Abstract:
In a petroleum refinery, a crude distillation unit (CDU) is a major unit operation that
separates the crude oil into its components. The CDU separates the crude oil into
components, including light gas streams of naphtha, kerosene, diesel, atmospheric gas oil,
and residue. Cutpoint temperatures dictate the quality and quantity of these components,
hence need for a complete and fitting model for cut point temperature management
originates. The cutpoint temperature majorly relies on crude oil process conditions and
composition. The CDU consumes the most extensive amounts of energy. Consequently,
significant energy-saving opportunities arise. In this study, Pakistani Crudes from the
Zamzama and Kunnar field was used, which was separated into their components via a
CDU. This study also examines the effects of uncertainty in process conditions, such as
flow rates, pressure, temperature, and feed compositions. A thermodynamic approach was
used to carry out the exergy analysis. A hybrid approach based on the Taguchi method
and genetic algorithm is used to generate optimum cutpoint temperature for different
variants of feed composition and process conditions, generating 228 data sets. An artificial
neural networks (ANN) model is developed using these datasets to predict the optimum
cutpoint temperature by eliminating the need for the hybrid approach. For the ANN
predicted cutpoints up to 38.93% decrease in energy required per kilo barrel of diesel and
8.2% increase in diesel production was observed for Zamzama and up to 18.87% decrease
in energy required per kilo barrel of diesel and 33.96% increase in diesel production was
observed for Kunnar. In addition to minimizing the E/V values and maximizing flow rates,
exergy efficiency was found to be increased by 163.5% and 120% for the Zamzama and
Kunnar crude respectively.
