APPLICATION OF MACHINE LEARNING TECHNIQUES FOR MODELING THE REACTOR OF THE CATALYTIC CRACKING PROCESS

Cristina Roxana POPA, Mădălina CĂRBUREANU, Bogdan DOICIN

Abstract


This paper presents the research and results obtained by authors regarding the modeling of the reactor of the fluid catalytic cracking unit (FCCU), using various machine learning techniques based on supervised learning algorithms Compared with the analytic mathematic modeling methods, machine learning techniques (ML) can solve the difficulties in FCC process modeling, such as strong correlation between variables, nonlinearities, the complexity of kinetic models, and feedstock complexity. The ML method establishes a mathematical correlation between the input and output variables of the process using industrial data. Five supervised learning machine methods are used to model the reactor: random forest regression (RFR), decision trees regression (DTR), k-nearest neighbors (KNN), support vector machines (SVM) and gradient boosting regression (GBR).

References


Reza, S., Fluid Catalytic Cracking Handbook, Butterworth- Heinemann, ISBN 978-0-12-812663-9, Texas, 2020.

Popa, C, Pătrășcioiu, C., New Approach in Modelling, Simulation and Hierarchical Control of the Fluid Catalytic Cracking Process I - Process Modeling, Revista de chimie, vol. 61, pp. 419-246, 2010.

Weekman Jr., V.W., A Model of Cracking Catalytic Conversion in Fixed, Moving, and Fluid Bed Reactors, Industrial &Engineering Chemistry Process Design and Development, vol. 7, pp. 90-95, 1968.

Wojciechowski, B.W., The Kinetic Foundations and the Practical Application of the Time on Stream Theory of Catalyst Decay, Industrial &Engineering Chemistry Process Design and Development, volume 9, pp. 79-113, 1974.

Vorobev, A., Chuzlov, V., Elena, I., Artem, A., Simple Model of Industrial Catalytic Cracking Riser Reactor, Industrial & Engineering Chemistry Research, volume 52, pp. 22005-22016, 2023.

Dragomir, R., Paul, R., Popa, C., Five- kinetic model for Catalytic Cracking Process, Revista de chimie, volume 59, pp. 2633-2638, 2018.

Heydrei, M., Habib, A, Dabir, B., Study of seven lump kinetic model in fluid catalytic cracking unit, American Journal of Applied Sciences, volume 7, pp. 71-76, 2010.

Jacob, S.M., Gross, B., Voltz, S.E., Weekman, V.M., A Lumping and Reaction Scheme for Catalytic Cracking, AIChE Journal, volume 2, pp. 701-713, 1976.

Barbosa, A.C., Lopes, G.C., Rosa, L.M., Mori, M., Martignoni, W.P., Three Dimensional Simulation of Catalytic Cracking Reactions in an Industrial Scale Riser Using an 11-lump Kinetic Model, AIDIC Conference, volume 11, pp. 31-41, ISBN 978-88-95608-55-6, 2013.

Doicin, B., Carburenu, M., Popa, C.R, Artificial Neuronal Network VS Linear Regression for Modeling Reactor of the Catalytic Cracking Process, Proceeding of the 24th International Multidisciplinary Scientific GeoConferences: Informatics, SGEM, volume 24(2.1), pp. 27-34, ISBN 978-619-7603-69-9, 2024.

Khalid. A.A, Omara, H., Lazzar, M., Achlab, M.A., Computational Intelligence and Applications for Pandemic and Healthcare, ISBN 9781799898313, 2022.

Popescu, C., Cangea, O., Bucur, G., Mois,e A., The Utility of Neuro-Fuzzy Hybrid Systems in Control, Informatics, Geoinformatics and Remote Sensing Conference Proceeding, SGEM, volume 1, pp. 383-390, ISBN 1314-2704, Albena, Bulgaria, 2015.

Sen, P. C., Hajra, M., Ghosh, M., Supervised Classification Algorithms in Machine Learning: A Survey and Review, Advances in Intelligent Systems and Computing, volume 27, pp. 99-111, 2020.

Reza, R.R., Mohammadreza, B., Sajadi, S.M., Pirmoradian, M., Renani, M.R., Baghaei, S., Salahshour, S., Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling, Alexandria Engineering Journal, volume 84, pp. 184–203, 2023.

Cui, Z., Machine learning and small data, Educational measurement, volume 40, pp. 8-12, 2021.

Carbureanu, M., Mihalache, S, F., Zamfir, F., Machine Learning Methods Applied for Waster pH neutralization Process Modelling, 14th International Conference on Electronics, Computers and Artificial Intelignece, pp. 101-107, ISBN:978-1-6654-9535-6, Romania, 2022.

Muller, A.C., Guido, S., Introduction to Machine Learning with Python, A Guide for Data Scientists, Published by O’Reilly Media Inc., ISBN 1449369901, 2017.

Huang, R., Ma, C., Ma, J., Huangfu, X., He, Q., Machine Learning in Natural and Engineered Water Systems, Water Research, volume 205, pp. 1-14, 2021.

Yildiz, B., Bilbao, J.I., Sproul, A.B., A Review and Analysis of Regression and Machine Learning Models on Commercial Building Electricity Load Forecasting, Renewable and Sustainable Energy Reviews, volume 73, pp. 1104-1122, 2017.

Bentéjac, C., Csörgő, A., Martínez‑Muñoz, G., A comparative analysis of gradient boosting algorithms, Artificial Intelligence Review, volume 54, pp. 1937–1967, 2012.

Shai, S., Shai, D., Understanding machine learning. From Theory to Algorithms, Cambridge University Press, ISBN 9781107298019, 2014.


Refbacks

  • There are currently no refbacks.


JOURNAL INDEXED IN :