In mining, access to accurate quantitative mineralogical data has significant implications for the production process efficiency. Quantitative mineralogical data, available in real-time, would greatly assist material characterization (e.g., ore versus waste), operational decision making, optimization of ore processing, and the specification of product quality. Understanding material composition in mineral processing can also minimise the technical and financial risks. It may thereby greatly enhance economic, safety, and environmental performance of a mining operation.
Geometallurgical investigation links the geological and mineralogical characteristics to the metallurgical performance of an orebody. It is an important approach to optimize resource efficiency and reduce the technical risk associated with mining operations. The required information for geometallurgical applications is not limited to knowledge on the grades of valuable elements and their variability, but also extends to the gangue minerals, as their composition and volume also play a crucial role in ore processing. Extant studies highlight the importance of mineralogical information for the sustainability and energy efficiency of geometallurgical processes [1
]. Ore minerals occur in veins, disseminated in the host rock and/or in pores with varying concentrations of other associated minerals such as silica, oxides and carbonates. The concentration of these minerals can be associated with the metallurgical behaviour of the ore minerals. Therefore, quantitative mineralogical information on the co-occurring minerals is one of the crucial parameters for the optimisation of ore processing.
Despite rapid advances in sensor technologies, there is still a demand for novel ideas to enable quantitative investigations of mineralogical compositions using sensor-derived data. In addition, in-situ application of sensor technologies requires portable and high-speed systems. Portable sensor technologies (such as X-ray fluorescence—XRF, and short-wave infrared—SWIR) that provide geochemical or mineralogical data are available. However, most of the currently available sensor technologies are laboratory-based techniques. Owing to the growing interest in an accurate, in-situ and on-line quantitative analysis of minerals, infrared technologies coupled with advanced data analytics can be promising alternative tools. Despite rapid advances in sensor technologies, there is still a demand for novel ideas to enable quantitative investigations of mineralogical compositions using sensor-derived data. In addition, most of the currently available sensor technologies are laboratory-based techniques. Owing to the growing interest in an accurate, in-situ and on-line quantitative analysis of minerals, infrared technologies coupled with advanced data analytics can be promising alternative tools. Infrared (IR) spectroscopy is a well-established analytical technique that can be applied in qualitative and quantitative analysis of organic and inorganic materials [3
]. State-of-the-art infrared technologies are fast, portable, non-destructive, and can operate over a wide electromagnetic spectral range [6
]. The IR region of the electromagnetic spectrum extends from λ = 0.7 to 1000 µm and is subdivided into different regions [5
]. For example, it can be divided into the near infrared (NIR: 0.7–1.4 μm), SWIR: 1.4–2.5 μm and far infrared (FIR: 15–1000 μm) regions. The mid-wave infrared (MWIR) and long-wave infrared (LWIR) are the subsets that correspond to the wavelength ranges of 2.5−7 µm and 7−15 µm, respectively [9
]. Spectral signals in MWIR and LWIR regions are produced as a consequence of molecular vibrations of the functional groups that can be related to mineralogy [11
Numerous previous studies indicate that IR technologies can be utilised for the accurate identification of minerals. Such applications are usually qualitative. For example, near-infrared (NIR) sensors can provide accurate identification of clay minerals and rock-forming minerals [13
], whereas short-wave infrared (SWIR) is one of the most widely used infrared technologies for the identification of alteration minerals [15
]. On the other hand, LWIR permits identification of rock-forming minerals, whereas far-infrared (FIR) can be used for the identification of rare earth minerals [11
]. Characteristic features of the minerals have also been utilised to quantitatively relate variations in mineral concentrations. For example, Hecker et al. [18
] estimated concentrations of rock-forming minerals using LWIR. Similarly, Mroczkowska-Szerszeń and Orzechowski [19
] used ATR-FTIR (attenuated total reflectance Fourier transform infrared) for semi-quantitative analysis of minerals in carbonate rocks. Palayangoda and Nguyen [20
], on the other hand, estimated mineral concentrations in oil shale using ATR-FTIR spectra combined with Principal Component Regression (PCR) method. In another study, Guatame-Garcia and Buxton [21
] assessed the use of infrared spectroscopy for predicting the soluble Al2
content in calcined kaolin. Although few researchers indicated the potential for using IR technologies in quantitative analysis of minerals, some authors also discussed the limitations of this approach. Specifically, Kaufhold et al. [22
] assessed the possibility of the use of infrared spectra for quantitative analysis of clay minerals and pointed out the mineral-specific challenges owing to instrument detection limit, availability of suitable reference and particle size. At present, IR techniques are insufficiently used in quantitative analysis of minerals. Moreover, most of the existing studies in this field addressed the challenge for the development of reliable calibration models to predict mineral concentrations in complex mixtures. Consequently, there is a need for advanced data-driven approaches and spectral signal pre-processing techniques that can be incorporated into comprehensive calibration models, thus to achieve accurate estimation of mineral concentrations in different commodities.
Data fusion is the term applied to the integrated analysis of multiple data blocks from different data sources, such that they can interact and inform each other [23
]. Fusing of different data sources enhances the reliability of prediction or classification models owing to the synergy among the incorporated datasets. Data fusion can be implemented in different ways and at different levels using various multivariate linear (e.g., partial-least squares regression—PLSR) and non-linear (e.g., support vector machine—SVM) data analysis techniques [23
]. Data fusion can be realised at low-, mid-, and high-level. In low-level fusion, data from the different sources are pre-processed and concatenated to form a fused data block [23
]. Thus, it is commonly referred to as data-level fusion. The mid-level fusion requires two modelling steps. First, the informative features (relevant information) of the different data blocks are separately extracted using suitable variable screening or selection methods [23
]. In the second step, the extracted features are concatenated into a single matrix and are used to develop models based on multivariate analysis techniques. In mid-level fusion, feature extraction can be accomplished using different strategies, such as data decomposition (Multivariate curve resolution—MCR) and feature selection (Principal Component Analysis—PCA) methods. Mid-level fusion is therefore also called features-level fusion. High-level fusion is a decision-level fusion, as the outputs (predicted value) of the prediction or classification models developed for each data block are fused (e.g., by averaging). Data fusion approaches are now widely used in several disciplines, such as robotics [27
], image processing [28
], food analysis [24
], and pharmacological studies [26
]. Findings yielded by pertinent studies indicate that data fusion approaches can be highly beneficial for mineralogical applications [31
]. However, at present, the application of data fusion for mineralogical investigations remains very limited.
The MWIR and LWIR provide spectral signals that can be used to identify various minerals. The LWIR permits identification of the rock-forming minerals. Whereas, the MWIR is the least-explored region of the electromagnetic spectrum, however, it has a great potential for material characterization. Therefore, combinations of the two regions can potentially result in a comprehensive and enhanced characterization of minerals. To date, quantitative analysis of minerals in polymetallic sulphide ore samples using MWIR and LWIR spectra combined with data fusion methods has never been conducted. This gap in the current analytical methodology and the promising findings [33
] reported recently have motived the present study. Its main aims are thus (1) to investigate the use of diffuse reflectance infrared (MWIR and LWIR) spectra for quantitative analysis of mineral mixtures in polymetallic sulphide ore samples, and (2) to evaluate the data fusion methods using linear (PLSR and PCR) and non-linear (SVR) multivariate regression techniques. The implemented low-level data fusion approaches are data fusion without feature selection (fusion of the entire variables in the MWIR and LWIR data blocks) and with feature selection (fusion of the extracted features of the two data blocks).
In this work, different scenarios were investigated to assess their influence on the prediction of SiO2, Al2O3 and Fe2O3 concentrations in polymetallic sulphide ore samples using infrared reflectance spectra, namely:
the use of individual spectral regions (MWIR and LWIR);
the effect of different data pre-processing techniques on the prediction performance;
potential for improvement in prediction accuracy by applying low-level and low-level with feature selection data fusion approaches;
comparative benefits of applying linear (PLSR and PCR) and non-linear (SVR) multivariate analysis techniques.
The results reported in the preceding sections show that both MWIR and LWIR datasets include relevant information that can be employed in determining mineral concentrations. Moreover, data fusion significantly improved model prediction accuracy. Models incorporating both the linear and non-linear multivariate techniques (PLSR, PCR, and SVR) resulted in comparable performance. The choice of the data pre-processing techniques was shown to exert significant influence on the model output. For the prediction of Al2O3, the best-performing model was achieved using PLSR and the low-level fusion of the extracted features after data normalisation (RMSEP = 1.4, R2 = 0.91). The PLSR model better predicted Fe2O3 in polymetallic sulphide ore after low-level fusion of normalised MWIR and LWIR data blocks (RMSEP = 3.3, R2 = 0.94). Finally, the best prediction of SiO2 concertation was achieved by the PLSR model after normalised data blocks were subjected to low-level fusion (RMSEP = 5.96, R2 = 0.93). Overall, both the linear and non-linear techniques provided good and acceptable results. Although the acquired prediction accuracies are lower than those of the standard laboratory-based techniques, the proposed method is suitable for rapid in-situ indication (semi-quantification) of mineralogical concentrations along the mining value chain.
The fact that the use of the extracted features significantly reduced the data volume and resulted in promising results suggests a great potential of applying data fusion to data obtained from multiple sources. Our future work will focus on extending the data fusion framework for integration of additional data sources (e.g., SWIR and Raman) to achieve a holistic description and improved quantification of minerals in different deposit types using the synergy among the different data sources. This will be beneficial for improving resource efficiency in the mining industry.