Fuzzy modeling of fluidized catalytic cracking unit

• Published in: Applied soft computing Vol. 7; no. 1; pp. 298 - 324
• Main Authors: Azeem, Mohammad Fazle, Ahmad, Nesar, Hanmandlu, M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2007
• Subjects: Dynamic fuzzy model; FCCU models; Fuzzy clustering; Fuzzy inference systems; Fuzzy models; Hybrid learning; Mountain clustering; Supervised learning; Unsupervised learning; Fuzzy clustering; Supervised learning; Fuzzy models; Fuzzy inference systems; Mountain clustering; FCCU models; Dynamic fuzzy model; Hybrid learning; Unsupervised learning
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2005.01.007

The paper deals with the fuzzy system identification of reactor–regenerator–stripper–fractionator's (RRSF) section of a fluidized catalytic cracking unit (FCCU). The fuzzy system identification based on the data collected from an operating refinery of FCCU of capacity, 1.2MMPTA, with a sample time of 10min. A generalized fuzzy model (GFM) and identification of structure and model parameter for multi-input/single output is presented. The GFM has the capability of representing both the CRI model and TS model under certain conditions. The structure identification and the parameter estimation are carried out using hybrid learning approach comprising modified mountain clustering and gradient descent learning with least square estimation (LSE) for the identification of a fuzzy model. The modified mountain clustering considers every data point as a potential cluster center in x×y hyperspace. The optimum number of clusters, which leads to an optimum number of rules, is determined with the help of validity function that guides the search. The obtained result from the modified mountain clustering initializes the GFM. Further hybrid of the gradient descent technique and LSE is aimed at learning of the GFM parameters in two phases. In the first phase of an epoch of learning gradient descent tunes the premise parameter and index of fuzziness of each rule. In second phase, LSE utilizes the results of first phase for evaluating the coefficient of local linear model of corresponding rules.