Application of abnormal event detection technology to hydrocracking units

• US 20060074598A1
• Filed: 08/26/2005
• Published: 04/06/2006
• Est. Priority Date: 09/10/2004
• Status: Abandoned Application

• Current Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Original Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Inventors: Kendi, Thomas A., Woo, Stephen S., Emigholz, Kenneth F.
• Application Number:
  US11/212,211
• Publication Number:
  US 20060074598A1
• Time in Patent Office: Days
• US Class Current: 702/185
• CPC Class Codes: C10G 47/36 Controlling or regulating
• CPC Class Codes: G05B 23/021 adopting a different treatment of each operating region or a different mode of the monitored system, e.g. transient modes; different operating configurations of monitored system
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G06F 18/2135 based on approximation criteria, e.g. principal component analysis

Abstract

The present invention is a method for detecting an abnormal event for process units of a hydrocracking unit. The method compares the operation of the process units to a model developed by principle components analysis of normal operation for these units. If the difference between the operation of a process unit and the normal operation indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

Claims

1. A method for abnormal event detection (AED) for some of process units of a hydrocracker unit of a petroleum refinery comprised of:

(a) comparing online measurements from the process unit to a set of models for normal operation of the corresponding process units,

(b) determining if the current operation differs from expected normal operations so as to indicate the presence of an abnormal condition in a process unit,

(c) assisting the process operator to determine the underlying cause of an abnormal condition in the HDC process unit, and

(d) performing corrective action to return the unit to normal operation.

2. The method of claim 1 wherein said set of models correspond to equipment groups and operating modes, one model for each group which may include one or more operating modes.

3. The method of claim 1 wherein said set of models correspond to equipment groups and process operating modes, one model for each group and each mode.

4. The method of claim 2 wherein said equipment groups include all major material and energy interactions in the same group.

5. The method of claim 4 wherein said equipment groups include quick recycles in the same group.

6. The method of claim 5 wherein said set of models of normal operations include principle component models.

8. The method of claim 1 wherein said set of models of normal operation for each process unit is either a Principle Components model or an engineering model.

9. The method of claim 8 wherein a hydrocracker process unit is partitioned into functional sections with a Principle Components model for each section.

10. The method of claim 9 where there are three functional sections.

11. The method of claim 4 wherein said Principle Components include process variables provided by online measurements.

12. The method of claim 10 wherein the three functional sections of the hydrocracking process unit include:

1st stage hydrotreating reactor (R1), 2nd stage hydrocracking reactor (R2), 3rd stage hydrocracking reactor (R3), 1st &

2nd stage LP/HP separators, stabilizer tower, splitter tower, and the reciprocal compressor.

13. The method of claim 6 further comprising additional models to determine the consistency between selected control valves and flow meters, process analyzers and secondary measurements, and the onset of temperature and pressures oscillations in the reactor beds.

14. The method of claim 4 wherein said model further comprises suppressing model calculates to eliminate operator induced notifications and false positives.

15. The method of claim 2 wherein:

(a) deriving said model begins with obtaining an initial model based upon questionable data, (b) use of said initial model to refine the data and improve the model, and (c) iteratively repeating step (b) to improve the model.

16. The method of claim 9 wherein said training data set includes historical data of the processing unit for model development.

17. The method of claim 9 wherein said model includes transformed variables.

18. The method of claim 9 wherein said transformed variables include reflux to total product flow in distillation columns, log of composition and overhead pressure in distillation columns, pressure compensated temperature measurements, flow to valve position and bed differential temperature and pressure.

19. The model of claim 9 wherein some measurement pairs are time synchronized to one of the variables using a dynamic filter.

20. The model of claim 9 wherein the process measurement variables affected by operating point changes in the process operations are converted to deviation variables.

21. The method of claim 9 wherein the measurements of a variable are scaled prior to model identification

22. The method of claim 19 wherein the measurements are scaled by the expected normal range of that variable.

23. The method of claim 9 wherein the number of principle components is selected by the magnitude of total process variation represented by successive components.

7. The method of 6 wherein set of models of normal operations includes engineering models.

24. A system for abnormal event detection (AED) for some of the hydrocracker process units of a petroleum refinery comprised of:

(a) a set of models for the process units describing operations of the process units,

(b) a display which indicates if the current operation differs from expected normal operations so as to indicate the presence of an abnormal condition in the process unit,

(c) a display which indicates the underlying cause of an abnormal condition in the HDC process unit.

25. The system of claim 24 wherein said model for each process unit is either a Principle Components model or an engineering model.

26. The system of claim 24 wherein a hydrocracker unit is partitioned into three operational sections with a Principle Components model for each section.

27. The system of claim 26 wherein said Principle Components include process variables provided by online measurements.

28. The system of claim 26 wherein the three operational sections of the hydrocracking process unit include:

1st stage hydrotreating reactor (R1), 2nd stage hydrocracking reactor (R2), 3rd stage hydrocracking reactor (R3), 1st and 2nd stage LP/HP separators, stabilizer tower, splitter tower, and the reciprocal compressor.

29. The system of claim 28 wherein additional models determine the consistency between selected control valves and flow meters, process analyzers and secondary measurements, and the onset of temperature and pressures oscillations in the reactor beds.

30. The system of claim 26 wherein said model further comprises suppressing model calculates to eliminate operator induced notifications and false positives.

31. The system of claim 25 wherein:

(a) deriving said model begins with obtaining an initial model based upon questionable data, (b) use of said initial model to refine the data and improve the model, and (c) iteratively repeating step (b) to improve the model.

32. The system of claim 31 wherein said training data set includes historical data of the processing unit for model development.

33. The system of claim 32 wherein said model includes transformed variables.

34. The system of claim 33 wherein said transformed variables include reflux to total product flow in distillation columns, log of composition and overhead pressure in distillation columns, pressure compensated temperature measurements, flow to valve position and bed differential temperature and pressure.

35. The system of claim 32 wherein some measurement pairs are time synchronized to one of the variables using a dynamic filter.

36. The system of claim 32 wherein the process measurement variables affected by operating point changes in the process operations are converted to deivation variables.

37. The system of claim 32 wherein the measurements of a variable are scaled prior to model identification.

38. The system of claim 37 wherein the measurements are scaled by the expected normal range of that variable.

39. The system of claim 32 wherein the number of principle components is selected by the magnitude of total process variation represented by successive components.