Claims:1. A method for predicting kinematic viscosity of vacuum residue at a predetermined temperature to optimize selection of crude oils, the method comprising:
receiving, by a processor (102), physical parameters of the crude oil as an input, wherein the physical parameters comprise at least one of Vacuum Residue yield and Conradson Carbon Residue (CCR) content;
determining, by the processor (102), kinematic viscosity of a fraction of the crude oil from the physical parameters of the crude oil based on a first correlation model; and
generating an output based on the first correlation model corresponding to the input, wherein the output is the kinematic viscosity of the fraction of the crude oil at the predetermined temperature.
2. The method as claimed in claim 1, wherein the method comprises determining the kinematic viscosity of a heavy product blend from the kinematic viscosity of fraction of the heavy product blend based on a second correlation model.
3. The method as claimed in claim 2, wherein the heavy product blend is obtained by blending different fractions of the crude oil derived from different or same crude oils.
4. The method as claimed in claim 1, wherein the method comprises determining kinematic viscosity of the fraction at a second predetermined temperature from kinematic viscosity of the fraction of crude oil at the predetermined temperature based on a third correlation model.
5. The method as claimed in claim 1, wherein the fraction of the crude oil is Vacuum Residue of the crude oil.
6. The method as claimed in claim 1, wherein the physical parameters include one or more of API gravity, Sulphur content, Hydrogen content, Nitrogen content, Mercaptan value, Pour point, Saturates, Aromatics, Resins and Asphaltenes.
7. The method as claimed in claim 1, wherein the kinematic viscosity of the fraction of crude oil generated determines production requirements of Fuel oil, Low Sulphur Heavy Stock, Low Sulphur Fuel Oil, and bitumen.
8. The method as claimed in claim 1, wherein the predetermined temperature is in a range of 50 degree Celsius to 135 degree Celsius.
9. A system (100) for predicting kinematic viscosity at a predetermined temperature to optimize crude oil selection, the system (100) comprising:
a processor (102);
a database (112) comprising crude oil data, wherein the crude oil data comprises physical parameters of a crude oil;
a memory (104) coupled to the processor (102) and the database (112), the memory (104) comprising;
a first prediction module (114) to predict kinematic viscosity of a fraction of the crude oil from the physical parameters of the crude oil, wherein the physical parameters of the crude oil include at least one of vacuum residue yield and Conradson Carbon Residue (CCR) content; and
a second prediction module (116) to predict the kinematic viscosity of a heavy product blend from the kinematic viscosity of fractions of the heavy product blend.
10. The system (100) as claimed in claim 8, wherein the memory (104) comprises a third prediction module (118) to predict kinematic viscosity of fraction of crude oil at a second predetermined temperature from kinematic viscosity of the fraction at the predetermined temperature.
11. The system (100) as claimed in claim 8, wherein the predetermined temperature is in a range of 50 degree Celsius to 135 degree Celsius.
12. The system (100) as claimed in claim 8, wherein the memory (104) comprises a fourth prediction module (120) to predict the amount of optimal cutter stock requirement for evacuating vacuum residue.
13. The system (100) as claimed in claim 8, wherein the physical properties include one or more of API gravity, Sulphur content, Hydrogen content, Nitrogen content, Mercaptan value, Pour point, Saturates, Aromatics, Resins and Asphaltenes.
14. The system (100) as claimed in claim 8, wherein the heavy product blend is obtained by blending different fractions of the crude oils derived from different or same crude oils.
15. A method for estimating an amount of cutter stock for crude oil processing, wherein the method comprises:
determining, by a processor (102), kinematic viscosity of vacuum residue of a crude oil based on physical parameters of the crude oil, wherein the physical parameters comprise at least one of Vacuum Residue yield and Conradson Carbon Residue (CCR) content; and
calculating, by the processor (102), the amount of cutter stock based on the kinematic viscosity of vacuum residue of the crude oil.
16. The method as claimed in claim 14, wherein the cutter stock is a blend of one or more fractions of the crude oil.
17. The method as claimed in claim 14, wherein the cutter stock includes one or more of kerosene, gasoline, jet fuel, diesel, Naphtha, VGO, CLO, LCO, LSHS, FO, LSFO, and VR.
18. The method as claimed in claim 14, wherein the amount of cutter stock is calculated as a weight percentage of a refinery product.
19. The method as claimed in claim 14, wherein the crude oil processing includes optimal evacuation of vacuum residue from a vacuum distillation column.
20. The method as claimed in claim 14, wherein the physical parameters include one or more of API gravity, Sulphur content, Hydrogen content, Nitrogen content, Mercaptan value, Pour point, Saturates, Aromatics, Resins and Asphaltenes.
, Description:As Attached