The use of sensitivity analysis and genetic algorithms for the management of catalyst emissions from oil refineries.

Abstract

Excessive catalyst emissions from Fluidized Catalytic Cracking Units (FCCU) during start-up situations are common, and have been deemed 'normal', with little research conducted on determining their causes. A MATLAB model found to predict trends in emission rates under normal conditions has been expanded to better represent the actual processes inside a FCCU. First and second order sensitivity analysis techniques are used to assess the interactions between various operational parameters, with a genetic algorithm used to optimize the operating conditions to minimize air emissions. These 'key' parameters may then be altered to help manage both normal and start-up emissions through operational changes. It was also found that significant scale-up issues arise with the use of the attrition models found in the literature.