Abstract
Changes in the quality of the feedstocks generated by involving various petroleum fractions in catalytic cracking significantly affect catalyst deactivation, which stems from coke formed on the catalyst surface. By conducting experimental studies on feedstocks and catalysts, as well as using industrial data, we studied how the content of saturates, aromatics and resins (SAR) in feedstock and the main process variables, including temperature, consumptions of the feedstock, catalyst and slops, influence the formation of catalytic coke. We also determined catalyst deactivation patterns using TG-DTA, N2 adsorption and TPD, which were further used as a basis for a kinetic model of catalytic cracking. This model helps predict the changes in reactions rates caused by coke formation and, also, evaluates quantitatively how group characteristics of the feedstock, the catalyst-to-oil ratio and slop flow influence the coke content on the catalyst and the degree of catalyst deactivation. We defined that a total loss of acidity changes from 8.6 to 30.4 wt% for spent catalysts, and this depends on SAR content in feedstock and process variables. The results show that despite enriching the feedstock by saturates, the highest coke yields (4.6–5.2 wt%) may be produced due to the high content of resins (2.1–3.5 wt%).
Highlights
A Model of Catalytic CrackingTG-DTA, N2 adsorption and temperature-programmed desorption of ammonia (TPD), which were further used as a basis for a kinetic model of catalytic cracking
A tendency to produce heavier crude oil generates the need for improving advanced petroleum refining processes both through the development and application of high activity and selective forms of catalysts and the prediction and optimization of their operating variables using predictive models [1,2,3]
Changes in the quality of the feedstocks generated by involving the various petroleum fractions in catalytic cracking significantly affect catalyst deactivation, which stems from coke formed on the catalyst’s surface
Summary
TG-DTA, N2 adsorption and TPD, which were further used as a basis for a kinetic model of catalytic cracking. This model helps predict the changes in reactions rates caused by coke formation and, evaluates quantitatively how group characteristics of the feedstock, the catalyst-to-oil ratio and slop flow influence the coke content on the catalyst and the degree of catalyst deactivation. We defined that a total loss of acidity changes from 8.6 to 30.4 wt% for spent catalysts, and this depends on SAR content in feedstock and process variables. The results show that despite enriching the feedstock by saturates, the highest coke yields (4.6–5.2 wt%) may be produced due to the high content of resins (2.1–3.5 wt%).
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