Patent Number: 
Section: claims

1. A method of determining the performance of a processing apparatus for processing particulate material, the method comprising:capturing with a first sensor an image of radiation after the radiation has passed through a sample of particulate material to be fed to the processing apparatus, and which radiation has been altered by the particulate material;capturing with a second sensor an image of radiation after the radiation has passed through a sample of particulate material which has exited from the processing apparatus, and which radiation has been altered by the particulate material;determining from the captured images a parameter of the particulate material in each of the sample of material to be fed to the apparatus, and the sample which exits the apparatus; anddetermining a performance index of the apparatus by comparing the parameter of the material which has exited the apparatus with respect to an expected parameter of the material which has exited the apparatus, and having regard to the parameter of the particulate material fed to the apparatus. 2. The method of claim 1 wherein the processing apparatus is a separator and the sample of the material exiting the apparatus is a first sample of material directed to overflow comprising product, and a second sample of material directed to underflow and comprising refuse, and wherein the method is further comprised of capturing an image of radiation which is emitted from the first sample of particulate material, and capturing an image of radiation which is emitted from the second sample of particulate material, and determining a parameter of the particulate material of both the sample of material to be fed to the apparatus, and the sample which has exited the apparatus, such that a determination of performance index can be made as to how much material should be expected to be directed to overflow and how much should be expected to be directed to underflow if the separator is performing satisfactorily, and from the determination of the parameter of the first sample and the parameter of the second sample, an indication of amounts of material actually directed to underflow and overflow can be made, and by comparing amounts actually directed to underflow and overflow with the expected amounts to be directed to underflow and overflow, a determination of the performance index of the separator can be made. 3. The method of claim 2 wherein the separator is a dense medium cyclone. 4. The method of claim 3 wherein the parameter is the density of the particulate material or mineral content, and the amount of material which is directed to underflow and the amount of material which is directed to overflow is determined by the densities or mineral content of material which is directed to underflow and the densities or mineral content of material which is directed to overflow, so that if particular densities or mineral content are directed to underflow which should in fact be directed to overflow, a determination that the separator is not performing satisfactorily can be made. 5. The method of claim 2 wherein the determined parameter is the density of the particulate material or mineral content, and the amount of material directed to underflow and the amount of material directed to overflow is determined by the densities or mineral content of material directed to underflow and the densities or mineral content of material directed to overflow, so that if particular densities or mineral content are directed to underflow which should in fact be directed to overflow, a determination that the separator is not performing satisfactorily can be made. 6. The method of claim 1 wherein the radiation is x-ray radiation. 7. The method of claim 1 wherein the apparatus is a crusher for crushing the particulate material to a smaller size, and the parameter is the size or density or mineral content of the particulate material. 8. The method of claim 1 wherein the apparatus is a calcinating apparatus and the parameter is the amount of calcination of the particulate material. 9. The method of claim 1 wherein the alteration of the radiation is the attenuation of the radiation. 10. The method of claim 1 wherein the radiation is captured by the first or second sensor by radiating the particulate material with a source of radiation so that the radiation passes through the particulate material to the sensor. 11. The method of claim 1 wherein the particulate material is inherently self-radiating or made self-radiating by including radiation producing material within the particulate material. 12. The method of claim 1 wherein the captured image is made up of a number of pixels, each of which has an intensity level depending on the degree to which the radiation has been attenuated by the particle through which the radiation passes. 13. The method of claim 12 wherein the intensity level is at least one of a gray scale and an RGB colour. 14. The method of claim 1 wherein the determination of the parameter includes the steps of generating local image windows within which only one particle or a group of overlapped particles presents by identifying boundaries of individual particles or groups of overlapped particles, calculating image characteristics of each identified local image window, calculating indicator variables summarising the information contained in the image characteristics by multivariate statistical methods, and determining from indicator variables at least one parameter of each particle or a group of overlapped particles in the particulate material using a predictive mathematical model. 15. The method of claim 14 wherein the image characteristics are selected from the group consisting of one or more of statistical features based on grey-level or RGB colour histogram which is a plot of the frequency of values of an intensity versus intensities of pixels with a local image window, textures features based on grey-level co-occurrence matrix, Gabor features based on Gabor filters, and features based on wavelet transforms. 16. The method of claim 15 wherein statistical features based on grey-level or RGB colour histogram are selected from the group consisting of one or more of total number of pixels, mean, median, standard deviation, kurtosis and skew, and the textures features based on grey-level co-occurrence are selected from the group consisting of one or more of entropy, contrast, correlation, energy, local homogeneity, maximum probability, sum entropy and difference entropy. 17. The method of claim 15 wherein the method includes obtaining a measure of the thickness of the material to provide a thickness measure and providing the thickness measure as a further characteristic feature to determine the parameter. 18. The method of claim 17 wherein the thickness measure is obtained by light sources to illuminate the material and cameras for detecting the illuminated material so that a three-dimensional image of the material is obtained from which the thickness measure is determined. 19. The method of claim 14 wherein the mathematical model for the determination of the parameter comprises artificial neural networks or multivariate regression models. 20. The method of claim 19 further comprising the step of training and validating the artificial neural network model with a number of calibration samples with captured X-ray images and known parameters and thickness of the particulate material. 21. The method of claim 19 further comprising calculating parameters in a predictive mathematical model using a number of image characteristics or indicator variables with known thickness of the particulate material. 22. A device for determining the performance of a processing apparatus for processing particulate material, the device comprising:a first sensor for capturing an image of radiation after the radiation has passed through a sample of particulate material to be fed to the processing apparatus, and which radiation has been altered by the particulate material, and a second sensor for capturing an image of radiation after the radiation has passed through a sample of particulate material which has exited from the processing apparatus, and which radiation has been altered by the particulate material; anddata processing means for determining from the captured images a parameter of the particulate material in each of the sample of material to be fed to the apparatus, and the sample which has exited the apparatus, and for determining a performance index of the apparatus by comparing the parameter of the particulate material which has exited the apparatus with respect to an expected parameter of the particulate material which has exited the apparatus and having regard to the parameter of the particulate material fed to the apparatus. 23. The device of claim 22 wherein the processing apparatus is a separator and the sample of the material existing the apparatus is a first sample of material directed to overflow comprising product, and a second sample of material directed to underflow comprising refuse, and wherein the method is further comprised of capturing an image of radiation which is emitted from the first sample of particulate material, and capturing an image of radiation which is emitted from the second sample of particulate material, and the data processing means determines parameters of the particulate material of both the sample of material to be fed to the apparatus, and the sample exiting the apparatus, such that a determination of performance index can be made as to how much material should be expected to be directed to overflow and how much should be expected to be directed to underflow if the separator is performing satisfactorily, and from the determination of the parameter of the first sample and the parameter of the second sample, an indication of amounts of material actually directed to underflow and overflow can be made, and by comparing the amounts actually directed to underflow and overflow with the expected amounts to be directed to underflow and overflow, a determination of the performance index of the separator can be made. 24. The device of claim 23 wherein the separator is preferably a dense medium cyclone. 25. The device of claim 24 wherein the parameter is the density of the particulate material or mineral content, and the amount of material which is directed to underflow and the amount of material which is directed to overflow is determined by the densities or mineral content of material which is directed to underflow and the densities or mineral content of material which is directed to overflow, so that if particular densities or mineral content are directed to underflow which should in fact be directed to overflow, a determination that the separator is not performing satisfactorily can be made. 26. The device of claim 23 wherein the determined parameter is the density of the particulate material or mineral content, and the amount of material directed to underflow and the amount of material directed to overflow is determined by the densities or mineral content of material directed to underflow and the densities or mineral content of material directed to overflow, so that if particular densities or mineral content are directed to underflow which should in fact be directed to overflow, a determination that the separator is not performing satisfactorily can be made. 27. The device of claim 22 wherein the apparatus is a crusher for crushing the particulate material to a smaller size, and the parameter is the size or density or mineral content of the particulate material. 28. The device of claim 22 wherein the apparatus is a calcinating apparatus and the parameter is the amount of calcination of the particulate material. 29. The device of claim 22 wherein the device also includes a first and second source of radiation for producing radiation for irradiating the particulate material, so the radiation can emit from the particulate material to be captured by the first and second sensor, respectively. 30. The device of claim 29 wherein each sensor and each source of radiation are provided in a housing and conveyors are provided for conveying the particulate material passed between each source of radiation and each sensor, so the radiation can pass through the particulate material, and so the respective images can be captured by the sensor. 31. The device of claim 30 wherein each sensor is a X-ray linear array detector based various detecting techniques, such as scintillator and photoconductors, or a CCD X-ray detector having a plurality of pixels for forming an image. 32. The device of claim 22 wherein each captured image of radiation is made up of a number of pixels, each of which has an intensity level depending on the degree to which the radiation has been attenuated by the particle through which the radiation passes. 33. The device of claim 32 wherein the intensity level is at least one of a gray scale level and an RGB colour. 34. The device of claim 22 wherein the processing means is for determination of the parameter by generating local image windows within which only one particle or a group of overlapped particles presents by identifying boundaries of individual particles or groups of overlapped particles, calculating image characteristics of each identified local image window, calculating indicator variables summarising the information contained in the image characteristics by multivariate statistical methods, and determining from indicator variables at least one parameter of each particle or a group of overlapped particles in the particulate material using a predictive mathematical model. 35. The device of claim 34 wherein the image characteristics are selected from the group consisting of one or more of statistical features based on grey-level or RGB colour histogram which is a plot of the frequency of values of an intensity versus intensities of pixels with a local image window, textures features based on grey-level co-occurrence matrix, Gabor features based on Gabor filters, and features based on wavelet transforms. 36. The device of claim 35 wherein the statistical features based on grey-level or RGB colour histogram are selected from the group consisting of one or more of total number of pixels, mean, median, standard deviation, kurtosis and skew. 37. The device of claim 35 further comprising a thickness measuring device for providing a thickness measure of the material. 38. The device of claim 37 wherein the thickness measuring device consists of light sources to illuminate the material and cameras for detecting the illuminated material so that a three-dimensional image of the material is obtained from which the thickness measure is determined. 39. The device of claim 37 wherein the processing means further comprises artificial neural networks or multivariate regression models for providing a predictive mathematical model for the determination of the parameter. 40. The device of claim 39 wherein the processing means is also for training and validating the artificial neural network model with a number of calibration samples with captured X-ray images and known parameters and thickness of the particulate material. 41. The device of claim 39 wherein the processing means is also for calculating parameters in the predictive mathematical model using a number of image characteristics or indicator variables with known thickness of the particulate material.