Open Access
Add to BibSonomy
Abstract
Currently a significant fraction of the world energy is still produced from the combustion of mineral coal. The extraction of coal from mines is a relatively complex and dangerous activity that still requires the intervention of human miners, and therefore in order to minimize risks, automation of the coal mining process is desirable. An aspect that is still under investigation is potential techniques that can recognize on-line if the mineral being extracted from the mine is coal or if it is the surrounding rock. In this contribution we present the proof of concept of a method that has potential for recognition of the extraction debris from mining based on their terahertz transmission.
© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
1. Introduction
While there is an important effort to transit from fossil fuels to renewable energy sources, the current energetic requirement in several parts of the world will still depend on the use of oil and coal for several years. [1,2] Countries such as China, use considerable amounts of coal in their energy production, and this implies that mining coal is an important activity that still employs many workers that are subject to uncomfortable and risky work conditions. Therefore, there is an interest to automatize, as much as possible all the steps of the coal-mining process in order to minimize the requirement for human intervention, and hence, the risk associated with this activity. [3–5]
One particular aspect that requires appropriate technological solutions, is the recognition of coal from the surrounding rock sections while extracting it from the mines. Various techniques have been proposed in the past for this. For instance, the recognition of the coal-rock interface including methods based on the hardness difference between the coal bed and the rock layer, [6,7] which result in changes in the shearer operation parameters, such as applied voltage, current, motor speed, etc. [8] Additionally, the idea to monitor the temperature of the cutting tool has been explored. [9,10]. Methods based on the acoustic signals produced at the point of contact of the tool with either coal or rocks have been explored, [11] among several other techniques. [12–14] However, these methods are not always suitable for coal mines since the contrast in hardness of some types of rock is not large enough from that of coal for appropriate recognition.
The terahertz band of the electromagnetic spectrum, located between the microwave and the infrared regions, was only accessible to scientists about three decades ago. The introduction of the technique called terahertz time-domain spectroscopy (THz-TDS) developed in the late 1980s opened the possibility of performing spectroscopic measurements in this band of the electromagnetic spectrum [15]. The number of applications that terahertz has found since then is enormous [16], and ranges from study of materials [17–20] to non-destructive testing in biology [21–23], chemistry [24,25], industry [26–28] and art [29–31]. Terahertz technology was first proposed to recognize coal-rock interface by Wang and co-workers [32]. They presented the classification of compressed powder pellets of rock and coal using the absorption or the refractive index spectra from 0.2 THz to 1.6 THz for the classification model, demonstrating the potential of the technique in coal-rock interface recognition. Yet, the complexity and time required to fabricate the compressed tablets cannot meet the needs of on-line coal-rock interface identification, and the cost of a broad-band terahertz time-domain spectrometer is too high to be an appropriate solution with potential of broad real-world application.
In this article we present a method based on terahertz spectroscopy of powders that allows to distinguish several types of coal from various rocks. The method is based on quantifying the losses in the powders caused by scattering, which are related to both the grain size, and the refractive index of the powder, which in turn is a characteristic of each type of mineral.
2. Results
Terahertz waves have frequencies between 100 GHz and 10 THz correspond to wavelengths between 3 mm and 30 $\mu$m. Powders with grain sizes in that range are expected to exhibit significant scattering in this band. When the wavelength and the scattering center dimensions are comparable, the appropriate formalism to treat their interaction is what is called the Mie theory. [33] While we will not go into the details of the derivation and assumptions of this theory, we will explore the possibility of material recognition in powder form using the transmission through the mineral powders.
2.1 Terahertz dielectric properties of bulk minerals
Before we get started with the modeling, we need to know the refractive indices of the bulk materials involved. Three types of coal: Anthracite (CA), Lignite (CL) and Fat coal (CF) as well as five types of rock usually found in coal mines Carbonaceous mudstone (RCM), Mudstone (RM), Conglomerate (RC), Limestone (RL), Siltstone (RS) were chosen. Samples of all of these minerals were obtained from the China Coal Science and Technology Museum and Dr. Weining Xie from the China University of Mining and Technology. The refractive index of all these minerals in “solid” form were measured as described in the Methods section over the band between 75 GHz and 2 THz. As seen in Fig. 1 the refractive indices show very little optical dispersion and the imaginary part of the refractive index is negligible. For the purposes of this initial modelling it is enough to know that all values fall between 1.9 and 2.9.
Fig. 1. Panels a to i show the real (continuous) and imaginary (dashed) parts of the refractive indices for the coal types Anthracite (CA), Lignite (CL) and Fat coal (CF) as well as the rocks Carbonaceous mudstone (RCM), Mudstone (RM), Conglomerate (RC), Limestone (RL), Siltstone (RS) respectively, given that the Conglomerate stone is rather heterogeneous two representative positions are reported, panels f and g, which show important variations across the sample.
2.2 Theory of scattering in powders at THz frequencies
The Mie theory of scattering [34] assumes a spherical particle of radius $a$ and refractive index $n$, and predicts that the extinction cross-section is given by
where $\omega$ is the angular frequency, $c$ is the speed of light,Fig. 2. Transmittance predicted by Mie theory for a 3.6 mm thick layer of particles with diameters in the ranges 57 $\mu$m to 146 $\mu$m (blue), 171 $\mu$m to 397 $\mu$m (green) and 691 $\mu$m to 1204 $\mu$m (red) with refractive indices between 1.9 (lighter colors) and 2.9 (darker colors).
2.3 Recognition of mineral powders
Powders of the three coal and five rock types were prepared as described in the Methods section. Sieves with different hole sizes were used to separate the powders into eleven different ranges, resulting in $(3+5)\times 11=88$ different powder samples. The powders were placed in cuvettes consisting of flat-faced polyethylene windows with a cavity of 3.6 mm separation between walls (see Fig. 3(i)). The transmission of the powders was measured resulting in the plots shown in Fig. 3. We can see that, at least qualitatively, the trend as function of the grain size is consistent with the theoretical scattering calculations presented earlier. Although at first glance the spectra of all the coal and stone samples seem to be almost identical, it is possible to see that subtle changes emerge between them. The maximum amplitudes of the spectra are different for the various minerals, and the drop in transmission at higher frequencies follows a different shape. Additionally, the variation of the curves as function of the grain size is also different, for instance, Anthracite (CA) and Limestone (RL) show greater changes in their spectral behaviour with particle size in comparison to Lignite (CL).
Fig. 3. Panels a-h present the transmittance spectra from powder samples for Anthracite (CA), Lignite (CL) and Fat coal (CF) as well as the rocks Carbonaceous mudstone (RCM), Mudstone (RM), Conglomerate (RC), Limestone (RL), Siltstone (RS) respectively. The different curves in each panel represent the 11 different grain size ranges starting from 13 $\mu$m-43 $\mu$m (darker color) to 691$\mu$m-1204 $\mu$m (lighter color), the 11 size intervals are explicitly displayed in Fig. 4. Panel i presents a schematic representation of the terahertz pulse before ($E_{Ref}$) and after ($E_{Sam}$) propagation through the powder samples in a cuvette.
In order to identify the rock and coal powders based on their terahertz transmission, we propose to construct parameters of the form
where $f_i$ and $f_j$ are frequencies chosen for each grain size, such that the separation of $p$ for the different minerals is maximized. By plotting two parameters for a different choice of $f_i$ we are able to generate a 2-dimensional parametric plot. Such plots are shown in Fig. 4 where the frequencies used are shown in THz on the axes (for visual clarity without units). As seen in the different plots, significant dispersion of the various minerals is seen. Coals are shown in “warm” colors (red-yellow) and with open symbols (*,$\times$,+), while rocks are shown in “cold” colors (green-blue) and with closed symbols ($\square$,$\circ$,$\bigstar$, $\triangledown$, $\vartriangle$) for easy identification. In addition, rectangles are shown around the regions where the points that correspond to coals are provided as a guide-to-the-eye. In some cases, such as the particle range between 101 $\mu$m and 238 $\mu$m, shown in Fig. 4(h), perfect separation of all minerals is possible, indicating that it could be possible to distinguish between these materials by simply using tree monochromatic THz sources and detectors on appropriately chosen grain sizes. Yet, in such a technology additional aspects such as standing waves will have to be taken into account [35].Fig. 4. Panels a-k present the results from the ratio of the experimentally calculated transmittance powder samples at the frequencies indicated in the axes (in THz), yet the unit was omitted for visual reasons. Each panel condenses the results of 9 measurements for each mineral powder with the grain size range indicated. The symbols used are Anthracite (*), Lignite ($\times$) and Fat coal (+) as well as the rocks Carbonaceous mudstone ($\square$), Mudstone ($\circ$), Conglomerate ($\bigstar$), Limestone ($\triangledown$), Siltstone ($\vartriangle$). In order to facilitate the visualization the colours used are consistent with Fig. 3, “warm” colors (red-yellow) are used for coals, while “cold” colors (green-blue) are used for rocks. As a “guide-to-the-eye” we also included rectangles that indicate the regions where the parameters that correspond to the three different coals are found.
3. Discussion and conclusions
We performed a characterization of the terahertz dielectric properties of a series of coals and rocks that are commonly found around them in a mine. Firstly, we found that all minerals have relatively small extinction coefficients (0.01$<\kappa <$0.06) and can, to a certain extent be regarded as reasonably transparent dielectrics with relatively little optical dispersion. In addition we found that rocks have slightly higher (2.01$<n<$2.88) refractive indices than coals (1.94$<n<$2.07). With additional experiments we were able to determine that powders of those minerals have a strong dependence on the grain size as well as moderate dependence on the refractive index of the material. This ties up very well with Mie scattering theory that explains the dependence on both variables. Furthermore, using two parameters based on transmittance at three specific frequencies, it was possible to distinguish between different minerals.
After further development this technique could represent the working principle of an on-line mineral discrimination system if the appropriate machinery to produce powders reliably and swiftly is incorporated. Furthermore, this could be achieved with only three terahertz monochromatic sources and detectors, which are more economical and less sensitive to the operation conditions than a full THz-TDS system. This could be used for feedback of automated mining machinery in order to optimize the coal extraction process without the intervention of personnel in the mines. This technology, when further developed, has the potential to reduce the risks that mining personnel are currently subjected to when operating machinery in the mine, since the inspection of the mineral by a worker present in a mine would be avoided.
4. Methods
4.1 Sample selection and preparation
We selected Anthracite, Fat coal and Lignite as coal samples for this study since they cover broadly different qualities of coal. Five types of rock: mudstone, carbonaceous mudstone, siltstone, conglomerate and limestone, common in coal-bearing formations were also selected for the experiment. Mudstone is a sedimentary rock of complex composition, mainly composed of clay minerals, a hydrous silicate ore containing aluminum and magnesium, followed by clastic minerals, epigenetic minerals, and ferromanganese and organic matter. Carbonaceous mudstone is also a sedimentary rock with an organic carbon content of 6%-40%. Its main component is clay minerals followed by quartz, muscovite and a small amount of feldspar. Limestone is a carbonate rock with calcite as its main component. Siltstone is mainly composed of quartz. Finally, conglomerate is an heterogeneous metamorphic rock.
In order to get the refractive indices of the rock and coal samples selected, flat slabs of each material were made using a power saw. The faces were subsequently polished using sand paper in order to obtain flat and parallel faces for the slabs. The final thickness of each slab was measured by using a digital vernier.
Powders of each mineral were prepared by electric grinder and an agate mortar and pestle. Then the particulates were separated by eleven sieves with different hole sizes. The size graded particles were dried at 120$^\circ$C for $\sim$25 min in a furnace. The particle size of all eleven size intervals were measured by the LEICA microscope. In order to obtain quantify the particle size range. Particles chosen randomly from each mineral and sieve micrograph, such as the ones shown in Fig. 5 were measured. The interval range reported is the resultant of the average plus minus 1 standard deviation of the sizes found for each sieve. As the shape of the particles is irregular, the particle size is determined by the longest line on the two-dimensional image. The resulting particle size intervals are shown explicitly in the different panels of Fig. 4.
Fig. 5. We show micrographs of the powders produced, the bottom of each image shows a ruler with a spacing of 1mm between marks. The samples shown are Siltstone (a) sieve 1 (691 $\mu$m-1204 $\mu$m) and (b) sieve 5 (222 $\mu$m-512 $\mu$m), as well as Fat coal (c) sieve 1 (691 $\mu$m-1204 $\mu$m) and (d) sieve 5 (222 $\mu$m-512 $\mu$m
The powders were placed in a sealed “cuvette” with polyethylene windows, which is highly transparent across the entire terahertz range, separated by a polymethyl methacrylate 3.6 mm-thick spacer with a 1 cm hole which formed the powder cavity.
4.2 Terahertz time-domain spectroscopy
The measurements were made using terahertz time-domain spectrometer based on a 1550 nm $\sim$60 fs Er:fiber mode locked laser. The pulses were split into two. One part was sent to a delay variable line and guided by an optical fiber onto a photoconductive emitter, the other half was guided to a photoconductive detector. The terahertz transients produced in the emitter were collimated and focused onto the samples by high-density polyethylene lenses. Subsequently the terahertz radiation transmitted was also collected and focused onto the detector using high density polyethylene lenses. Further details on the terahertz time-domain technique and setup can be found in [36,37].
The measurements were taken in a nitrogen atmosphere in order to prevent atmospheric water vapour absorption. The refractive indices were obtained according to the procedure described in [38]. The transmittance of the powder samples is given by
where $\tilde {E}_\textrm {sample}$ and $\tilde {E}_\textrm {reference}$ are the Fourier transforms of the waveforms acquired for each sample and a reference recorded in the absence of sample. Each powder sample was measured in three different positions three times at each position, also the three references were acquired in between each position reference in order to account for possible slow drifts of the spectrometer, meaning that a total of 1056 spectra were acquired for the various powder samples, and their respective references.Funding
Alexander von Humboldt-Stiftung; China Scholarship Council (201806420056); National Key Research and Development Program of China (2017YFC080440).
Acknowledgments
The authors would like to thank the financial support of National Key R&D Program of China (grant number 2017YFC080440), the China Scholarship Council (CSC 201806420056) and the Alexander von Humboldt Foundation through an Experienced Research Fellowship. Additionally we want to thank the China Coal Science and Technology Museum and Dr. Weining Xie from China University of Mining and Technology for supplying the rock and coal samples.
Disclosures
The authors declare no conflicts of interest.
References
1. P. E. Brockway, A. Owen, L. I. Brand-Correa, and L. Hardt, “Estimation of global final-stage energy-return-on-investment for fossil fuels with comparison to renewable energy sources,” Nat. Energy 4(7), 612–621 (2019). [CrossRef]
2. C. Zou, Q. Zhao, G. Zhang, and B. Xiong, “Energy revolution: From a fossil energy era to a new energy era,” Nat. Gas Ind. B 3(1), 1–11 (2016). [CrossRef]
3. J.-G. Li and K. Zhan, “Intelligent mining technology for an underground metal mine based on unmanned equipment,” Engineering 4(3), 381–391 (2018). [CrossRef]
4. S. Hao, S. Wang, R. Malekian, B. Zhang, W. Liu, and Z. Li, “A geometry surveying model and instrument of a scraper conveyor in unmanned longwall mining faces,” IEEE Access 5, 4095–4103 (2017). [CrossRef]
5. C. Liu, J. Jiang, Z. Zhou, and S. Ye, “Unmanned working face remote monitoring system based on b/s architecture,” in 2018 5th International Conference on Information Science and Control Engineering (ICISCE), (IEEE, 2018), pp. 597–601.
6. Y. Jiang, Z. Xu, Z. Zhang, and X. Liu, “A novel shearer cutting pattern recognition model with chaotic gravitational search optimization,” Measurement 144, 225–233 (2019). [CrossRef]
7. L. Si, Z. Wang, X. Liu, C. Tan, and L. Zhang, “Cutting state diagnosis for shearer through the vibration of rocker transmission part with an improved probabilistic neural network,” Sensors 16(4), 479 (2016). [CrossRef]
8. Y. Yang, Q. Zeng, G. Yin, and L. Wan, “Vibration test of single coal gangue particle directly impacting the metal plate and the study of coal gangue recognition based on vibration signal and stacking integration,” IEEE Access 7, 106784 (2019). [CrossRef]
9. G. Zhang, Z. Wang, L. Zhao, Y. Qi, and J. Wang, “Coal-rock recognition in top coal caving using bimodal deep learning and hilbert-huang transform,” Shock. Vib. 2017, 1–13 (2017). [CrossRef]
10. L. Si, Z. Wang, Y. Liu, and C. Tan, “Online identification of shearer cutting state using infrared thermal images of cutting unit,” Appl. Sci. 8(10), 1772 (2018). [CrossRef]
11. H. Wang and Q. Zhang, “Dynamic identification of coal-rock interface based on adaptive weight optimization and multi-sensor information fusion,” Inf. Fusion 51, 114–128 (2019). [CrossRef]
12. S. L. Bessinger and M. G. Nelson, “Remnant roof coal thickness measurement with passive gamma ray instruments in coal mines,” IEEE Trans. Ind. Appl. 29(3), 562–565 (1993). [CrossRef]
13. J. Asfahani and M. Borsaru, “Low-activity spectrometric gamma-ray logging technique for delineation of coal/rock interfaces in dry blast holes,” Appl. Radiat. Isot. 65(6), 748–755 (2007). [CrossRef]
14. R. L. Chufo and W. J. Johnson, “A radar coal thickness sensor,” IEEE Trans. Ind. Appl. 29(5), 834–840 (1993). [CrossRef]
15. S. S. Dhillon, M. S. Vitiello, E. H. Linfield, A. G. Davies, M. C. Hoffmann, J. Booske, C. Paoloni, M. Gensch, P. Weightman, G. P. Williams, E. Castro-Camus, D. R. S. Cumming, F. Simoens, I. Escorcia-Carranza, J. Grant, S. Lucyszyn, M. Kuwata-Gonokami, K. Konishi, M. Koch, C. A. Schmuttenmaer, T. L. Cocker, R. Huber, A. G. Markelz, Z. D. Taylor, V. P. Wallace, J. A. Zeitler, J. Sibik, T. M. Korter, B. Ellison, S. Rea, P. Goldsmith, K. B. Cooper, R. Appleby, D. Pardo, P. G. Huggard, V. Krozer, H. Shams, M. Fice, C. Renaud, A. Seeds, A. Stoehr, M. Naftaly, N. Ridler, R. Clarke, J. E. Cunningham, and M. B. Johnston, “The 2017 terahertz science and technology roadmap,” J. Phys. D: Appl. Phys. 50(4), 043001 (2017). [CrossRef]
16. M. C. Beard, G. M. Turner, and C. A. Schmuttenmaer, “Terahertz spectroscopy,” (2002).
17. C. A. Schmuttenmaer, “Exploring dynamics in the far-infrared with terahertz spectroscopy,” Chem. Rev. 104(4), 1759–1780 (2004). [CrossRef]
18. R. Huber, F. Tauser, A. Brodschelm, M. Bichler, G. Abstreiter, and A. Leitenstorfer, “How many-particle interactions develop after ultrafast excitation of an electron–hole plasma,” Nature 414(6861), 286–289 (2001). [CrossRef]
19. N. Vieweg, B. Fischer, M. Reuter, P. Kula, R. Dabrowski, M. Celik, G. Frenking, M. Koch, and P. U. Jepsen, “Ultrabroadband terahertz spectroscopy of a liquid crystal,” Opt. Express 20(27), 28249–28256 (2012). [CrossRef]
20. D. P. McMeekin, G. Sadoughi, W. Rehman, G. E. Eperon, M. Saliba, M. T. Hoerantner, A. Haghighirad, N. Sakai, L. Korte, B. Rech, M. B. Johnston, L. M. Herz, and H. J. Snaith, “A mixed-cation lead mixed-halide perovskite absorber for tandem solar cells,” Science 351(6269), 151–155 (2016). [CrossRef]
21. A. K. Singh, A. Perez-Lopez, J. Simpson, and E. Castro-Camus, “Three-dimensional water mapping of succulent Agave victoriae-reginae leaves by terahertz imaging,” Sci. Rep. 10(1), 1404 (2020). [CrossRef]
22. N. Born, D. Behringer, S. Liepelt, S. Beyer, M. Schwerdtfeger, B. Ziegenhagen, and M. Koch, “Monitoring plant drought stress response using terahertz time-domain spectroscopy,” Plant Physiol. 164(4), 1571–1577 (2014). [CrossRef]
23. R. M. Woodward, B. E. Cole, V. P. Wallace, R. J. Pye, D. D. Arnone, E. H. Linfield, and M. Pepper, “Terahertz pulse imaging in reflection geometry of human skin cancer and skin tissue,” Phys. Med. Biol. 47(21), 3853–3863 (2002). [CrossRef]
24. R. J. Falconer and A. G. Markelz, “Terahertz spectroscopic analysis of peptides and proteins,” J. Infrared, Millimeter, Terahertz Waves 33(10), 973–988 (2012). [CrossRef]
25. J. A. Morales-Hernández, A. K. Singh, S. J. Villanueva-Rodriguez, and E. Castro-Camus, “Hydration shells of carbohydrate polymers studied by calorimetry and terahertz spectroscopy,” Food Chem. 291, 94–100 (2019). [CrossRef]
26. K. Su, Y.-C. Shen, and J. A. Zeitler, “Terahertz sensor for non-contact thickness and quality measurement of automobile paints of varying complexity,” IEEE Trans. Terahertz Sci. Technol. 4(4), 432–439 (2014). [CrossRef]
27. K. Kawase, “Terahertz imaging for drug detection and large-scale integrated circuit inspection,” Opt. photonics news 15(10), 34–39 (2004). [CrossRef]
28. Y.-C. Shen and P. F. Taday, “Development and application of terahertz pulsed imaging for nondestructive inspection of pharmaceutical tablet,” IEEE J. Sel. Top. Quantum Electron. 14(2), 407–415 (2008). [CrossRef]
29. C. L. K. Dandolo and P. U. Jepsen, “Wall painting investigation by means of non-invasive terahertz time-domain imaging (thz-tdi): Inspection of subsurface structures buried in historical plasters,” J. Infrared, Millimeter, Terahertz Waves 37(2), 198–208 (2016). [CrossRef]
30. F. Lambert, E. Reyes-Reyes, G. Hernandez-Cardoso, A. Gomez-Sepulveda, and E. Castro-Camus, “In situ determination of the state of conservation of paint coatings on the kiosk of guadalajara using terahertz time-domain spectroscopy,” J. Infrared, Millimeter, Terahertz Waves 41(4), 355–364 (2020). [CrossRef]
31. K. Krügener, J. Ornik, L. M. Schneider, A. Jäckel, C. L. Koch-Dandolo, E. Castro-Camus, N. Riedl-Siedow, M. Koch, and W. Viöl, “Terahertz inspection of buildings and architectural art,” Appl. Sci. 10(15), 5166 (2020). [CrossRef]
32. X. Wang, K.-X. Hu, L. Zhang, X. Yu, and E.-J. Ding, “Characterization and classification of coals and rocks using terahertz time-domain spectroscopy,” J. Infrared, Millimeter, Terahertz Waves 38(2), 248–260 (2017). [CrossRef]
33. G. Mie, “Beiträge zur optik trüber medien, speziell kolloidaler metallösungen,” Ann. Phys. 330(3), 377–445 (1908). [CrossRef]
34. J. R. Frisvad, N. J. Christensen, and H. W. Jensen, “Computing the scattering properties of participating media using lorenz-mie theory,” in ACM SIGGRAPH 2007 papers, (2007), pp. 60–1–60–10.
35. R. Wilk, F. Breitfeld, M. Mikulics, and M. Koch, “Continuous wave terahertz spectrometer as a noncontact thickness measuring device,” Appl. Opt. 47(16), 3023–3026 (2008). [CrossRef]
36. J. Neu and C. A. Schmuttenmaer, “Tutorial: An introduction to terahertz time domain spectroscopy (thz-tds),” J. Appl. Phys. 124(23), 231101 (2018). [CrossRef]
37. N. Vieweg, F. Rettich, A. Deninger, H. Roehle, R. Dietz, T. Göbel, and M. Schell, “Terahertz-time domain spectrometer with 90 db peak dynamic range,” J. Infrared, Millimeter, Terahertz Waves 35(10), 823–832 (2014). [CrossRef]
38. W. Withayachumnankul and M. Naftaly, “Fundamentals of measurement in terahertz time-domain spectroscopy,” J. Infrared, Millimeter, Terahertz Waves 35(8), 610–637 (2014). [CrossRef]
