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2013 | 13 | 3 |

Tytuł artykułu

Neural network simulation in running of acetic acid synthesis unit while start-up

Treść / Zawartość

Warianty tytułu

RU
Nejjroetevoe modelirovanie dlja upravlenija kolonnojj sinteza uksusnojj kisloty v period puska

Języki publikacji

EN

Abstrakty

EN
RU

Wydawca

-

Rocznik

Tom

13

Numer

3

Opis fizyczny

p.188-192,fig.,ref.

Twórcy

autor
  • Volodymyr Dahl East-Ukrainian National University, Radyansky pr. 59-a, Severolonetsk, 93406, Ukraine
autor
  • Volodymyr Dahl East-Ukrainian National University, Radyansky pr. 59-a, Severolonetsk, 93406, Ukraine

Bibliografia

  • 1. Afanasenko A.G., Verevkin A.P., 2009.: Neuronet simulation of carbonation process quality rating . VestnikU. ,Vol. 13 №2 (35), 222-225.
  • 2.Baskin I.I., Palyulin V.A., Zefirov N.S., 2006.: Multilayered perceptrons in "structure-property" relationship studies for organic compounds. Rus. Chem. Journ. ( Journal of Russian chemical society after D. I. Mendeleyev)., T. L. №2., 86-96.
  • 3.Baskin I.I., Palyulin V.A., Zefirov N.S., 2005.: Using artificial neural networks for chemical compounds properties prognostication. Neural computers: design, applications, № 1-2, 98-101.
  • 4.Baskin I.I., Skvortsova M.I., Palyulin V.A., Zefirov N.S., 1997.: Quantitative chemical structure-property/activity relationship studies using artificial neural networks. Foundations of computing and decision sciences. Vol. 22, №2, 107-116.
  • 5.De Souza, M. В., Pinto J. C., et al., 1996:. Control of a chaotic polymerization reactor: A neural network based model predictive approach. Polymer Engineering and Science, 36(4), 448-457.
  • 6.Galushkin A.I., 2000.: The book. Theory of neural networks. M.: I, 416.
  • 7.Gardner J.W., Hines E.L., Wilkinson M., 1990.: Application of artificial neural networks to an electronic olfactory system. Measurement science and technology. Vol. 1, 446-451.
  • 8.Haykin S., 2006.: The book, Neural networks. Complete course. M.: Williams, 1104.
  • 9.Hong H.-K., Kwon C.N., Kim S.-R., 2000.: Portable electronic nose system with gas sensor array and artificial neural network. Sensors and Actuators B. , Vol. 66, 49-52.
  • 10.Huang, Y. F., G. H. Huang, et al., 2003.: Development of an artificial neural network model for predicting minimum miscibility pressure in ACETIC ACID SYNTHESIS UNIT WHILE START-UP 191 C02 flooding. Journal of Petroleum Science and Engineering, 37(1-2), 83-95.
  • 11.Karimov R.N., 2000.: Experimental information processing. P.3. Multivariate analysis: learning aid, Saratov: SSTU, 108.
  • 12.Kruglov V.V., Borisov V.V., 2002.: The book Artificial neural networks. Theory and practice. 2nd edition, pattern, M.: Hot line - Telecom, 382.
  • 13.Kruglov V.V., Borisov V.V., 2001.: The book, Hybrid neural networks., Smolensk: Rusich, 224.
  • 14.Kusz A., Maksym P., Marciniak A.W., 2011.: Bayesian networks as knowledge representation system in domain of reliability engineering. TEKA Commission of Motorization I Energ. Roln., 11 C, 173-180.
  • 15.Larachi F., 2001.: Neural network kinetic prediction of coke burn-off on spent Mn02/Ce02 wet oxidation catalysts. Applied Catalysis B, Environmental, 30(1-2), 141-150.
  • 16.Larachi F. and Granjean B. P. A., 2000.: Comments on "Neural network modeling of structured packing height equivalent to a theoretical plate" and "HETP and pressure drop prediction for structured packing distillation columns using a neural network". Industrial & Engineering Chemistry Research, 39(11), 44374437.
  • 17.Medvedev V.S., Potemkin V.G., 2002.: The book, Neural networks. MATLAB 6. Under the editorship of Cand. of Sc. Potemkin V.G., M.: DIALOG IEFI, 495.
  • 18.Nagy Z., American Institute of Chemical Engineers., et al., 2000.: A Comparison of First Principles and Neural Network Model Based Nonlinear Predictive Control of a Distillation Column. Distributed by American Institute of Chemical Engineers, New York, N. Y.
  • 19.Park J.-K., 1993.: Modeling of Distillation Column and Reactor Dynamics Using Artificial Neural Networks (Neural Networks). DAI- 55, №., 01B, 6660.
  • 20.Sabharwal A., 1998.: A Hybrid Approach Applied to an Industrial Distillation Column That Compares Physical and Neural Network Modeling Techniques. MAI, 37, no. 02, 0648.
  • 21.Santos V. M. L., Carvalho F. R., et al., 2000.: Predictive control based on neural networks: An application to a fluid catalytic cracking industrial unit. Brazilian Journal of Chemical Engineering, 17(4-7), 897-905.
  • 22.Su G. H., Fukuda K., et al. 2002.: Application of an artificial neural network in reactor thermohydraulic problem: Prediction of critical heat flux. Journal of Nuclear Science and Technology, 39(5), 564-571.
  • 23.Su G. H., Fukuda K., et al. 2002.: Applications of artificial neural network for the prediction of flow boiling curves. Journal of Nuclear Science and Technology, 39(11), 1190-1198.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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