Open AccessPredictive control based on neural networks: an application to a fluid catalytic cracking industrial unit
Author(s) -
Vera Santos,
Florival Rodrigues de Carvalho,
Maurício B. de Souza
Publication year - 2000
Publication title -
brazilian journal of chemical engineering/brazilian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.313
H-Index - 52
eISSN - 1678-4383
pISSN - 0104-6632
DOI - 10.1590/s0104-66322000000400054
Subject(s) - fluid catalytic cracking , model predictive control , artificial neural network , identification (biology) , process (computing) , engineering , nonlinear system , control engineering , control (management) , process control , focus (optics) , cracking , computer science , artificial intelligence , chemistry , botany , physics , optics , quantum mechanics , biology , operating system
Artificial Neural Networks (ANNs) constitute a technology that has recently become the focus of great attention. The reason for this is due mainly to its capacity to treat complex and nonlinear problems. This work consists of the identification and control of a fluid cracking catalytic unit (FCCU) using techniques based on multilayered ANNs. The FCC unit is a typical example of a complex and nonlinear process, possessing great interaction among the operation variables and many operational constraints to be attended. Model Predictive Control is indicated in these occasions. The FCC model adopted was validated with plant data by Moro (1992); and was used in this work to replace the real process in the generation of data for the identification of the ANNs and to test the predictive control strategy. The results of the identification and control of the process through ANNs indicate the viability of the technique