Application of artificial neural networks and chemometrics in the modeling of fluid catalytic cracking process

Author(s)

Wagner Roberto de Oliveira Pimentel

• Document type: Doctoral Thesis
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Química
• Defense date: 2005-03-18

Examining board members

Antônio Carlos Luz Lisbôa; Waldir Pedro Martignoni; Ronei Jesus Poppi; Liliane Maria Ferrareso Lona; Rubens Maciel Filho

• Advisor: Antônio Carlos Luz Lisbôa

Abstract

The fluidized bed catalytic cracking process is one of the most important refining processes. It produces, among other distillates, gasoline and liquefied petroleum gas (LPG). It is very difficult to model it by fundamental balances. On the other hand, artificial neural networks (ANN) offer convenient tools to describe complex processes. They are able to learn what is going on with in the process through a limited amount of information, requiring less computing time than phenomenological modeling. The main objective of this work was to develop empirical models ¿ based on ANNs and chemometrics ¿ able to relate input and output variables of the FCC process, using data from a pilot and from an industrial plant. Experimental data were obtained from the Petrobras FCC pilot plant located in São Mateus do Sul, Parané, nd from the Petrobras Landulpho Alves Refinery PCC industrial plant located in São Francisco do Conde, Bahia. The principal component analysis (PCA) technique was initially used to preprocess the data. Artificial neural networks were then employed with the following supervising training algorithms: Broyden-Fletcher-Godfarb-Shanno (BFGS), Scale Conjugated Gradient (SCG) and Levenberg-Marquardt (LM). Methods devised to increase the artificial network prediction power were also used... Note: The complete abstract is available with the full electronic digital thesis or dissertations (AU)