This thesis deals with machine vision in the context of the mining, mineral and metal industry (MMMI). Professor Heikki Hyötyniemi also contributed a lot to this thesis, especially in the early stages of the work.
List of Publications
Contributions of the Author – Publications
Nomenclature
NIC network interface card NIPALS algorithm for computing PLS OLE object connectivity and embedding OPC OLE for process control. PAL Phase Alternate Line (color image standard) PC Principal Component or Personal Computer PCA Principal Component Analysis.
Contents
Introduction
- Background and Motivation
- Objectives
- Summary of Publications
- Contributions of the Author – Thesis
- Structure and Organisation of the Thesis
To study machine vision in the context of the mineral beneficiation process using the Pyhäsalmi mine as an example. And specifically; to gain information on the potential of machine vision in the control of the flotation process.
State of the Art in Machine Vision
Particle Size Measurement Systems
What all these systems have in common is that they are based on photographic images of the target. This allows weight information to be used not only in reducing the calculated ratios to actual mass flow values, but also in the calibration model.
Flotation Analysis Systems
To the best of the author's knowledge, there is no published literature reporting the details or use of PlantVision for IPO. To the best of the author's knowledge, SOLBAS™ technology is not available for slurry analysis in a similar environment to that described in Chapter 6.
The Test Bench – Pyhäsalmi Mine
History & Geology
The mine was gradually deepened after it was noticed that the ore body lay as a narrow band. At the same time the mine was completely modernized and, after three years of construction, a completely new mine was opened in 2001.
Production
Outokumpu Oy conducted thorough geological research and decided to open a mine in Pyhäsalmi, which was eventually started on March 1, 1962 as an open-pit mine. For a while it seemed that the mine would be completely exhausted by the year 2000, but fortunately a new large lens-shaped deep ore body was found in 1996 [45].
Process Description
- Underground Operations
- Concentration Plant
In the first of three phases, lumps and fine ore are mixed together (LM2 in Figure 3.11). This is one of the motivations for developing the particle size distribution analysis system described in Chapter 4 (courtesy of Pyhäsalmi Mine Oy). However, xanthate molecules do not naturally attach to the surface of zinc mineral particles.
Camera location of the single cell analysis point is marked with an arrow. courtesy of Pyhäsalmi Mine Oy). Therefore, the coarser fraction of the flotation tailings (see Fig. 3.8) is transported back to the mine and used for backfilling.
Particle Size Distribution Analysis
Motivation & Initial Considerations
- Considered approaches
If this distribution were known, it would give an indication of future changes in the size distribution at the grinding station approx. 1-3 days in advance. The corrective actions were initially to be taken by controlling the jaw spacing of the crusher, but during the research it was noticed that the fan spacing used for blasting had a significant effect on the size distribution, as reported by Larinkari [60]. Therefore, the control should be based on a combination of the fan distance and the jaw setting, which means that the new information can also be used in mine planning.
This was based on a simple idea that, when illuminated with a single light source at a relatively shallow angle, the larger particles will cast longer shadows than the smaller particles, as illustrated in the left part of Fig. Since there are distinct intensity changes. between dark shadows and other illuminated areas, as can be seen in the left part of the Fig.
Shadow Based Analysis
- Selected System Setup
- Image Analysis
- Shadow Histogram Calculations
- Data Collection & Modelling
- Improvements & Neural Network Validation
- Software Components
- Reasons for the Shift to 3D Analysis
- The New Data
- Segmentation
- Virtual Sieving and Volume Estimation
- Calibration Models
- Results & Considerations
This was partly the reason for purchasing a belt weigher for the HKU2 belt. Further details of the improved PLS model and neural network validation are given in [61]. Furthermore, the kernel is implemented as a COM object (Component Object Model) [6] that can be automatically generated using MATLAB™ tools.
Although the improved PLS model produced satisfactory results, there were problems with the lifetime of the halogen lamps used in the analysis. It was necessary to get the dimensional information in the direction of the tire; the other two directions were covered by the scanner.
Flotation Froth Analysis
Motivation
As the rapid development of camera and computer technology has enabled machine vision techniques to cover an increasing range of applications, the possibility of using image analysis to control mineral flotation generated great interest in the mineral engineering community in the 1990s (e.g. [74] ). While most such developments took place in the laboratory, some instrument systems were tested online in flotation plants (e.g. and were reported to be useful for classifying foams or for extracting physical properties such as average bubble size, size distribution and shape parameters of bubbles, foam velocity as well as color parameters Characterization of Flotation Froth Structure and Color by Machine Vision (ChaCo)” was launched in 1997, initiating the development of a flotation foam analyzer for the Pyhäsalmi mine.
The development of the image analysis system, its extension to multi-camera system and the achieved results are described below.
Single Cell Analysis
- Froth Analyser
- Calculated Variables
Foam Color: During operator investigation, operators noted that foam color is related to the mineral concentration of the foam. In some cases, bubble size is also related to the mineral load of the foam. The actual implementation of the algorithm is done in the Discrete Fourier Transform (DFT) domain in order to minimize the computational burden.
Now the number of pixels above a certain threshold gives an estimate of the speed at which the bubble collapses. This number (which takes values between 0% and 100%) is the outcome of the algorithm, which is called the load variable.
Extension to Multi-Camera-Analysis
- Froth Image Analyser-software
- FrothEye-software
- Froth Height Measurement
The flexibility of the programming environment is achieved by using modular design and code generation. As mentioned, this architecture was also used in the design of the software for particle size analysis (see subsection 4.2.6). An additional result of the research was a new way to measure the foam height and foam thickness.
To obtain the position of the projected line, a laser detection algorithm was developed and added to the core of the FrothEye software. Other algorithms have configurable regions of interest (ROIs) that can be chosen to be in the middle part of the image.
Results
- Dependencies
- Closed Loop Control
- Support for the Operators
The next three figures illustrate the dependencies between the image variables of the coarser bank and the final zinc concentrate grade. The next step was to predict the zinc degree of the cleaner cell (ZnR Zn%) using only the image variables as predictors. This is due to the changes in the time delay between the imaging variables and the XRF analysis of the final product.
A similar control modification of the cyanide2 setpoint in the copper circuit purge bank is reported in [P4]. Its purpose is to prevent activation of the sphalerite mineral because it flows in the following zinc circuit.
Grade Estimation with Colour and
Froth Analysis
- Prototype for On-Line Measurements
- Results
This section describes the method used for the comparison between an RGB camera and a spectrophotometer. When looking at the image it is easy to see that at different positions of the line there will be both total reflection points (high intensity values) and bubble edges (low intensity values). The RGB camera and spectrophotometer were installed on the last cell of the zinc cleaning circuit (see Figure 5.6), which produces the final concentrate.
The recorded variables were the mean values of R, G, B components, the mean intensity of the RGB image, line spectra and the zinc, copper and iron content of the concentrate stream. From the images, however, it appears that the PLS model gives slightly better results.
Slurry Analysis
- Initial Laboratory Tests
- Prototype for On-Line Measurements
- Modelling & Results
Furthermore, it was thought that the prediction accuracy of the XRF analyzer could be improved with this technique. The results of the laboratory analysis for the three samples are shown in Table 6.2 below. 6.5, where there are 11 spectra drawn with different line types depending on the origin of the sample.
However, as expected, a standard PLS model would not be sufficient due to the changes in the operating point of the process (these are mainly due to variation in the properties of the incoming ore). An example of the possibilities of this technique in connection with sudden process disturbances (discussed at the beginning of this section) is shown in fig.
Current Status of the Research
The idea is to collect operator experience on the usefulness of these new measurements in the management of the flotation process. To help operators detect sudden disturbances, an animated arrow estimating the gradient direction is displayed next to the numerical values. 6.7, the yellow arrow next to the Zn reading would quickly turn to point down and its color would change to red.
Conclusions
The research and related results are described in Chapter 6 and, although a work in progress, the results obtained so far (and reported in this thesis) confirm its applicability in the context of mineral flotation. In fact, the excellent results obtained during this study motivated Outotec Minerals Oy to consider commercialization and it is likely that these measurement capabilities will be available in future versions of the Courier® XRF analyzers. The work presented in this thesis will continue; as mentioned, the results presented in Chapter 6 will likely be commercialized, the use of the FrothEye software and supporting tools will continue in Pyhäsalmi, and new areas of application for the machine vision platform will be sought.
In addition, the modeling of the ore transport chain will continue and the advantages of spectral measurements will be studied with the current particle size analysis system (actually, spectral measurements are already integrated with other measurements). The first results of this approach will be reported in a forthcoming publication by Pietilä and Haavisto [84].
27] Haavisto O., Kaartinen J., "Multichannel reflectance spectral evaluation of zinc and copper flotation sludges", International Journal of Mineral Processing. 28] Haavisto, O., Kaartinen J., Hyötyniemi H., "Improving the accuracy of flotation float measurements by data matching", 6th International Conference on Intelligent Materials Processing and Manufacturing - IPMM-2007, Salerno, Italy, June 24- 29, 2007. 31] Hasu V., "Design of Experiments in Flotation Foam Performance Analysis", Helsinki University of Technology, Control Engineering Laboratory, Finland, Report 114, 1999.
73] Moolman D.W., Aldrich C., Van Deventer J.S.J., Bradshaw D.B., "Characterization of foam surfaces and relation to process performance using connectionist image processing techniques", Mineral Engineering p. 85] Qin S., "Partial Least Squares Regression for Recursive System Identification", In: Proceedings of the 32nd Conference on Decision and Control, San Antonio, TX, USA, 1993, p. 87] Rao, S.R., "Surface Chemistry of Froth Flotation", 2nd edition, Kluwer Academic/Plenum Publishers, New York, 2004.
Appendix: Publications
