettaneh-Wold, Nouna
    Wold, Svante
    Multivariate process and quality monitoring applied to an electrolysis process.: Part II - Multivariate time-series analysis of lagged latent variables1998In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, Vol. 42, no 1-2, p. 233-240Article in journal (Refereed)
    Abstract [en]

    Multivariate time series analysis is applied to understand and model the dynamics of an electrolytic process manufacturing copper. Here, eight metal impurities were measured, twice daily, over a period of one year, to characterize the quality of the copper. In the data analysis, these eight variables were summarized by means of principal component analysis PCA.. Two principal component PC.scores were sufficient to well summarize the eight measured variables R2s0.67.. Subse-quently, the dynamics of these PC-scores latent variables.were investigated using multivariate time series analysis, i.e., par-tial least squares PLS.modelling of the lagged latent variables. Stochastic models of the auto-regressive moving average ARMA.family were appropriate for both PC-scores. Hence, the dynamics of both scores make the exponentially weighted moving average EWMA.control chart suitable for process monitoring.

  • 23. Wold, S.
    et al.
    Albano, C.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Adaptive, robust, and non-linear PCA and PLS methods for on-line applications2000In: Proceedings of ACHEMA 2000, 2000Conference paper (Other scientific)
  • 24. Wold, Svante
    et al.
    Kettane, Nouna
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Holmberg, Andrea
    Modelling and diagnostics of batch processes and analogous kinetic experiments. 1998In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, Vol. 44, no 1/2, p. 331-340Article in journal (Refereed)
    Abstract [en]

    In chemical kinetics and batch processes K variables are measured on the batches at regular time intervals. This gives a J×K matrix for each batch (J time points times K variables). Consequently, a set of N normal batches gives a three-way matrix of dimension (N×J×K). The case when batches have different length is also discussed. In a typical industrial application of batch modelling, the purpose is to diagnose an evolving batch as normal or not, and to obtain indications of variables that together behave abnormally in batch process upsets. Other applications giving the same form of data include pharmaco-kinetics, clinical and pharmacological trials where patients (or mice) are followed over time, material stability testing and other kinetic investigations. A new approach to the multivariate modelling of three-way kinetic and batch process data is presented. This approach is based on an initial PLS analysis of the ((N×J)×K) unfolded matrix ((batch×time)×variables) with ‘local time' used as a single y-variable. This is followed by a simple statistical analysis of the resulting scores and results in multivariate control charts suitable for monitoring the kinetics of new experiments or batches. ‘Upsets' are effectively diagnosed in these charts, and variables contributing to the upsets are indicated in contribution plots. In addition, the degree of ‘maturity' of the batch can be as predicted vs. observed local time. The analysis of batch data with respect to various questions is discussed with respect to typical objectives, overview and summary, classification, and quantitative modelling. This is illustrated by an industrial example of yeast production.

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