%0 Journal Article %1 noauthororeditor %A Karas, Peter %A Kozak, Stefan %D 2017 %J International Journal of Advances in Chemistry (IJAC) %K Identification bed control fluidized model networks neural polypropylene predictive reactor %N 3/4 %P 01-14 %R 10.5121/ijac.2017.3401 %T ARTIFICIAL NEURAL NETWORK APPROACH TO MODELING OF POLYPROPYLENE REACTOR %U http://airccse.com/ijac/papers/3417ijac01.pdf %V 3 %X This paper shows modeling of highly nonlinear polymerization process using the artificial neural network approach for the model predictive purposes. Polymerization occurs in a fluidized bed polypropylene reactor using Ziegler - Natta catalyst and the main objective was modeling of the reactor production rate. The data set used for an identification of the model is a real process data received from an existing polypropylene plant and the identified model is a nonlinear autoregressive neural network with the exogenous input. Performance of a trained network has been verified using the real process data and the ability of the production rate prediction is shown in the conclusion.

@article{noauthororeditor, abstract = {This paper shows modeling of highly nonlinear polymerization process using the artificial neural network approach for the model predictive purposes. Polymerization occurs in a fluidized bed polypropylene reactor using Ziegler - Natta catalyst and the main objective was modeling of the reactor production rate. The data set used for an identification of the model is a real process data received from an existing polypropylene plant and the identified model is a nonlinear autoregressive neural network with the exogenous input. Performance of a trained network has been verified using the real process data and the ability of the production rate prediction is shown in the conclusion. }, added-at = {2018-05-22T07:04:30.000+0200}, author = {Karas, Peter and Kozak, Stefan}, biburl = {https://www.bibsonomy.org/bibtex/2155fda1dbfb4983c1ba934329f7e9b59/ijac}, doi = {10.5121/ijac.2017.3401}, interhash = {78fa2eefbe92dc4824b0494933212377}, intrahash = {155fda1dbfb4983c1ba934329f7e9b59}, issn = {2455-7862}, journal = {International Journal of Advances in Chemistry (IJAC) }, keywords = {Identification bed control fluidized model networks neural polypropylene predictive reactor}, language = {English}, month = nov, number = {3/4}, pages = {01-14}, timestamp = {2018-05-22T07:04:30.000+0200}, title = {ARTIFICIAL NEURAL NETWORK APPROACH TO MODELING OF POLYPROPYLENE REACTOR}, url = {http://airccse.com/ijac/papers/3417ijac01.pdf}, volume = 3, year = 2017 }