Fuzzy Logic Modeling of the Fluidized Catalytic Cracking Unit of a Petrochemical Refinery


Authors: P.B. Osofisan and O.J. Obafaiye (University of Lagos, Akoka-Yaba, Lagos, Nigeria)




This paper describes investigations carried out regarding the application of Fuzzy Logic Control to the Fluidized Catalytic Cracking Unit (FCCU) of Kaduna Refinery and Petrochemical Company in Northern Nigeria, as a case study. An optimal control solution where the objective is to determine a well-defined relationship between the vital variables (reactor temperature/riser outlet temperature, regenerator gas temperature, regenerated catalyst feed rate, and the airflow rate) through the use of Fuzzy Logic control scheme is the focus of this paper.


In the catalytic cracking unit, feed oil is contacted with re-circulating catalyst and reacted in a riser tube. The feed oil vaporizes and is cracked as it flows up the riser, thus forming lighter hydrocarbons (the gasoline fraction). Large amounts of coke are formed as a byproduct. The coke deposits on the catalyst and reduces its activity. The lighter hydrocarbon products are separated from the spent catalyst in the ‘reactor’. Steam is supplied to strip volatile hydrocarbons from the catalyst. The catalyst is then returned to the regenerator, where the coke is burnt off in contact with air. This is usually done by partial or complete combustion. The regenerated catalyst is then re-circulated back to mix with the inlet feed oil from the crude unit.


The behaviour of the reactor temperature/riser outlet temperature and the regenerator gas temperature during the chemical reactions in the FCCU were simulated using MATLAB®. The problem of control, will however involve the control of two outputs (reactor temperature/riser outlet temperature and the regenerator gas temperature) by manipulating the two inputs (regenerated catalyst feed rate and the airflow rate), which are the critical and vital factors for optimization of the cracking process in the FCCU. A relationship was developed between the above stated input(s) and output(s) with the help of the fuzzy logic controller. This will facilitate optimization of gasoline production.


Source: The Pacific Journal of Science and Technology