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Virtual Analyzers: Identification Approach

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Abstract

A definition of virtual analyzers as software algorithmic systems generating models in real time on the basis of current and retrospective information about the industrial processes was given. Methods of development of the virtual analyzers were presented, as well as examples of their industrial applications.

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REFERENCES

  1. Dubinin, V.A., Informatsionnyi menedzhment--fantom, obretayushchii plot' (Information Management is a Phantom Assuming Flesh), Moscow: Planeta KIS, 2001.

    Google Scholar 

  2. Kulikov, V.N., Strategy of Development of the Industrial Information Technologies, Mir Komp'yuternoi Avtomatizatsii, 2001, no. 4, pp. 12–15.

    Google Scholar 

  3. Afanas'ev, V.N. and Postnikov, A.I., Informatsionnye tekhnologii v upravlenii predpriyatiem (Information Technologies in Industrial Control), Moscow: MGIEM, 2003.

    Google Scholar 

  4. Hoske, M.T., How to Integrate Software, Control Engineering, 2000, no. 11.

  5. Nesterova, A., MES--Industrial Control Systems. Make use of Evident Advantages, Mir Komp'yuternoi Avtomatizatsii, 2001, no. 4, pp. 24–26.

    Google Scholar 

  6. Musaev, A.A., Virtual Analyzers: The Concept of Design and Use in the Problems of Continuous Process Control, Avtomatiz. Promyshl., 2003, no. 8, pp. 28–33.

    Google Scholar 

  7. Harrold, D., Process Control's Latest Tool: Soft Sensors, Control Eng. Eur., June/July 2001, pp. 42–45.

  8. Ljung, L., System Identification: Theory for the User, Englewood Cliffs: Prentice Hall, 1987. Translated under the title Identifikatsiya sistem. Teoriya dlya pol'zovatelya, Moscow: Nauka, 1991.

    Google Scholar 

  9. Sorkin, L.R., Achievements of the Trapeznikov Institute of Control Sciences in the Development and Introduction of the Information Control Technologies in the Oil and Gas System, in: Plenarnye dokl. Mezhdunar. konf. po problemam upravleniya (Int. Conf. on Control. Plenary Papers), Moscow: Inst. Probl. Upravlen., 1999, pp. 172–179.

    Google Scholar 

  10. Dozortsev, V.M., Kneller, D.V., and Levit, M.Yu., On Adequacy of the Process Simulators, in: Plenarnye dokl. Mezhdunar. konf. "Identifikatsiya sistem i zadachi upravleniya" (Pleanry Papers. Int. Conf. "System Identification and Control Problems"), Moscow: Inst. Probl. Upravlen., 2000.

    Google Scholar 

  11. Tumanov, N.A., Tumanov, D.N., Chadeev, V.M., and Bakhtadze, N.N., Virtual Analyzer-Based Control Systems for Production of Mineral Fertilizers, Avtomatiz. Promyshl., 2003, no. 8, pp. 33–36.

    Google Scholar 

  12. Osnovy upravleniya tekhnologicheskimi protsessami (Process Control Fundamentals), Raibman, N.C., Ed., Moscow: Nauka, 1978.

    Google Scholar 

  13. Tsypkinh, Ya.Z., Control of Dynamic Objects under Bounded Uncertainty, Izmereniya, Kontrol', Avtomatizatsiya, 1991, no. 3–4, pp. 3–21.

    Google Scholar 

  14. Fomin, V.N., Fradkov, A.L., and Yakubovich, V.A., Adaptivnoe upravlenie dinamicheskimi ob"ektami (Adaptive Control of Dynamic Objects), Moscow: Nauka, 1981.

    Google Scholar 

  15. Bartos, F.J., Artificial Intelligence: Smart Thinking for Complex Control, Control Eng., 1997, no. 7.

  16. Zadeh, L.A., Fuzzy Sets and Applications, New York: Wiley, 1987.

    Google Scholar 

  17. Zadeh, L.A., Fuzzy Sets, Inform. Control, 1965, no. 6, pp. 338–353.

    Google Scholar 

  18. Zadeh, L.A., Outline of a New Approach to the Analysis of Complex Systems and Decision Processes, IEEE Trans. Syst. Man Cybernetics, 1973, no. 1, pp. 28–44.

    Google Scholar 

  19. Masalovich, A.I., Computer... Predicts, Softmarket, 1996, no. 23, pp. 6–9.

    Google Scholar 

  20. Nechetkie mnozhestva v modelyakh upravleniya i iskusstvennogo intellekta (Fuzzy Sets in Models of Control and Atrificial Intelligence), Pospelov, D.A., Ed., Moscow: Nauka, 1986.

    Google Scholar 

  21. Kosko, B., Fuzzy Thinking, New York: Hyperion, 1992.

    Google Scholar 

  22. Kosko, B., Neural Networks and Fuzzy Systems. A Dynamic System Approach to Machine Intelligence, New Jercey: Prentice Hall, 1992.

    Google Scholar 

  23. Zemankova-Leech, M. and Kandel, A., Fuzzy Relational Data Bases: A Key to Expert Systems, Cologne: TUV Rheinland, 1984.

    Google Scholar 

  24. Karpenko, A.S., Multivalued Logics, Logika Komp'yuter, 1997, no. 4.

  25. McNeill, D. and Freiberger, P., Fuzzy Logic, New York: Simon and Schuster, 1993.

    Google Scholar 

  26. Shtovba, S.D., Introduction to the Fuzzy Set Theory and Fuzzy Logic, in: Tez. dokl. Vseros. nauch. konf. "Proektirovanie nauchnykh and inzhenernykh prilozhenii v srede MATLAB" (Abstracts of Paper Russian Conf. "Design of Scientific and Engineering Applications in the MATLAB Environment"), Moscow: Inst. Probl. Upravlen., 2002.

    Google Scholar 

  27. Mamdani, E.H., Application of Fuzzy Algorithms for the Control of a Simple Dynamic Plant, Proc. IEEE, 1974, vol. 121, pp. 371–378.

    Google Scholar 

  28. Kutukov, S.E. and Vasil'ev, V.I., Elements of Artificial Intelligence in Systems of Collection, Preparation, and Transportation of Hydrocarbon Materials (http://www.ogbus.ru/authors/kutukov/kuts.pdf).

  29. Gorban', A.N., Vozmozhnosti neironnykh setei. Neiroinformatika (Potentialities of the Neural Networks. Neural informatics), Novosibirsk: Nauka, 1998.

    Google Scholar 

  30. Blum, F., Leiserson, A., and Hofstedter L., A Brain, Mind and Behavior, Moscow: Mir, 1988, pp. 53–80.

    Google Scholar 

  31. Makhotilo, K.V., Analysis of Parametric Sensitivity of the Neural Network Control System, Tr. Mezhdunar. nauch.-tekh. konf. "MicroCAD'97," Inform. tekhnologii: nauka, tekhnika, tekhnologiya, obrazovanie, zdorov'e (Proc. Int. Conf. "microCAD'97," Information Technologies, Education, Health), Khar'kov: KHGPU, 1997, pp. 137–141.

    Google Scholar 

  32. Kashchavtsev, S., Personal Intelligence, Komp'yuterra, 2002, no. 20.

  33. Galushkin, A.I., Current Directions of Development of the Neural Computer Technologies in Russia, Otkrytye Sistemy, 1997.

  34. McCulloch, W.W. and Pitts, W., A Logical Calculus of the Ideas Imminent in Nervous Activity, Bulletin of Mathematical Biophysics, 1943, no. 5, pp. 115-33. Translated in Avtomaty, Shannon, C.E. and McCarthy, J.M., Eds., Moscow: Inostrannaya Literatura, 1956, pp. 362–384.

    Google Scholar 

  35. Rosenblatt, F., Principles of Neurodynamics, New York: Spartan Books, 1962. Translated under the title Printsipy neirodinamiki, Moscow: Mir, 1965.

    Google Scholar 

  36. Minsky, M. and Papert, S., Perceptrons, Cambridge: MIT Press, 1969. Translated under the title Perseptrony, Moscow: Mir, 1971.

    Google Scholar 

  37. Galushkin, A.I. and Logovskii, A.S., Neural Control: Basic Principles and Directions of Application of Neural Computers for Solution of Problems of Dynamic Plant Control, Dokl. Mezhdunar. konf. po problemam upravleniya (Proc. Int. Conf. on Control), Moscow: Inst. Probl. Upravlen., 1999.

    Google Scholar 

  38. Rumelhart, D.E., Hinton, G.E., and Williams, R.G., Learning Representation by Back-propagating Error, Nature, 1986, vol. 323, no. 6088, pp. 533–536.

    Google Scholar 

  39. Aved'yan, E.D., Barkan, G.V., and Levin, I.K., Cascaded Neural Networks, Avtom. Telemekh., 1999, no. 3, pp. 38–55.

    Google Scholar 

  40. Koposov, A.I., Shcherbakov, I.B., and Kislenko, N.A., Creating an Analytical Review of the Information Sources on Application of Neural Networks for Gas Technology. Research Report, Moscow: VNIIGAZ, 1995.

    Google Scholar 

  41. Hecht-Nilsen, R., Neurocomputing: Picking the Human Brain, IEEE Spectrum, 1998, vol. 25, no. 3, pp. 36–41.

    Google Scholar 

  42. Holland, J., Adaptation in Natural and Artificial Systems. Adaptation in Natural and Artificial Systems, Ann Arbor: Univ. Michigan, 1992.

    Google Scholar 

  43. Booker, L.B., Goldberg, D.E., and Holland, J.H., Classifier Systems and Genetic Algorithms, Artificial Intelligence, 1989, vol. 40, no. 2, pp. 235–282.

    Google Scholar 

  44. Goldberg, D., Genetic Algorithms in Machine Learning, Optimization and Search, Massachusetts: Addison-Wesley, 1989.

    Google Scholar 

  45. Jones, A.J., Genetic Algorithms and Their Applications to the Design of Neural Network, Neural Computing Applications, 1993, vol. 1, no. 1.

  46. Bunich, A.L. and Bakhtadze, N.N., Sintez i primenenie adaptivnykh sistem s identifikatorom (Design and Use of Adaptive Systems with Identifier), Moscow: Nauka, 2003.

    Google Scholar 

  47. Kurzhanskii, A.B., Upravlenie i nablyudenie v usloviyakh neopredelennosti (Control and Observation under Uncertainty), Moscow: Nauka, 1977.

    Google Scholar 

  48. Chernous'ko, F.L., Otsenivanie fazovogo sostoyaniya dinamicheskikh sistem. Metod ellipsoidov (Estimation of the Phase State of Dynamic Control. Ellipsoid method), Moscow: Nauka, 1988.

    Google Scholar 

  49. Tsypkinh, Ya.Z., Informatsionnaya teoriya identifikatsii (Information Theory of Identification), Moscow: Nauka, 1995.

    Google Scholar 

  50. Soderstrom, T. and Stoica, P., Variable Methods for System Identification New York: Springer, 1983.

    Google Scholar 

  51. Tertychnyi, V.Yu., Stokhasticheskaya mekhanika (Stochastic Mechanics), Moscow: Faktorial Press, 2001.

    Google Scholar 

  52. Grigor'ev, F.N., Kuznetsov, N.A., and Serebrovskii, A.P., Upravlenie nablyudeniyami v avtomaticheskikh sistemakh (Control of Observations in Automatic Systems), Moscow: Nauka, 1986.

    Google Scholar 

  53. Krasovskii, A.A., Historical Review and State-of-the-Art of the Fundamental Applied Science of Control as Exemplified by the Self-organizing Controllers, Plenarnye dokl. Mezhdunar. konf. po problemam upravleniya (Int. Conf. on Control, Plenary Papers), Moscow: Inst. Probl. Upravlen., 1999, pp. 4–23.

    Google Scholar 

  54. Polyak, B.T., Trudy Inst. Probl. Upravlen., 1999, vol. 5, pp. 36–41.

    Google Scholar 

  55. Semenov, A.V., Osnovy H∞-teorii upravleniya. Kurs lektsii (Fundamentals of the H∞-theory of Control. Lectures), Moscow: GOSNIIAS, 1992.

    Google Scholar 

  56. Andronov, A.A. and Pontryagin, L.S., Rough Systems, Dokl. Akad. Nauk SSSR, 1937, vol. 14, no. 5, pp. 247–249.

    Google Scholar 

  57. Huber, P.J., Robust Statistics, New York: Wiley, 1981. Translated under the title Robastnost' v statistike, Moscow: Mir, 1984.

    Google Scholar 

  58. Tsypkin, Ya.Z. and Polyak, B.T., Robust Stability of Linear Discrete Systems, Dokl. Akad. Nauk SSSR, 1991, vol. 316, no. 4, pp. 842–846.

    Google Scholar 

  59. Tsypkin, Ya.Z., Robustness in System of Control and Data Processing, Avtom. Telemekh., 1992, no. 1, pp. 165–169.

    Google Scholar 

  60. Dzhuri, E., Robustness of Discrete Systems, Avtom. Telemekh., 1990, no. 5, pp. 3–28.

    Google Scholar 

  61. Tsypkin, Ya.Z. and Polyak, B.T., Frequency Methods of Robust Stability of Linear Discrete Systems, Avtomatika, 1990, no. 4, pp. 3–9.

    Google Scholar 

  62. Polyak, B.T. and Tsypkin, Ya.Z., Frequency Methods of Robust Stability and Aperiodicity of Linear Systems, Avtom. Telemekh., 1990, no. 9, pp. 45–54.

    Google Scholar 

  63. Tsypkin, Ya.Z., Stochastic Discrete Systems with Internal Models, Probl. Upravlen. Inf., 1996, no. 12, pp. 21–25.

    Google Scholar 

  64. Kurdyukov, A.P., Osnovy robastnogo upravleniya (Fundamentals of Robust Control), Moscow: Mosk. Gos. Tekhn. Univ., 1995.

    Google Scholar 

  65. Petrov, B.I., Rutkovskii, V.Yu., and Zemlyakov, S.D., Adaptivnoe koordinatno-parametricheskoe upravlenie nestatsionarnymi ob"ektami (Adaptive Coordinate-Parametric Control of Nonstationary Objects), Moscow: Nauka, 1980

    Google Scholar 

  66. Zemlyakov, S.D. and Rutkovskii, V.Yu., Adaptation Algorithms and Conditions for Operability of the Self-adjusting Control System of a Multivariable Plant with Variable Parameters, Avtom. Telemekh., 1981, no. 1, pp. 65–73.

    Google Scholar 

  67. Pavlov, B.V., Structural Design of the Main Loop of Searchless Self-adjusting Systems, Avtom. Telemekh., 1977, no. 12, pp. 56–64.

    Google Scholar 

  68. Yadykin, I.B., Shumskii, V.M., and Ovsepyan, F.A., Adaptivnoe upravlenie nepreryvnymi tekhnologich-eskimi protsessami (Adaptive Control of Continuous Processes), Moscow: Energoatomizdat, 1985.

    Google Scholar 

  69. Bukov, V.N., Adaptivnye prognoziruyushchie sistemy upravleniya poletom (Adaptive Predicting Flight Control Systems), Moscow: Nauka, 1987.

    Google Scholar 

  70. Afanas'ev, V.N., Kolmanovskii, V.B., and Nosov, V.R., Matematicheskaya teoriya konstruirovaniya sistem upravleniya (Mathematical Theory of Control System Design), Moscow: Vysshaya Shkola, 2003.

    Google Scholar 

  71. Derevitskii, D.P. and Fradkov, A.L., Prikladnaya teoriya diskretnykh adaptivnykh sistem upravleniya (Applied Theory of Discrete Adaptive Control Systems), Moscow: Nauka, 1981.

    Google Scholar 

  72. Nazin, A.V., Adaptivnyi vybor variantov: Rekkurentnye algoritmy (Adaptive Choice of Variants: Recurrent Algorithms), Moscow: Nauka, 1986.

    Google Scholar 

  73. Kurdyukov, A.P., Design of Optimal Robust Controllers under Disturbances, in: Metody klassicheskoi i sovremennoi teorii upravleniya (Methods of the Classical and Modern Control Theory), Moscow: Mosk. Gos. Tekhn. Univ., 2000.

    Google Scholar 

  74. Doyle, J., Analysis of Feedback Systems with Strustured Uncertainties, IEEE Proc., 1982, vol. 129, part D, no. 6.

  75. Stein, G. and Doyle, J., Beyond Singular Values and Loop Shapes, J. Guidance, 1991, vol. 14, no. 1.

  76. Packard, A. and Doyle, J., The Complex Structured Singular Value, Automatica, 1993, vol. 29, no. 1.

  77. Packard, A. and Doyle, J., Quadratic Stability with Real and Complex Perturbations, IEEE Trans. Automat. Control, vol. 35, no. 2.

  78. Lu, W.M., Zhou, K., and Doyle J., Stabilization of LFTnsystems, Proc. 30 CDC, Brihton, 1991.

  79. Vidyasagar, M., Optimal Rejection of Persistent Bounded Disturbances, IEEE Trans. Automat. Control, 1986, vol. 31, pp. 527–534.

    Google Scholar 

  80. Khammash, M. and Pearson, J.B., Perfomance Robustness of Discrete-time Systems with Structured Uncertainty, IEEE Trans. Automat. Control, 1991, vol. 36, no. 4.

  81. Dahleh, M.A. and Khammash, M.H., Controller Design for Plants with Structured Uncertainty, Automatica, 1993, vol. 29, no. 1.

  82. Chappelat, H. and Bhattacharya, S.P., A Generalization of Kharitonov's Theorem: Robust Stability of Interval Plants, IEEE Trans. Automat. Control, 1989, vol. 34, no. 3.

  83. Hollot, C.V. and Yang, F., Robust Stabilization of Interval Plants Using Lead or Lag Compensators, Syst. Control. Lett., 1990, vol. 14, no. 1.

  84. Petersen, I.R., A New Extention of Kharitonov's Theorem, IEEE Trans. Automat. Control, 1990, vol. 35, no. 7.

  85. Barmish, B.R. and Kang, H.I., Extreme Point Results for Robust Stability of Interval Plants: Beyond First Order Compensators, Proc. FirstIFACSymp. OnDes. Meth.Control Sys., ETH, Zurich, 1991, vol. 1, pp. 1–16.

    Google Scholar 

  86. Semyonov, A.V., Vladimirov, I.G., and Kurdjukov, A.P., Stochastic Approach to H∞-optimization, Proc. 33rd Conf. Decision and Control, Florida, vol. 3, 1994.

  87. Vladimirov, I.G., Kurdyukov, A.P., and Semenov, A.V., Stochastic Problem of H∞-optimization, Dokl. Ross. Akad. Nauk, 1995, no. 5, pp. 343–350.

    Google Scholar 

  88. Vladimirov, I.G., Kurdyukov, A.P., and Semenov, A.V., Asymptotics of the Anisotropic Norm of Linear Stationary Systems, Avtom. Telemekh., 1999, no. 3, pp. 78–87.

    Google Scholar 

  89. Polyak, B.T. and Kiselev, O.N., Design of Low-order Controllers by the H∞-Criterion and the Criterion for Maximum Robustness, Avtom. Telemekh., 1999, no. 3, pp. 119–130.

    Google Scholar 

  90. Tsypkin, Ya.Z., Design of Robust-optimal Plant Control Systems under Bounded Uncertainty, Avtom. Telemekh., 1992, no. 9, pp. 139–159.

    Google Scholar 

  91. Tsypkin, Ya.Z., Robust-optimal Discrete Control Systems, Avtom. Telemekh., 1999, no. 3, pp. 25–37.

    Google Scholar 

  92. Barabanov, A.E. and Granichin, O.N., Optimal Controller of a Linear Plant with Bounded Noise, Avtom. Telemekh., 1984, no. 5, pp. 39–46.

    Google Scholar 

  93. http://www.umich.edu/flash.html

  94. Bakhtadze, N.N., Lototskii, V.A., and Fayans, M.A., Identification Approach to Investment Planning, Tr. Mezhdunar. konf. "Identifikatsiya sistem i zadachi upravleniya" (Proc. Int. Conf. "System Identification and Problems of Control"), Moscow: Inst. Probl. Upravlen., 2000, pp. 9–14.

    Google Scholar 

  95. Bakhtadze, N.N. and Nazin, A.V., Virtual Analyzer in the Systems of Company Debt Management, Tez. dokl. 2-i Mezhdunar. konf. po problemam upravleniya (Second Int. Control Conf.), Moscow: Inst. Probl. Upravlen., vol. 2, p. 4, 2003.

    Google Scholar 

  96. Lototskii, V.A. and Mandel', A.S., Modeli i metody upravleniya zapasami (Models and Methods of Inventory Control), Moscow: Nauka, 1991.

    Google Scholar 

  97. Bobrovskii, S.A., Outlooks and Tendencies of Artificial Intelligence, PC Week/RE, 2001, no. 32, p. 32.

    Google Scholar 

  98. Masterenko, D.A., Recurrent Robust Estimation in Computerized Information-and-Measurement Systems, Tez. dokl. nauchno-tekhn. konf. "Sostoyanie i problemy tekhnicheskikh izmerenii" (Second Conf. "State-of-the-Art and Problems of Technical Measurements"), Moscow: Mosk. Gos. Tekhn. Univ., 1995.

    Google Scholar 

  99. Gorbunov, A.R., Upravlenie finansovymi potokami (Management of Financial Flows), Moscow: Toratsentr, 2003.

    Google Scholar 

  100. Kardash, V.A., Modeli upravleniya proizvodstvenno-ekonomicheskimi protsessami v sel'skom khozyaistve (Models of Control of Production and Economics in Agriculture), Moscow: Ekonomika, 1981.

    Google Scholar 

  101. Prokopchina, S.V., Bayes Integrating Technologies based on Intelligent and Soft Measurements, in Dokl. konf. SCM'99 (Proc. Conf. SCM'99), St. Petersburg, 1999, pp. 25–32.

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Bakhtadze, N.N. Virtual Analyzers: Identification Approach. Automation and Remote Control 65, 1691–1709 (2004). https://doi.org/10.1023/B:AURC.0000047885.52816.c7

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