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Knowledge-Based Intelligent Process Control

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New Approaches in Intelligent Control

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Abstract

In the last decades, the number of process control applications that use intelligent features has increased. This is mainly due to the complex and critical character of the process to be controlled. The intelligent process control systems works better than conventional control schemes in the domains of fault diagnosis (detection, cause analysis and repetitive problem recognition); complex control schemes; process and control performance monitoring and statistical process control; real time Quality Management; control system validation, startup and normal or emergency shutdown. Conventional control technologies use quantitative processing while knowledge-based integrates both qualitative and quantitative processing (having as target the increase of efficiency). This chapter presents an overview of intelligent process control techniques, from rule based systems, frame based systems (object oriented approach), hybrid systems (fuzzy logic and neural network). The focus is on expert systems and their extension, the knowledge based systems. Finally, an industrial case study is presented with conclusions to knowledge based systems limitations and challenges associated to real time implementation of the system.

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References

  1. Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. Artif. Intell. Commun. 7, 39–59 (1994)

    Google Scholar 

  2. Al-Jenani, Nounou, H., Nounou, M.: Fuzzy control of a CSTR process. In: Proceedings of the 8th international symposium on mechatronics and its applications, ISMA 2012, ISBN-978-1-4673-0862-5 (2012)

    Google Scholar 

  3. Babuska, R.: Fuzzy Modelling for Control. Kluwer Academic Publishers (1998)

    Google Scholar 

  4. Behbahani, M., Saghaee, A., Noorossana, R.: A case-based reasoning system development for statistical process control: case representation and retrieval. Comput. Ind. Eng. 63, 1107–1117 (2012)

    Article  Google Scholar 

  5. Bergmann, R., Althoff, K.D., Minor, M., Reichle, M., Bach, K.: Case-based reasoning—introduction and recent developments. Kunstliche Intelligenz: Spec. Issue Case-Based Reasoning 23, 5–11 (2009)

    Google Scholar 

  6. Bhogle, P.P., Patre, B.M., Waghmare, L.M., Panchade, V.M.: Neuro-fuzzy temperature controller. In: Proceedings of IEEE international conference on mechatronics and automation, Harbin, China, p. 3344–3348, ISSN 1-4244-0828-8 (2007)

    Google Scholar 

  7. Bloch, G., Denoeux, T.: Neural networks for process control and optimization: two industrial applications. ISA Trans. 42(1), 39–51 (2003)

    Article  Google Scholar 

  8. Cărbureanu, M., Gheorghe, C.: pH variation in the presence of the coagulants used in oil well industry wastewater treatment. Rev. Chim. 65(12), 1498–1501 (2014)

    Google Scholar 

  9. Cărbureanu, M.: A system for monitoring the effluent’s quality of an industrial wastewater treatment plant. In: Proceedings of the 5th international conference on manufacturing science and education-MSE 2011, pp. 409–412. Sibiu (2011)

    Google Scholar 

  10. Cărbureanu, M.: Sistem expert neuro-fuzzy pentru controlul proceselor de epurare a apelor uzate. Ph.D. Thesis, Petroleum-Gas University (2014a)

    Google Scholar 

  11. Cărbureanu, M.: The development of a neuro-fuzzy expert system for wastewater pH control. J. Control Eng. Appl. Inform. 16(4), 30–41 (2014)

    Google Scholar 

  12. Ćirović, G., Pamučar, D.: Decision support model for prioritizing railway level crossings for safety improvements: application of the adaptive neuro-fuzzy system. Expert Syst. Appl. 40(6), 2208–2223, ISSN 0957-4174 (2013)

    Google Scholar 

  13. Cobaner, M., Citakoglu, H. Kisi, O., Haktanir,T.: Estimation of mean monthly air temperatures in Turkey. Comput. Electron. Agricu. 109, 71–79, ISSN 0168-1699 (2014)

    Google Scholar 

  14. de Canete J.F., del Saz-Orozco, P., Garcia-Cerezo A., Garcia-Moral I.: Neural and genetic control approaches in process engineering, chapter in frontiers in advances control systems. InTech 59–74 (2012)

    Google Scholar 

  15. Dogan, E., Gumrukcuoglu, M., Sandalci, M., Opan, M.: Modelling of evaporation from the reservoir of Yuvacik dam using adaptive neuro-fuzzy inference systems. Eng. Appl. Artif Intell. 23(6), 961–967, ISSN 0952-1976 (2010)

    Google Scholar 

  16. Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Control, 2nd edn. Springer, ISBN 978-3-662-03284-8 (1996)

    Google Scholar 

  17. El-Sayed, M.A.H.: Rule-based approach for real-time reactive power control in interconnected power systems. Expert Syst. Appl. 14(3), 355–360 (1998)

    Article  MathSciNet  Google Scholar 

  18. Fuente, M.J., Robles, C., Casado, O., Syafiie, S., Tadeo, F.: Fuzzy control of a neutralization process. Eng. Appl. Artif Intell. 19, 905–914 (2006)

    Article  Google Scholar 

  19. Giarratano, J.C., Riley, G.D.: Expert Systems—Principles and Programming, 4th edn. Thomson, Boston (2005)

    Google Scholar 

  20. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Massachusetts (1989)

    MATH  Google Scholar 

  21. González, C.A., Acosta, G., Mira, J., de Prada, C.: Knowledge based process control supervision and diagnosis: the AEROLID approach. Expert Syst. Appl. 14(3), 371–383 (1998)

    Article  Google Scholar 

  22. Gupta, A. Rani, A.: Control of distillation process using neuro-fuzzy technique. Int. J. Electr. Data Commun. 1(9) 16–20, ISSN 2320-2084 (2013)

    Google Scholar 

  23. Gurubel, K.J., Sanchez, E.N.: Carlos-Hernandez, neuro-fuzzy control strategy for methane production in an anaerobic process, In: Proceedings of the 2013 International Joint Conference on Neural Networks, p. 1–8, 1467361291 (2013)

    Google Scholar 

  24. Gustafsson, T., Waller, K.: Dynamic modelling and reaction invariant control of pH. Chem. Eng. Sci. 38(3), 389–398 (1983)

    Article  Google Scholar 

  25. Henson, M., Seborg, D.: Adaptive nonlinear control of a pH neutralization process. Control Syst. Technol. 2(3), 169–182 (1994)

    Article  Google Scholar 

  26. Holmblad, L.P., Østergaard, J.J.: The FLS application of fuzzy logic. Fuzzy Sets Syst. 70, 135–146 (1995)

    Article  Google Scholar 

  27. Holmblad, L.P., Østergaard, J.J.: Control of a cement kiln by fuzzy logic. In: Gupta, M.M., Sanchez, E.(eds.) Fuzzy Information and Decision Processes. North-Holland, Amsterdam, pp. 389–399. (Reprint in: FLS Review 67, FLS Automation A/S, Høffdingsvej 77, DK-2500., Valby, Copenhagen, Denmark) (1982)

    Google Scholar 

  28. http://www.gensym.com/

  29. Hussain, M.A.: Review of the applications of neural networks in chemical process control—simulation and online implementation. Artif. Intell. Eng. 13(1), 55–68 (1999)

    Article  Google Scholar 

  30. Ibrahim, R.: Practical modelling and control implementation studies on a pH neutralization process pilot plant. Ph. D. Thesis, Department of Electronics and Electrical Engineering, University of Glasgow (2008)

    Google Scholar 

  31. Jager, R.: Fuzzy logic in control. PhD Thesis, Technische Universiteit Delft, ISBN 90-9008318-9 (1995)

    Google Scholar 

  32. Jana K., Vasičkaninová, A., Dvoran, J.: Neuro-fuzzy control of exothermic chemical reactor, 2013 In: Proceedings of the IEEE International Conference on Process Control, p. 168. Slovakia, ISSN 978-1-4799-0927-8 (2013)

    Google Scholar 

  33. Jang, R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst, Man Cybern. 23(3), 665 (1993)

    Article  Google Scholar 

  34. Jantzen, J.: Foundations of Fuzzy Control. Wiley, ISBN 0-470-02963 (2007)

    Google Scholar 

  35. Kadam D.B., Patil A.B., Paradeshi K.P.: Neural networks based intelligent process control system. Proc. Int. Conf. Recent Trends Inf. Telecommun. Comput. 356–358 (2010)

    Google Scholar 

  36. Kendra, S.J., Basila, M.R., Cinar, A.: Intelligent process control with supervisory knowledge-based systems. IEEE Control Syst 14(3), 37–47 (1994)

    Article  Google Scholar 

  37. Khoshnevisan, B., Rafiee, S., Omid, M., Mousazadeh, H.: Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Inf. Process. Agric. 1(1), 14–22, ISSN 2214-3173 (2014)

    Google Scholar 

  38. Lee, C.C.: Fuzzy logic in control systems: fuzzy logic controller. IEEE Trans. Syst. Man Cybern. 20(2), 404–435 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  39. Liouane, H., Douik, A., Messaoud, H.: Design of optimized state feedback controller using ACO control law for nonlinear systems described by TSK models. Stud. Inf Control 16(3), 307–320 (2007)

    Google Scholar 

  40. Liteanu, C., et al: Quantitative Analytical Chemistry, pp. 178–185. Pedagogic and Didactic Press, Bucharest (1972)

    Google Scholar 

  41. Luca, C., et al: Analytical Chemistry and Instrumental Analysis, pp. 136–146. Pedagogic and Didactic Press, Bucharest (1983)

    Google Scholar 

  42. Marcolla, R.F., Machado, R.A.F., Cancelier, A., Claumann, C.A., Bolzan, A.: Modelling techniques and process control application based on neural networks with on-line adjustment using genetic algorihms. Braz. J. Chem. Eng. 26(1), 113–126 (2009)

    Article  Google Scholar 

  43. Marinoiu, V., Paraschiv, N.: Chemical Processes Automation, vol. 1, pp. 316–325. Technical Press, Bucharest (1992)

    Google Scholar 

  44. Martinez-Zeron, E., Aceves-Fernandez, M.A., Gorrostieta-Hurtado, E., Sotomayor-Olmedo, A., Ramos-Arreguín, J.M.: Method to improve airborne pollution forecasting by using ant colony optimization and neuro-fuzzy algorithms. Int. J. Intell. Sci. 4, 81–90 (2014)

    Article  Google Scholar 

  45. Mathworks: Fuzzy logic toolbox for use with Matlab: user’s guide, version online edition. The MathWorks Inc. Available from http://www.mathworks.com/help/pdf_doc/fuzzy/fuzzy.pdf, (2014)

  46. McAvoy, J., et al.: Dynamics of pH in controlled stirred tank reactor. Chem. Process Des Develop. 11(1), 68–78 (1972)

    Article  Google Scholar 

  47. Mihalache, S.F., Carbureanu, M.: Fuzzy logic controller design for tank level control. Petrol-Gas Univ. Ploiesti Bull., Technical series, LXVI(3) (2014)

    Google Scholar 

  48. Moore, R., Rosenof, H., Stanley, G.: Process control using a real time expert system. Proc. IFAC 1990, 234–239 (1990)

    Google Scholar 

  49. Moreno, J.: Hydraulic plant generation forecasting in Colombian power market using ANFIS, Energy Economics 31(3), 450–455, ISSN 0140-9883 (2009)

    Google Scholar 

  50. Murthy B.V., Kumar Y.V.P., Kumari U.V.R.: Application of neural networks in process control: automatic/online tuning of PID controller gains for ±10 % disturbance rejection. In: Proceedings of the IEEE International Conference on Advanced Communication Control and Computing Technology, pp. 348–352 (2012)

    Google Scholar 

  51. Mwembeshi, M., et al.: An approach to robust and flexible modelling and control of pH in reactors. Chem. Eng. Res. Des. 79(3), 323–334 (2001)

    Article  Google Scholar 

  52. Naitali, A., Giri, F., Radouane, A., Chaoui, F.Z.: Swarm intelligence based partitioning in local linear models identification, pp. 843–848. Proc. of IEEE Int. Symp. Intell., Control (2014)

    Google Scholar 

  53. Narendra, K.S., Mukhopadhyay, S.: Adaptive control of nonlinear multivariable systems using neural networks. Neural Networks 7(5), 737–752 (1994)

    Article  MATH  Google Scholar 

  54. Narendra, K.S., Parthasarathy, K.: Identification and control of dynamically systems using neural networks. IEEE Trans. Neural Networks 1, 4–27 (1990)

    Article  Google Scholar 

  55. Nenițescu, C.: General Chemistry, pp. 482–495. Pedagogic and Didactic Press, Bucharest (1972)

    Google Scholar 

  56. Operating manual of the ECBTAR wastewater treatment from the romanian refinery (2010)

    Google Scholar 

  57. Oprea, M., Dunea, D.: SBC-Mediu: A multi-expert system for environmental diagnosis. Environ. Eng. Manage. J. 9(2), 205–213 (2010)

    Google Scholar 

  58. Oprea, M.: A case study of knowledge modelling in an air pollution control decision support system. AiCommunications 18(4), 293–303 (2005)

    MathSciNet  Google Scholar 

  59. Pedrycz, W.: Fuzzy control and fuzzy systems. 2nd edn. Wiley (1993)

    Google Scholar 

  60. pH/ORP analyzer manual (2014). www.gliint.com. Accessed 15 Sept 2014

  61. Pietrzyk, D. et al: Analytical Chemistry, 2nd edn. pp.111–160. Technical Press, Bucharest (1989)

    Google Scholar 

  62. Rojek, G., Kusiak, J.: Case-based reasoning approach to control of industrial processes. Comput Methods Mater. Sci. 12, 250–258 (2012)

    Google Scholar 

  63. Rojek G.: Agent based systems for assistance at industrial process control with experience modelling. In: Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 1037–1040 (2013)

    Google Scholar 

  64. Rumelhart, D., Widrow, B., Leht, M.: The basic ideas in neural networks. Commun. ACM 37, 87–92 (1994)

    Article  Google Scholar 

  65. Russel S., Norvig P.: Artificial intelligence—a modern approach. Prentice Hall (2010)

    Google Scholar 

  66. See5 (2014). https://www.rulequest.com/See5-info.html. Accessed 10 May 2014

  67. Shiri, J., Kisi, O., Yoon, H., Lee, K.K., Nazemi, A.H.: Predicting groundwater level fluctuations with meteorological effect implications—a comparative study among soft computing techniques. Comput. Geosci. 56, 32–44, ISSN 0098-3004 (2013)

    Google Scholar 

  68. Skoog, D. et al: Fundamentals of analytical chemistry, pp. 182–230. Saunders College Publishing, New York (1988)

    Google Scholar 

  69. Sozen, A., Kurt, M., Akcayol, M.A., Ozalp, M.: Performance prediction of a solar driven ejector-absorption cycle using fuzzy logic. Renew. Energy 29, 53–71 (2004)

    Article  Google Scholar 

  70. Stanley, G.M.: Experiences using knowledge-based reasoning in online control systems. IFAC Symp. Comput. Aided Design Control Syst., Swansea, UK (1991)

    Google Scholar 

  71. Sugeno, M. (ed.): Industrial applications of fuzzy control, ISBN 0444878297 North-Holland (1985)

    Google Scholar 

  72. Tabari, H., Kisi, O., Ezani, A., Talae, P.H.: SVM, ANFIS, regression and climate based models for reference evapotranspiration modelling using limited climatic data in a semi-arid highland environment. J. Hydrol. 444–445, pp. 78–89, ISSN 0022-1694 (2012)

    Google Scholar 

  73. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modelling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985)

    Article  MATH  Google Scholar 

  74. Talei, A., Chua, L.H.C., Wong, T.: Evaluation of rainfall and discharge inputs used by adaptive network-based fuzzy inference systems (ANFIS) in rainfall–runoff modelling. J. Hydrol. 391(3–4) 248–262, ISSN 0022-1694 (2010)

    Google Scholar 

  75. Towell, C.G., Shavlik, J.W.: Knowledge-based artificial neural network. Artif. Intell. 70, 119–165 (1994)

    Article  MATH  Google Scholar 

  76. Uraikul, V., Chan, C.W., Tontiwachwuthikul, P.: Artificial intelligence for monitoring and supervisory control of process systems. Eng. Appl. Artif. Intell. 20, 115–131 (2007)

    Article  Google Scholar 

  77. Vasickaninová, A., Bakošová, M.: Neuro-fuzzy control of integrating processes. Acta Montanistica Slovaca 16(1), 74–83 (2011)

    Google Scholar 

  78. Von Altrock, C.: Fuzzy logic and neurofuzzy applications explained. Prentice Hall (1995)

    Google Scholar 

  79. Wang, F.K., Chang, K.K., Tzeng, C.W.: Using adaptive network-based fuzzy inference system to forecast automobile sales, expert systems with applications, 38(8) 10587–10593, ISSN 0957-4174 (2011)

    Google Scholar 

  80. Wang, L.X.: A course in fuzzy systems and control, international edition, ISBN:0-13-540882-2. Prentice Hall (1997)

    Google Scholar 

  81. Wu, M., She, J.H., Nakano, M.: An expert control system using neural networks for the electrolytic process in zinc hydrometallurgy. Eng. Appl. Artif. Intell. 14(5), 589–598 (2002)

    Article  Google Scholar 

  82. Yurdusev, M. A., Firat, M.: Adaptive neuro fuzzy inference system approach for municipal water consumption modelling: an application to Izmir, Turkey. J. Hydrol. 365(3–4) 225–234, ISSN 0022-1694 (2009)

    Google Scholar 

  83. Zamoum Boushaki R., Chetate B., Zamoum Y.: Artificial neural networks control of the recycle compression system, Studies in Informatics and Control, 23(1), 65–76 (2014)

    Google Scholar 

  84. Zhang, J.: Modelling and optimal control of batch processes using recurrent neuro-fuzzy networks. IEEE Trans. Fuzzy Syst. 13(4), 417–427, ISSN 1063-6706 (2005)

    Google Scholar 

  85. Zhao, Y., Xiong, W.: fuzzy intelligent control of hydrogen purification process In: Proceedings of the International Conference on Intelligent Control and Information Processing, Dalian, China (2010)

    Google Scholar 

  86. Zhihong, M., Yu, X.H., Wu, H.R.: An RBF neural network-based adaptive control for SISO linearisable nonlinear systems. Neural Comput. Appl. 7(1), 71–77 (1998)

    Article  MATH  Google Scholar 

  87. Zmaranda D., Silaghi H., Gabor G., Vancea C.: Issues on applying knowledge-based techniques in real-time control systems. Int. J. Comput. Commun. Control (IJCCC) 8(1), 166–175 (2013)

    Google Scholar 

  88. Zorriassatine, F., Tannock, J.D.T.: A review of neural networks for statistical process control. J. Intell. Manuf. 9, 209–224 (1998)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Automatic Control Engineering, Petroleum-Gas University of Ploiesti, Ploiesti, Romania

    Mihaela Oprea, Sanda Florentina Mihalache & Mădălina Cărbureanu

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  1. Mihaela Oprea
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  2. Sanda Florentina Mihalache
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  3. Mădălina Cărbureanu
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Correspondence to Mihaela Oprea .

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  1. University of Hyogo, Chuo-ku, Japan

    Kazumi Nakamatsu

  2. Technical University of Sofia, Sofia, Bulgaria

    Roumen Kountchev

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Oprea, M., Mihalache, S.F., Cărbureanu, M. (2016). Knowledge-Based Intelligent Process Control. In: Nakamatsu, K., Kountchev, R. (eds) New Approaches in Intelligent Control. Intelligent Systems Reference Library, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-319-32168-4_7

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