Simulation of the reactor-regenerator-main fractionator fluid catalytic cracking unit using artificial neural networks

M. V. Cristea, R. Roman, P. S. Agachi

    Research output: Contribution to conferencePaper

    Abstract

    The dynamic behavior of the FCC unit was simulated using artificial neural networks (ANN). An analytical model, validated with construction and operation data, was used for producing a comprehensive input-target set of training data. The model predicted the final production rate of the main products, gasoline and diesel. The architecture and training algorithm used by the ANN were efficient and this was proved by the results obtained both on training set and set of input-target data not met during the training procedure. The same good ANN performance was obtained by the comparison between dynamic simulations results emerged from the ANN vs. first principle modeling, both using the same randomly varying inputs. The computation time was reduced when using the ANN model, compared to the use of the analytical model. This is an abstract of a paper presented at the 18th International Congress of Chemical Process Engineering (Praque, Czech Republic 8/24-28/2008).

    Original languageEnglish
    Publication statusPublished - 2008
    Event18th International Congress of Chemical and Process Engineering, CHISA 2008 - Prague, Czech Republic
    Duration: Aug 24 2008Aug 28 2008

    Other

    Other18th International Congress of Chemical and Process Engineering, CHISA 2008
    CountryCzech Republic
    CityPrague
    Period8/24/088/28/08

    Fingerprint

    Fluid catalytic cracking
    Regenerators
    Neural networks
    Analytical models
    Process engineering
    Chemical engineering
    Network performance
    Gasoline
    Computer simulation

    All Science Journal Classification (ASJC) codes

    • Chemical Engineering (miscellaneous)
    • Process Chemistry and Technology

    Cite this

    Cristea, M. V., Roman, R., & Agachi, P. S. (2008). Simulation of the reactor-regenerator-main fractionator fluid catalytic cracking unit using artificial neural networks. Paper presented at 18th International Congress of Chemical and Process Engineering, CHISA 2008, Prague, Czech Republic.
    Cristea, M. V. ; Roman, R. ; Agachi, P. S. / Simulation of the reactor-regenerator-main fractionator fluid catalytic cracking unit using artificial neural networks. Paper presented at 18th International Congress of Chemical and Process Engineering, CHISA 2008, Prague, Czech Republic.
    @conference{56d4d297f2e54109bb7c54780516b2ff,
    title = "Simulation of the reactor-regenerator-main fractionator fluid catalytic cracking unit using artificial neural networks",
    abstract = "The dynamic behavior of the FCC unit was simulated using artificial neural networks (ANN). An analytical model, validated with construction and operation data, was used for producing a comprehensive input-target set of training data. The model predicted the final production rate of the main products, gasoline and diesel. The architecture and training algorithm used by the ANN were efficient and this was proved by the results obtained both on training set and set of input-target data not met during the training procedure. The same good ANN performance was obtained by the comparison between dynamic simulations results emerged from the ANN vs. first principle modeling, both using the same randomly varying inputs. The computation time was reduced when using the ANN model, compared to the use of the analytical model. This is an abstract of a paper presented at the 18th International Congress of Chemical Process Engineering (Praque, Czech Republic 8/24-28/2008).",
    author = "Cristea, {M. V.} and R. Roman and Agachi, {P. S.}",
    year = "2008",
    language = "English",
    note = "18th International Congress of Chemical and Process Engineering, CHISA 2008 ; Conference date: 24-08-2008 Through 28-08-2008",

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    Cristea, MV, Roman, R & Agachi, PS 2008, 'Simulation of the reactor-regenerator-main fractionator fluid catalytic cracking unit using artificial neural networks' Paper presented at 18th International Congress of Chemical and Process Engineering, CHISA 2008, Prague, Czech Republic, 8/24/08 - 8/28/08, .

    Simulation of the reactor-regenerator-main fractionator fluid catalytic cracking unit using artificial neural networks. / Cristea, M. V.; Roman, R.; Agachi, P. S.

    2008. Paper presented at 18th International Congress of Chemical and Process Engineering, CHISA 2008, Prague, Czech Republic.

    Research output: Contribution to conferencePaper

    TY - CONF

    T1 - Simulation of the reactor-regenerator-main fractionator fluid catalytic cracking unit using artificial neural networks

    AU - Cristea, M. V.

    AU - Roman, R.

    AU - Agachi, P. S.

    PY - 2008

    Y1 - 2008

    N2 - The dynamic behavior of the FCC unit was simulated using artificial neural networks (ANN). An analytical model, validated with construction and operation data, was used for producing a comprehensive input-target set of training data. The model predicted the final production rate of the main products, gasoline and diesel. The architecture and training algorithm used by the ANN were efficient and this was proved by the results obtained both on training set and set of input-target data not met during the training procedure. The same good ANN performance was obtained by the comparison between dynamic simulations results emerged from the ANN vs. first principle modeling, both using the same randomly varying inputs. The computation time was reduced when using the ANN model, compared to the use of the analytical model. This is an abstract of a paper presented at the 18th International Congress of Chemical Process Engineering (Praque, Czech Republic 8/24-28/2008).

    AB - The dynamic behavior of the FCC unit was simulated using artificial neural networks (ANN). An analytical model, validated with construction and operation data, was used for producing a comprehensive input-target set of training data. The model predicted the final production rate of the main products, gasoline and diesel. The architecture and training algorithm used by the ANN were efficient and this was proved by the results obtained both on training set and set of input-target data not met during the training procedure. The same good ANN performance was obtained by the comparison between dynamic simulations results emerged from the ANN vs. first principle modeling, both using the same randomly varying inputs. The computation time was reduced when using the ANN model, compared to the use of the analytical model. This is an abstract of a paper presented at the 18th International Congress of Chemical Process Engineering (Praque, Czech Republic 8/24-28/2008).

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    Cristea MV, Roman R, Agachi PS. Simulation of the reactor-regenerator-main fractionator fluid catalytic cracking unit using artificial neural networks. 2008. Paper presented at 18th International Congress of Chemical and Process Engineering, CHISA 2008, Prague, Czech Republic.