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Development, implementation and performance evaluation of a self-tuning regulator adaptive controller applied to a FCC process

Pleycienne Trajano Ribeiro
Total Authors: 1
Document type: Doctoral Thesis
Institution: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Química
Defense date:
Examining board members:
Antônio Carlos Luz Lisboa; Ana Clélia Ferreira; Paula Sbaite Duarte dos Santos; Otávio Cavalett
Advisor: Rubens Maciel Filho

This work had as main objective the development and implementation of an selftuning regulator (STR) adaptive controller, with subsequent comparison of its performance with a PID (proportional-integral-derivative) controller and two predictive controllers, namely a predictive based on artificial neural networks (MPC-ANN) and a dynamic matrix controller (DMC). These control schemes were all implemented in the developed simulation tool, the FCCGUI - Fluid Catalytic Cracking Graphical User Interface. An artificial neural network, used as a model to estimate controller parameters, was trained and validated. This black box model provided an efficient approach for identification and nonlinear control of the catalytic cracking process. To implement the adaptive controller, three new PID control loops were structured based on statistical studies designed to analyze the effects of process variables and their interactions. The implementation of adaptive control was chosen to be in the manipulated-controlled pair CTCV-SEVER (regenerated catalyst valve opening - reaction severity). After restructuring and improvements in the simulator FCCGUI, several simulations were performed for graphical and numerical evaluation of controller performance through ITAE (Integral of Time and Absolute Error) dynamic performance criterion. The adaptive controller presented good results for both tests: servo and regulatory, in comparison with PID strategy without adaptation and other strategies available to the simulator, MPC-ANN and DMC. The ability to adjust the parameters of the controller makes it a promising strategy for systems that suffer from continuous changes in their process variables or setpoints (AU)