E NSAMBLAJE Y AJUSTE FINALES

Several individual contributions to spiral monitoring, modelling and plant instrumentation design are proposed in this project. However, the main contribution is the framework that encompasses the steps from sensor development to sensor implementation. Such a framework is useful to determine future implementation of a proposed sensor. Preliminary suggestions are also provided which indicate the importance of monitoring and possible control of certain positions of units within a spiral plant. Figure 6.1 summarises the main contributions from the most important project steps.

Figure 6.1: Summary of project steps and contribution(s) (green: research outputs; white: articles)

Interface detection results showed that machine learning provides an avenue for interface tracking in industrial mineral slurries (which, once solved, suggests the next step of possible future sensor implementation). Appropriate spiral models and plant simulation were required to investigate optimal sensor placement approaches. An extended version of the Holland-Batt model was proposed and compared against response surface methodology (RSM) modelling. The new Holland-Batt model was developed with spiral plant modelling kept in mind and it was found that it performed better on experimental confirmation results and also suited plant simulation better. Future spiral modelling methodologies should keep in mind plant simulation because most spiral modelling attempts, to date (see Section 3.1), stop at modelling or optimization of single spirals and do not show whether these models can lead to high fidelity plant simulations.

Finally, a metallurgical performance sensor placement approach (Algorithm SPII) was proposed and compared to the typical state estimation approach applied in literature (Algorithm SPI). Algorithm

102 SPII essentially constituted a sensitivity analysis of spiral plant optimization given different splitter control variations (varying feed conditions are not considered at length in optimal sensor placement literature). Results produced by Algorithm SPII include contributions of the different interface monitoring/splitter control configurations to plant production performance. This essentially allows classification of different spiral banks by how important their splitter settings are to the control of throughput and product grade/quality (instead of monitoring performance improvements which have little relevance at current spiral plants – see Section 1.2). Lastly, SPII compares sensor cost and revenue improvement directly (over a certain production time) leading to an overall design objective similar to the concept of return on investment. This is a new perspective on the multi-objective problem for sensor placement since previous methods relied on comparing dissimilar objectives via pareto fronts.

6.2. Interface detection

6.2.1. Conclusions

Two interface detection algorithms were formulated to solve the mineral interface (which form in spiral concentrators) detection problem. Algorithm CVI was based on existing image processing methods with parameter optimisation performed using genetic algorithms (GA). The second method, Algorithm CVII, was based on pixel classification using logistic regression. Both methods were applied to two data sets - derived from two different case studies: ilmenite sands and chromite separation. The chromite separation data set was representative of industrial spiral slurry conditions. The ilmenite image set proved to be the simpler application of Algorithms CVI and CVII, and both methods can detect the desired interfaces. Similar mineral interface detection results were achieved using Algorithms CVI and CVII. However, Algorithm CVII proved to be the more reliable method for concentrate interface detection since Algorithm CVI can generate additional spurious edge detections. Algorithm CVII always produces a single response per interface.

Interface detection with the chromite image set was more challenging. Algorithm CVI was unable to provide a concentrate interface response in more than 90 % of the testing images (for parameters obtained from each training case). Training of Algorithm CVI for concentrate-gulley interface detection was suspended due to the poor concentrate interface detection results. Algorithm CVII can detect concentrate interfaces in more than 50 % of the images (for parameters obtained from each training case). However, Algorithm CVII cannot provide a majority of true positive concentrate interface detections in more than 65 % of the testing images (even when using the parameter set that provides the best interface detection results).

Training of Algorithm CVI is in the order of hours even when smaller chromosome populations of 50 are used. Algorithm CVII’s training time is in the order of minutes and will only exceed training time of 1 h with larger training sets and greater iteration limits. Overall, it can be concluded that Algorithm

103 CVII is the superior method to detect mineral interfaces in spiral troughs. One of the major drawbacks of both algorithms is the amount of training data that must be generated manually to achieve useful interface detection results. Lastly, Algorithm CVII showed that interface detection on industrial slurries is possible and more complex machine learning methods may improve on the results found on this case study.

6.2.2. Recommendations and future work

Algorithm CVII uses logistic regression which is merely one of many statistical classifiers. Other statistical learning algorithms such as multi-layer perceptron (or neural) networks, naïve Bayesian classifiers or support vector machines could be applied to the existing image sets to determine whether the interface detection results can be improved. More labelled images should be prepared for the training of more complex classifiers – especially neural networks.

Another consideration for future study is to explore semi-supervised and unsupervised methods to segment mineral interfaces. Segmentation methods that require less user input (in this study’s case less labelled images) can be developed quicker with lower chance of user labelling errors. Unsupervised segmentation methods will require more investigation into the features that must be calculated from each image to properly cluster regions of the images. Even when appropriate clustering is achieved a general methodology will have to be developed to determine which clusters belong to which part of a spiral trough or slurry.

Finally, Algorithm CVII (or improvement of the algorithms) must be applied to images obtained from industry. Industrial spirals will not always show clear mineral interfaces when spirals are not cleaned (e.g. due to lack of maintenance), feed slurries are not screened or when slimes are present. It should also be determined whether the laboratory developed Algorithms CVI or CVII’s parameters can detect interfaces in industry. Another interesting problem includes testing whether optimal parameters from one spiral concentration application works for a different application (i.e. testing whether Algorithm CVII can detect ilmenite sands mineral interfaces when trained on the chromite images).