METHOD AND SYSTEM FOR TRACKING MATERIAL

• US 20130272829A1
• Filed: 10/28/2011
• Published: 10/17/2013
• Est. Priority Date: 10/29/2010
• Status: Active Grant

• Current Assignee: Technological Resources Pty. Ltd. (Rio Tinto Limited), Sydney The University of Business Liaison Office
• Original Assignee: University of Sydney
• Inventors: Durrant-Whyte, Hugh, Melkumyan, Arman, Innes, Chris, Nettleton, Eric
• Granted Patent: US 9,678,052 B2
• Time in Patent Office: Days
• US Class Current: 414/399
• CPC Class Codes: B65G 67/04 Loading land vehicles loading or unloading boats to or from land vehicles B60P3/10
• CPC Class Codes: G01G 19/08 for incorporation in vehicles
• CPC Class Codes: G01N 33/24 Earth materials G01N33/42 takes precedence

Abstract

Methods and systems are described for tracking material through a production chain or operational process chain in which the material is transferred via a plurality of spatially distinct lumped masses of material (12, 14, 16, 18). A dynamic state space (430) is maintained descriptive of the plurality of spatially distinct lumped masses of material, wherein a quantity of entries in the dynamic state space is augmented or diminished dependent on a quantity of spatially distinct lumped masses being tracked. Measurements relating to an observed lumped mass of material are fused into the dynamic state space and a dynamic covariance matrix to provide an updated estimate of material in the plurality of spatially distinct lumped masses of material.

Claims

1. A method for tracking material through a production chain or operational process chain in which the material is transferred via a plurality of spatially distinct lumped masses of material, the method comprising:

maintaining a dynamic state space descriptive of the plurality of spatially distinct lumped masses of material, wherein a quantity of entries in the dynamic state space is varied dependent on a quantity of spatially distinct lumped masses being tracked;

maintaining a dynamic covariance matrix associated with the dynamic state space, wherein a dimension of the dynamic covariance matrix is varied dependent on a quantity of spatially distinct lumped masses of material being tracked;

receiving one or more measurements relating to an observed lumped mass of material; and

fusing the received one or more measurements into the dynamic state space and dynamic covariance matrix to provide an updated estimate of material in the plurality of spatially distinct lumped masses of material.

2. The method of claim 1 wherein each entry in the dynamic state space comprises one or more values descriptive of a corresponding spatially distinct lumped mass of material.

3. The method of claim 1 wherein maintaining the dynamic state space comprises augmenting the state space by adding an additional entry corresponding to an additional spatially distinct lumped mass.

4. The method of claim 1 wherein maintaining the dynamic state space comprises removing an entry from the dynamic state space if a corresponding spatially distinct lumped mass of material is removed from the tracked production chain.

5. The method of claim 1 wherein maintaining the dynamic state space comprises removing an entry from the dynamic state space if a corresponding lumped mass of material is combined with another one of the plurality of lumped masses of material.

6. The method of claim 1 wherein maintaining the dynamic covariance matrix comprises augmenting the covariance matrix by adding additional rows and columns corresponding to an additional spatially distinct lumped mass of material.

7. The method of claim 1 wherein maintaining the dynamic covariance matrix comprises removing rows and columns from the covariance matrix that correspond to a spatially distinct lumped mass of material that is removed from the tracked production chain or combined with another spatially distinct lumped mass of material.

8. The method of claim 2 wherein the one or more values descriptive of a spatially distinct lumped mass of material comprise an extensive lumped mass property defining an amount of material present in the spatially distinct lumped mass.

9. The method of claim 8 wherein the extensive lumped mass property comprises at least one of lumped-mass volume and lumped-mass mass.

10. The method of claim 2 wherein the one or more values descriptive of a spatially distinct lumped mass of material comprise an intensive lumped mass property defining a characteristic of the material present in the spatially distinct lumped mass.

11. The method of claim 10 wherein the intensive lumped mass property comprises at least one of a chemical composition, density and a fragmentation level.

12. The method of claim 10 further comprising:

determining a combined mean and a combined variance for an intensive lumped mass property of a combined lumped mass if a first lumped mass is combined with a second lumped mass.

13. The method of claim 12 wherein determining the combined mean and variance comprises finding a covariance union that includes the means and variances or the first and second lumped masses.

14. The method of claim 13 wherein finding the covariance union comprises the use of weighted averages dependent on a mass of the first lumped mass and a mass of the second lumped mass.

15. The method of claim 12 wherein the intensive lumped mass property of the first lumped mass of material and the second lumped mass of material are treated as Gaussian distributions and wherein determining the combined mean and combined variance comprises calculating an average Gaussian distribution from a sum of the Gaussian distributions of the first and second lumped masses of material.

16. The method of claim 1 comprising:

estimating a material loss that occurs when material is transferred from a first spatially distinct lumped mass of material to a second spatially distinct lumped mass of material.

17. The method of claim 16 comprising:

augmenting the dynamic state space to include a new entry descriptive of the material loss.

18. The method of claim 1 comprising:

receiving data from a scan of a surface of material in a spatially distinct lumped mass;

applying a Gaussian Process to the received data to estimate a volume of material in the spatially distinct lumped mass.

19. The method of claim 18 comprising estimating the volume by approximating a triple integral of a set of resultant points estimated by the Gaussian Process.

20. The method of claim 1 comprising:

applying a Kalman filter to fuse the received measurements into the dynamic state space and dynamic covariance matrix.

21. The method of claim 1 wherein the production chain is an open pit mine and the spatially distinct lumped masses of material are selected from the group consisting of:

a grade-block lumped mass comprising material in a grade block;

an excavator-bucket lumped mass comprising material in an excavator bucket;

an excavator-bucket-loss lumped mass comprising material spilt during transfer from an excavator bucket to a haul truck;

a haul-truck lumped mass comprising material loaded into a haul truck;

a stockpile lumped mass comprising material in a stockpile; and

a haulage lumped mass comprising material removed from a stockpile.

22. The method of claim 1 wherein the plurality of spatially distinct lumped masses of material define a sequence through the production chain and wherein observations on an observed lumped mass in the sequence are used to improve an estimate of material in another lumped mass that is earlier in the sequence.

23. The method of claim 1 further comprising determining, based on the dynamic state space and the dynamic covariance matrix, how much mass in a first specified lumped mass of material derives from a second specified lumped mass.

24. A system for tracking material through a mining production chain in which the material is transferred via a plurality of spatially distinct lumped masses of material, the system comprising:

a plurality of sensors for measuring attributes of the spatially distinct lumped masses of material; and

a processor in data communication with the plurality of sensors and comprising instructions that, in use, cause the processor to;

maintain a dynamic state space descriptive of the plurality of spatially distinct lumped masses, wherein a quantity of entries in the dynamic state space is varied dependent on a quantity of spatially distinct lumped masses of material being tracked;

receive one or more measurements from the plurality of sensors relating to an observed lumped mass of material; and

fuse the received one or more measurements into the dynamic state space to provide an updated estimate of material in the plurality of spatially distinct lumped masses of material.

25. The system of claim 24 wherein the plurality of sensors includes at least one of:

a mass sensor arrangement that provides a measure of a mass of a spatially distinct lumped mass of material;

a volume sensor arrangement that provides a measure of a volume of a spatially distinct lumped mass of material;

a location sensor arrangement that provides a measure of a location of a spatially distinct lumped mass of material;

a grade sensor arrangement that provides a measure of a composition of a spatially distinct lump of material.

26. The system of claim 25 wherein the mass or volume sensor arrangement comprises:

a scanner that, in use, scans a surface of an observed lumped mass of material and outputs scan data; and

a processor that receives the scan data and estimates a volume or mass in the observed lumped mass of material.

27. The system of claim 26 wherein the scanner comprises at least one of a radar, a laser and a camera.

28. The system of claim 24 wherein at least one sensor is mounted on a vehicle.

29. The system of claim 24 comprising:

a data source having an estimated in-ground model describing a composition of material that in use is excavated in the mining production chain.

30. The system of claim 24 wherein the estimate of material comprises an estimate of masses of the spatially distinct lumped masses of material in the mining production chain.

31. The system of claim 24 wherein the estimate of material comprises an estimate of volumes of the spatially distinct lumped masses of material in the mining production chain.

32. The system of claim 30 wherein the estimate of material further comprises an estimate of at least one intensive property of the spatially distinct lumped masses of material in the mining production chain.

33. A system for tracking material through a mining production chain, comprising:

a) at least one excavator having;

i) an excavator sensor that in use scans a surface of material to be excavated; and

ii) a location sensor,

wherein, based on the surface scan, an associated processor estimates a quantity of material excavated by the excavator as a first spatially distinct lumped mass of material;

b) at least one haul truck that in use receives excavated material from the at least one excavator, wherein material loaded into the haul truck comprises a second spatially distinct lumped mass of material and wherein, in use, material is offloaded from the at least one haul truck to define at least a third spatially distinct lumped mass of material;

c) a monitoring system that tracks movement of the at least one haul truck; and

d) a material-tracking processor in data communication with the at least one excavator, the at least one haul truck and the monitoring system, wherein the material tracking processor maintains a dynamic state space descriptive of the first, second and third spatially distinct lumped masses of material and fuses information characterising the material in the mining production chain to provide an updated estimate of the spatially distinct lumped masses of material.