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Capturing Connectivity and Causality from Process Knowledge

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Capturing Connectivity and Causality in Complex Industrial Processes

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

Abstract

Process knowledge is the most reliable resource for qualitative modeling of complex industrial processes, which is typically expressed in natural language and stored in human brains. We thus need to capture useful connectivity and causality from such resources and convert the information into computer accessible formats. From first-principle structural models, causality can be captured and expressed as structural equations. From unstructured process knowledge and dynamic and algebraic equations, graphical models, in particular signed directed graphs and variants, can be obtained. Graphic models are widely used due to their computer tractability and human readability. Rule-based models are another alternative, which is used in expert systems. When the process information is accessible in web language, connectivity can be retrieved by query.

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References

  1. Alonso CJ, Llamas C, Maestro JA, Pulido B (2003) Diagnosis of dynamic systems: a knowledge model that allows tracking the system during the diagnosis process. Lect Notes Artif Intell 2718(6):208–218

    Google Scholar 

  2. Bauer M, Cox JW, Caveness MH, Downs JJ, Thornhill NF (2007) Finding the direction of disturbance propagation in a chemical process using transfer entropy. IEEE Trans Control Syst Technol 15(1):12–21

    Article  Google Scholar 

  3. Chang CC, Yu CC (1990) On-line fault diagnosis using the signed directed graph. Ind Eng Chem Res 29(7):1290–1299

    Article  Google Scholar 

  4. Cheng H, Tikkala VM, Zakharov A, Myller T, Jamsa-Jounela SL (2011) Application of the enhanced dynamic causal digraph method on a three-layer board machine. IEEE Trans Control Syst Technol 19(3):644–655

    Article  Google Scholar 

  5. Di Geronimo Gil GJ, Alabi DB, Iyun OE, Thornhill NF (2011) Merging process models and plant topology. In: Proceedings of 4th advanced control of industrial processes, Hangzhou, China

    Google Scholar 

  6. Fagarasan I, Ploix S, Gentil S (2004) Causal fault detection and isolation based on a set-membership approach. Automatica 40(12):2099–2110

    MATH  MathSciNet  Google Scholar 

  7. Fedai M, Drath R (2005) CAEX—a neutral data exchange format for engineering data. ATP Int Autom Technol 3(1):43–51

    Google Scholar 

  8. Friston KJ, Harrison L, Penny W (2003) Dynamic causal modelling. NeuroImage 19(4):1273–1302

    Article  Google Scholar 

  9. Gao D, Wu C, Zhang B, Ma X (2010) Signed directed graph and qualitative trend analysis based fault diagnosis in chemical industry. Chinese J Chem Eng 18(2):265–276

    Article  Google Scholar 

  10. Jan A, Jonas B, Erik F, Krysander M, Lars N (2007) Safety analysis of autonomous systems by extended fault tree analysis. Int J Adapt Control Signal Process 21(2–3):287–298

    MATH  Google Scholar 

  11. Kramer MA, Palowitch BL Jr (1987) A rule-based approach to fault diagnosis using the signed directed graph. AIChE J 33(7):1067–1078

    Article  MathSciNet  Google Scholar 

  12. Leyval L, Gentil S, Feray-Beaumont S (1994) Model based causal reasoning for process supervision. Automatica 30(8):1295–1306

    Article  MATH  MathSciNet  Google Scholar 

  13. Mah RSH (1989) Chemical process structures and information flows. Butterworth, Boston, MA

    Google Scholar 

  14. Maurya MR, Rengaswamy R, Venkatasubramanian V (2003) A systematic framework for the development and analysis of signed digraphs for chemical processes. 1. Algorithms and analysis. Ind Eng Chem Res 42(20):4789–4810

    Article  Google Scholar 

  15. Maurya MR, Rengaswamy R, Venkatasubramanian V (2003) A systematic framework for the development and analysis of signed digraphs for chemical processes. 2. Control loops and flowsheet analysis. Ind Eng Chem Res 42(20):4811–4827

    Article  Google Scholar 

  16. Maurya MR, Rengaswamy R, Venkatasubramanian V (2007) A signed directed graph and qualitative trend analysis-based framework for incipient fault. Chem Eng Res Des 85(10):1407–1422

    Article  Google Scholar 

  17. Montmain J, Gentil S (2000) Dynamic causal model diagnostic reasoning for online technical process supervision. Automatica 36(8):1137–1152

    Article  MATH  MathSciNet  Google Scholar 

  18. Mosterman PJ, Biswas G (1999) Diagnosis of continuous valued systems in transient operating regions. EEE Trans Syst Man Cybern Part A 29(6):554–565

    Article  Google Scholar 

  19. Oyeleye OO, Kramer MA (1988) Qualitative simulation of chemical process systems: steady-state analysis. AIChE J 34(9):1441–1454

    Article  Google Scholar 

  20. Pastor J, Lafon M, Trave-Massuyes L, Demonet JF, Doyon B, Celsis P (2000) Information processing in large-scale cerebral networks: the causal connectivity approach. Biol Cybern 82(1):49–59

    Article  MATH  Google Scholar 

  21. Paynter HM (1960) Analysis and design of engineering systems. MIT Press, Cambridge, MA

    Google Scholar 

  22. Pearl J (2009) Causality: models, reasoning, and inference, 2nd edn. Cambridge University Press, Cambridge, UK

    Book  Google Scholar 

  23. Shiozaki J, Matsuyama H, O’Shima E, Iri M (1985) An improved algorithm for diagnosis of system failures in the chemical process. Comput Chem Eng 9(3):285–293

    Article  Google Scholar 

  24. Thambirajah J, Benabbas L, Bauer M, Thornhill NF (2009) Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history. Comput Chem Eng 33(2):503–512

    Article  Google Scholar 

  25. Wright S (1921) Correlation and causation. J Agric Res 20:557–585

    Google Scholar 

  26. Yang F, Xiao D (2005) Approach to modeling of qualitative SDG model in large-scale complex systems. Control Instrum Chem Ind 32(5):8–11

    Google Scholar 

  27. Yang F, Xiao D (2006) Approach to fault diagnosis using SDG based on fault revealing time. Proceedings of 6th world congress on intelligent control and automation, Dalian, China, pp 5744–5747

    Google Scholar 

  28. Yang F, Shah SL, Xiao D (2009) SDG model-based analysis of fault propagation in control systems. Proceedings of 2009 Canadian conference on electrical and computer engineering, St John’s, NL, Canada, pp 1152–1157

    Google Scholar 

  29. Yang F, Shah SL, Xiao D (2012) Signed directed graph based modeling and its validation from process knowledge and process data. Int J Appl Math Comput Sci 22(1):41–53

    MATH  MathSciNet  Google Scholar 

  30. Yang F, Xiao D, Shah SL (2013) Signed directed graph-based hierarchical modelling and fault propagation analysis for large-scale systems. IET Control Theory Appl 7(4):537–550

    Article  MathSciNet  Google Scholar 

  31. Yim SY, Ananthakumar HG, Benabbas L, Horch A, Drath R, Thornhill NF (2006) Using process topology in plant-wide control loop performance assessment. Comput Chem Eng 31(2):86–99

    Article  Google Scholar 

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Authors and Affiliations

  1. Tsinghua Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China

    Fan Yang

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Ping Duan & Tongwen Chen

  3. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada

    Sirish L. Shah

Authors
  1. Fan Yang
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  2. Ping Duan
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  3. Sirish L. Shah
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  4. Tongwen Chen
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Correspondence to Fan Yang .

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Yang, F., Duan, P., Shah, S.L., Chen, T. (2014). Capturing Connectivity and Causality from Process Knowledge. In: Capturing Connectivity and Causality in Complex Industrial Processes. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-05380-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-05380-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05379-0

  • Online ISBN: 978-3-319-05380-6

  • eBook Packages: EngineeringEngineering (R0)

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