Application of abnormal event detection technology to fluidized catalytic cracking unit

• US 7,567,887 B2
• Filed: 08/26/2005
• Issued: 07/28/2009
• Est. Priority Date: 09/10/2004
• Status: Active Grant

• Current Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Original Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Inventors: Dash, Sourabh K., Woo, Stephen S., Emigholz, Kenneth F.
• Primary Examiner(s): Cosimano; Edward R
• Application Number:
  US11/212,188
• Publication Number:
  US 20060073013A1
• Time in Patent Office: 1,432 Days
• Field of Search: 340/3.1, 340/3.43, 340/3.5, 340/3.51, 340/3.6, 340/3.61, 340/500, 340/511, 340/635, 340/653, 340/679, 340/960, 340/825, 340/853.2, 340/870.01, 340/870.07, 340/870.16, 700/28, 700/29, 700/30, 700/31, 700/32, 700/47, 700/48, 700/49, 700/90, 700/108, 700/174, 700/177, 702/33, 702/34, 702/127, 702/182, 702/183, 702/184, 702/185, 702/188, 706/62, 706/903, 706/904, 706/906, 706/911, 706/912, 706/914
• US Class Current: 702/182
• CPC Class Codes: C10G 11/18 according to the "fluidised-bed" technique

Abstract

The present invention is a method for detecting an abnormal event for process units of a Fluidized Catalytic Cracking Unit. The method compares the operation of the process units to a statistical and engineering models. The statistical models are developed by principle components analysis of the normal operation for these units. In addition, the engineering models are based on partial least squares analysis and correlation analysis between variables. If the difference between the operation of a process unit and the normal model result 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 process units of a fluidized catalytic cracking unit (FCCU) comprising:

(a) determining equipment groups and process operating modes of said FCCU to be covered by principal component analysis (PCA) models, wherein said equipment groups have minimal interaction with each other,

(b) comparing online measurements from the process units to a set of models including principal components analysis models for normal operation of the corresponding process units of said FCCU,

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

(d) determining the underlying cause of an abnormal condition in the FCCU.

2. 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.

3. The method of claim 1 wherein said set of models of normal operation for each process unit is either a principal component analysis model or an engineering model.

4. The method of claim 1 wherein said set of models includes models for said FCCU which is divided into operational sections of the FCCU system.

5. The method of claim 4 wherein there are ten operational sections.

6. The method of claim 4 wherein the ten operational sections include Reactor-Regenerator, Light Ends Towers, Cat Circulation, Stack Valves, Cyclones, Air Blower, Carbon Balance, Catalyst, Carryover to Main Fractionator, Wet Gas Compressor, Valve-Flow Models.

7. The method of claim 6 wherein said model further identifies the consistency between tags around a specific unit, air blower, regenerator cyclones, valves/flow and wet gas compressor, to indicate any early breakdown in the relationship pattern.

8. The method of claim 7 wherein said model further comprises suppressing model calculations to eliminate false positives on special cause operations.

9. 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 mode.

10. The method of claim 9 wherein said equipment groups include all major material and energy interactions in the same group.

11. The method of claim 10 where a list of abnormality monitors automatically identified, isolated, ranked and displayed for the operator.

12. The method of claim 10 wherein said equipment groups include quick recycles in the same group.

13. The method of claim 12 wherein said set of models of normal operations include principal component analysis models.

14. The method of claim 13 wherein set of models of normal operations includes engineering models.

15. The method of claim 10 wherein said principal component analysis models include process variables provided by online measurements.

16. The model of claim 15 wherein some measurement pairs are time synchronized to one of the variables using a dynamic filter.

17. The model of claim 15 wherein the process measurement variables affected by operating point changes in the process operations are converted to deviation variables.

18. The method of claim 15 wherein the principal components analysis model includes principal components selected by the magnitude of total process variation represented by successive components.

19. The method of claim 1 wherein said set of models of normal operation for each process unit is determined using principal components analysis (PCA), partial least squares based inferentials and correlation-based engineering models.

20. The method of claim 19 wherein said models include process variables values measured by sensors.

21. The method of claim 19 wherein said principal components analysis models for different process units include some process variable values measured by the same sensor.

22. The method of claim 19 wherein there are twelve abnormality monitors for said Fluidized Catalytic Cracking Unit.

23. The method of claim 22 wherein each of the abnormality monitors generates a continuous signal indicating the probability of an abnormal condition in the area.

24. The method of claim 19 wherein (a) determining said model begins with a rough model based on questionable data, (b) using said rough model to gather high quality training data, and improve the model, and (c) repeating step (b) to further improve the model.

25. The model of claim 24 wherein some pairs of measurements for two variables are brought into time synchronization by one of the variables using a dynamic transfer function.

26. The method of claim 24 wherein said training data includes historical data for the model of the processing unit.

27. The model of claim 26 wherein variables of process measurements that are affected by operating point changes in process operations are converted to deviation variables by subtracting the moving average.

28. The method of claim 19 where the operator is presented with diagnostic information at different levels of detail to aid in the investigation of the event.

29. The method of claim 26 wherein the principal components analysis model is chosen such that it includes principal components whose coefficients become about equal in size.

30. The method of claim 26 wherein said model includes transformed variables.

31. The method of claim 30 wherein said transformed variables include reflux to feed ratio in distillation columns, log of composition in high purity distillation, pressure compensated temperature measurement, sidestream yield, flow to valve position, and reaction rate to exp (temperature).

32. The method of claim 26 wherein said model is corrected for noise.

33. The method of claim 32 wherein said model is corrected by filtering or eliminating noisy measurements of variables.

34. The method of claim 26 wherein the measurements of a variable are scaled.

35. The method of claim 34 wherein the measurements are scaled to the expected normal range of that variable.

36. A system for abnormal event detection (AED) for some of the process units of a fluidized catalytic cracking unit, FCCU, of a petroleum refinery comprised of:

(a) an array of process measurement sensors,

(b) an on-line means including a set of models including principal component analysis models in the set using process measurements from said array of process measurement sensors describing operations of the process units of said FCCU, wherein said FCCU has been divided into equipment groups with minimal interaction between groups,

(c) a display which the on-line means including said set of models indicates if the current operation differs from expected normal operations so as to indicate the presence of an abnormal condition in the process unit, and

(d) a display which the on-line means including said set of models indicates the underlying cause of an abnormal condition in the FCCU process unit.

37. The system of claim 36 wherein said model for each process unit is either a principal component analysis model and/or an engineering model.

38. The system of claim 37 wherein a FCCU is partitioned into three operational sections with a principal components analysis model for each section.

39. The system of claim 38 wherein said principal components analysis model include process variables provided by online measurements.

40. The system of claim 38 wherein said principal components analysis model further comprises suppressing model calculates to eliminate operator induced notifications and false positives.

41. The system of claim 40 wherein said model includes transformed variables.

42. The system of claim 40 wherein the process measurement variables affected by operating point changes in the process operations are converted to deviation variables.

43. The system of claim 41 wherein some measurement pairs are time synchronized to one of the variables using a dynamic filter.

44. The system of claim 41 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.