SpringerLink
Log in

Study on Hyperspectral Remote Sensing Based Rapid Determination of Coal Quality Parameters

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

In this research work, reflectance spectroscopy of Jharia and Raniganj coal samples across the Vis–NIR–SWIR range (wavelength: 350–2500 nm) were investigated in conjugation with coal properties to construct a spectral-compositional relationship. Several pre-processing approaches were applied to each spectrum for measuring the band depth of absorption features. Reflectance spectra of coal in the 350–2500 nm region demonstrate a number of absorption characteristics attributable to organic and inorganic compounds. In this spectral-proximate relationship study, coal volatile matter and ash content are captured with correlation coefficient (r) of 0.85 and 0.84 at band depth of 2300 nm and 2200 nm. Whereas for gross calorific value, moisture, fixed carbon, and fuel ratio show correlation coefficient values (r) of 0.82, 0.79, 0.79, and 0.79 at band depth of 2300 nm, 1900 nm, and 2200 nm, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

References

  • ASTM D5865–10 (2010) Standard test method for gross calorific value of coal and coke. ASTM International.

  • ASTM D3172–13 (2013) Standard test methods for proximate analysis of coal and coke. ASTM International.

  • Adams, J. B. (1974). Visible and near-infrared diffuse reflectance spectra of pyroxenes as applied to remote sensing of solid objects in the solar system. Journal of Geophysical Research, 79, 4829–4836.

    Article  CAS  Google Scholar 

  • Alciaturi, C. E., Escobar, M. E., & Vallejo, R. (1996). Prediction of coal properties by derivative DRIFT spectroscopy. Fuel, 75, 491–499.

    Article  CAS  Google Scholar 

  • Andrés, J. M., & Bona, M. T. (2006). ASTM clustering for improving coal analysis by near-infrared spectroscopy. Talanta, 70, 711–719.

    Article  Google Scholar 

  • Asadzadeh, S., Roberto, C., & Filho, D. S. (2016). A review on spectral processing methods for geological remote sensing. International Journal of Applied Earth Observation and Geoinformation, 47, 69–90.

    Article  Google Scholar 

  • BP (2020) Statistical Review of World Energy globally consistent data on world energy markets.

  • Bartholomeus, H. M., Schaepman, M. E., Kooistra, L., Stevens, A., Hoogmoed, W. B., & Spaargaren, O. S. P. (2008). Spectral reflectance based indices for soil organic carbon quantification. Geoderma, 145, 28–36.

    Article  CAS  Google Scholar 

  • Bhosle, K., & Musande, V. (2019). Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images. Journal of the Indian Society of Remote Sensing, 47, 1949–1958.

    Article  Google Scholar 

  • Bishop, C. A., Liu, J. G., & Mason, P. J. (2011). Hyperspectral remote sensing for mineral exploration in Pulang, Yunnan Province China. International Journal of Remote Sensing, 32, 2409–2426.

    Article  Google Scholar 

  • Bona, M. T., & Andrés, J. M. (2008). Reflection and transmission mid-infrared spectroscopy for rapid determination of coal properties by multivariate analysis. Talanta, 74, 998–1007.

    Article  CAS  Google Scholar 

  • Callejón-Ferre, A. J., Velázquez-Martí, B., López-Martínez, J. A., & Manzano-Agugliaro, F. (2011). Greenhouse crop residues: Energy potential and models for the prediction of their higher heating value. Renewable and Sustainable Energy Reviews, 15, 948–955.

    Article  Google Scholar 

  • Cannon, C. G., & Sutherland, G. B. B. M. (1945). The infra-red absorption spectra of coals and coal extracts. Transactions of the Faraday Society, 41, 279.

    Article  CAS  Google Scholar 

  • Carr, A. D., & Williamson, J. E. (1990). The relationship between aromaticity, vitrinite reflectance and maceral composition of coals: Implications for the use of vitrinite reflectance as a maturation parameter. Organic Geochemistry, 16, 313–323.

    Article  CAS  Google Scholar 

  • Chen, C., Gao, J., & Yan, Y. (1998). Observation of the type of hydrogen bonds in coal by FTIR. Energy & Fuels., 12, 446–449.

    Article  CAS  Google Scholar 

  • Christy, A. A., Dahl, B., & Kvalheim, O. M. (1989). Structural features of resins, asphaltenes and kerogen studied by diffuse reflectance infrared spectroscopy. Fuel, 68, 430–435.

    Article  CAS  Google Scholar 

  • Clark, R. N., King, T. V. V., Klejwa, M., Swayze, G. A., & Vergo, N. (1990). High spectral resolution reflectance spectroscopy of minerals. Journal of Geophysical Research, 95, 12653.

    Article  Google Scholar 

  • Cloutis, E. A. (1989). Spectral reflectance properties of hydrocarbons: Remote-sensing implications. Science, 245, 165–168.

    Article  CAS  Google Scholar 

  • Cloutis, E. A. (1996). Review article hyperspectral geological remote sensing: Evaluation of analytical techniques. International Journal of Remote Sensing, 17, 2215–2242.

    Article  Google Scholar 

  • Cloutis, E. A. (2003). Quantitative characterization of coal properties using bidirectional diffuse reflectance spectroscopy. Fuel, 82, 2239–2254.

    Article  CAS  Google Scholar 

  • Cloutis, E. A., Gaffey, M. J., & Moslow, T. F. (1994). Spectral reflectance properties of carbon-bearing materials. Icarus, 107, 276–287.

    Article  CAS  Google Scholar 

  • Crowley, J. K. (1986). Visible and near-infrared spectra of carbonate rocks: Reflectance variations related to petrographic texture and impurities. Journal of Geophysical Research, 91, 5001.

    Article  Google Scholar 

  • Fuller, M. P., Hamadeh, I. M., Griffiths, P. R., & Lowenhaupt, D. E. (1982). Diffuse reflectance infrared spectrometry of powdered coals. Fuel, 61, 529–536.

    Article  CAS  Google Scholar 

  • Fuller, E. L., & Smyrl, N. R. (1990). Chemistry and structure of coals: Hydrogen bonding structures evaluated by diffuse reflectance infrared spectroscopy. Applied Spectroscopy, 44, 451–461.

    Article  CAS  Google Scholar 

  • Furimsky, E., Palmer, A. D., Kalkreuth, W. D., Cameron, A. R., & Kovacik, G. (1990). Prediction of coal reactivity during combustion and gasification by using petrographic data. Fuel Processing Technology, 25, 135–151.

    Article  CAS  Google Scholar 

  • Gaffey, S. J. (1985). Reflectance spectroscopy in the visible and near-infrared (0.35–2.55 mu m): Applications in carbonate petrology. Geology, 13, 270–273.

    Article  CAS  Google Scholar 

  • Gautam, S., Yadav, R. S., & Suresh, N. (2015). Evaluation of the characteristics of as-received and washed low grade Indian coals for their industrial applications. Energy Sources, Part A Recover Util Environ Eff., 37, 2138–2149.

    CAS  Google Scholar 

  • Grove, C.I., Hook, S.J., & Paylor III, E. (1992). JPL-PUB-92-2.pdf. Jet Propulsion Laboratory.

  • Hunt, G. R. (1970). Visible and near-infrared spectra of minerals and rocks: I silicate minerals. Modern Geology, 1, 283–300.

    CAS  Google Scholar 

  • Hunt, G. R. (1977). Spectral signatures of particulate minerals in the visible and near infrared. Geophysics, 42, 501–513.

    Article  CAS  Google Scholar 

  • Hunt, G. R. (1979). Near‐infrared (1.3–2.4) μm spectra of alteration minerals—Potential for use in remote sensing. Geophysics, 44, 1974–1986.

    Article  CAS  Google Scholar 

  • Ibarra, J., Cervero, I., Miranda, J., & Moliner, R. (1991). Structural rank parameters of Spanish low rank coals and their influence on pyrolysis yields. Fuel Processing Technology, 28, 259–273.

    Article  CAS  Google Scholar 

  • Ito, O., Seki, H., & Iino, M. (1988). Diffuse reflectance spectra in near-i.r. region of coals; a new index for degrees of coalification and carbonization. Fuel, 67, 573–578.

    Article  CAS  Google Scholar 

  • Jiang, X., Tang, L., Wang, C., & Wang, C. (2004). Spectral characteristics and feature selection of hyperspectral remote sensing data. International Journal of Remote Sensing, 25, 51–59.

    Article  Google Scholar 

  • Kim, D. W., Lee, J. M., & Kim, J. S. (2009). Application of near infrared diffuse reflectance spectroscopy for on-line measurement of coal properties. Korean Journal of Chemical Engineering, 26, 489–495.

    Article  CAS  Google Scholar 

  • Kokaly, R. F. (2001). Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sensing of Environment, 75, 153–161.

    Article  Google Scholar 

  • Liu, Y., Dang, B., Li, Y., Lin, H., & Ma, H. (2016). Applications of Savitzky–Golay filter for seismic random noise reduction. Acta Geophysica, 64, 101–124.

    Article  Google Scholar 

  • Longhi, I., Sgavetti, M., Chiari, R., & Mazzoli, C. (2001). Spectral analysis and classification of metamorphic rocks from laboratory reflectance spectra in the 0.4-2.5 μ m interval: A tool for hyperspectral data interpretation. International Journal of Remote Sensing, 22, 3763–3782.

    Article  Google Scholar 

  • Mao, Y., Ma, B., Liu, S., Wu, L., Zhang, X., & Yu, M. (2014). Study and validation of a remote sensing model for coal extraction based on reflectance spectrum features. Canadian Journal of Remote Sensing, 40, 327–335.

    Article  Google Scholar 

  • Mastalerz, M. (1995). Application of reflectance micro-Fourier transform infrared spectrometry in studying coal macerals: Comparison with other Fourier transform infrared techniques. Fuel, 74, 536–542.

    Article  CAS  Google Scholar 

  • Mazumder, B. (Ed.). (2012). Coal science and engineering. Woodhead Publishing Limited.

    Google Scholar 

  • Mondal, C., Pandey, A., Pal, S. K., Samanta, B., & Dutta, D. (2021). Prediction of gross calorific value as a function of proximate parameters for Jharia and Raniganj coal using machine learning based regression methods. International Journal of Coal Preparation and Utilization, 00, 1–14.

    Google Scholar 

  • Mondal, C., Pandey, A., Kumar Pal, S., Samanta, B. & Dutta, D. (2023). Hyperspectral measurement technique based rapid determination of coal quality parameters of Jharia and Raniganj basin coal. Infrared Physics and Technology 128, 104504. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1350449522004856

  • Mutanga, O., & Skidmore, A.K. (2003) Continuum-removed absorption features estimate tropical savanna grass quality in situ. 3rd EARSEL Work Imaging Spectrosc Herrsching, 13–16 May 2003., pp. 13–6.

  • NITI Aayog and IEEJ. (2018). Energizing India. A joint project Report of NITI Aayog and IEEJ [Internet]. 2018. Available from: https://smartnet.niua.org/sites/default/files/resources/energy_booklet.pdf

  • Ravi, V, & Reddy, P.J. (1999) Ranking of Indian coals via fuzzy multi attribute decision making. Fuzzy Sets and Systems 103, 369–77. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0165011497003941

  • Román Gómez, Y., Cabanzo Hernández, R., Guerrero, J. E., & Mejía-Ospino, E. (2018). FTIR-PAS coupled to partial least squares for prediction of ash content, volatile matter, fixed carbon and calorific value of coal. Fuel, 226, 536–544.

    Article  Google Scholar 

  • SVC. (2019). Field Spectroscopy Guide with SVC i-series Spectroradiometers [Internet]. Available from: https://spectravista.com/wp-content/uploads/2021/05/SVC-Field-Spectroscopy-Guide-Rev-1-2019-10-22.pdf

  • Sarangi, R. K., Singh, S., Dwidevi, R. M., & Matondkar, S. G. P. (2008). Hyperspectral radiometric observation of the northeast Arabians Sea during April 2006. J Indian Soc Remote Sens., 36, 13–25.

    Article  Google Scholar 

  • Schmidt, K. S., & Skidmore, A. K. (2003). Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment, 85, 92–108.

    Article  Google Scholar 

  • Sobkowiak, M., & Painter, P. (1995). A Comparison of DRIFT and KBr Pellet methodologies for the quantitative analysis of functional groups in coal by infrared spectroscopy. Energy & Fuels., 9, 359–363.

    Article  CAS  Google Scholar 

  • Song, Z., & Kuenzer, C. (2017). Spectral reflectance (400–2500 nm) properties of coals, adjacent sediments, metamorphic and pyrometamorphic rocks in coal-fire areas: A case study of Wuda coalfield and its surrounding areas, northern China. International Journal of Coal Geology, 171, 142–152.

    Article  CAS  Google Scholar 

  • Sridharan, S.J., Pandey, A. & Sastry, B.S. (2019). Current developments in mine air cooling systems: Case study of an Indian coal mine using different cooling strategies. Recent Advances Mining Technology [Internet]. Acharya Institute of Technology, Bengaluru; 2019. Available from: https://www.researchgate.net/publication/337621783_Current_Developments_in_Mine_Air_Cooling_Systems_Case_Study_of_an_Indian_Coal_Mine_Using_Different_Cooling_Strategies

  • Stach, E., Mackowsky, M. T., Teichmueller, M., Taylor, G. H., Chandra, D., & Teichmueller, R. (1982). Stach’s textbook of coal petrology (3rd ed.). Schweizerbart Science Publishers.

    Google Scholar 

  • Tan, P., Zhang, C., **a, J., Fang, Q.-Y., Chen, G. (2015). Estimation of higher heating value of coal based on proximate analysis using support vector regression. Fuel Processing Technology 138, 298–304. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0378382015300394

  • Thangavelu, M., Shanmugam, S., & Bhattacharya, A. K. (2011). Hyperspectral radiometry to quantify the grades of Iron Ores of Noamundi and Joda Mines, Eastern India. Journal of the Indian Society of Remote Sensing, 39, 473–483.

    Article  Google Scholar 

  • Vargas-Moreno, J. M., Callejón-Ferre, A. J., Pérez-Alonso, J., & Velázquez-Martí, B. (2012). A review of the mathematical models for predicting the heating value of biomass materials. Renewable and Sustainable Energy Reviews, 16, 3065–3083.

    Article  CAS  Google Scholar 

  • Wang, Y., Yang, M., Wei, G., Hu, R., Luo, Z., & Li, G. (2014). Improved PLS regression based on SVM classification for rapid analysis of coal properties by near-infrared reflectance spectroscopy. Sensors Actuators B Chem., 193, 723–729.

    Article  CAS  Google Scholar 

  • Yang X, Lei S, Zhao Y, Cheng W. (2020). Use of hyperspectral imagery to detect affected vegetation and heavy metal polluted areas: a coal mining area, China. Geocarto International 2020;0:1–20.

  • Yerel, S., & Ersen, T. (2013). Prediction of the calorific value of coal deposit using linear regression analysis. Energy Sources, Part a: Recovery, Utilization, and Environmental Effects, 35, 976–980.

    Article  CAS  Google Scholar 

  • Yin, C.-Y. (2011). Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel, 90, 1128–1132.

    Article  CAS  Google Scholar 

  • Yünsel, T. Y. (2019). In-situ coal quality variability analysis by combining Gaussian co-simulation and a JavaScript. Energy Sources, Part a: Recovery, Utilization, and Environmental Effects, 41, 2631–2649.

    Article  Google Scholar 

Download references

Acknowledgements

Chinmay Mondal would like to thank Regional remote Sensing Centre-East, NRSC (ISRO) in carrying out the spectrometer readings. The authors would also like to thank IIT Kharagpur for supporting this research work.

Funding

No particular grants from funding agencies in the public, commercial or non-profit sectors were obtained for this study.

Author information

Authors and Affiliations

  1. Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur, WB, 721302, India

    Chinmay Mondal, Samir Kumar Pal & Biswajit Samanta

  2. Department of Mining Engineering, Birsa Institute of Technology, Sindri, JH, 828123, India

    Aditya Pandey

  3. Regional Remote Sensing Centre-East, NRSC (Indian Space Research Organisation), Kolkata, WB, 700163, India

    Dibyendu Dutta

Authors
  1. Chinmay Mondal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aditya Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samir Kumar Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Biswajit Samanta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dibyendu Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chinmay Mondal.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mondal, C., Pandey, A., Pal, S.K. et al. Study on Hyperspectral Remote Sensing Based Rapid Determination of Coal Quality Parameters. J Indian Soc Remote Sens (2024). https://doi.org/10.1007/s12524-024-01893-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12524-024-01893-3

Keywords

Search

Navigation