238
Views
8
CrossRef citations to date
0
Altmetric
Research Article

Biomass fuel identification using flame spectroscopy and tree model algorithms

, , , &
Pages 1055-1072 | Received 18 Jul 2019, Accepted 10 Oct 2019, Published online: 22 Oct 2019

References

  • Breiman, L. 2001. Random forests. Mach. Learn. 45:5–32. doi:10.1023/A:1010933404324.
  • Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and regression trees. California: Wadsworth Publishing Company.
  • Chi, T., H. Zhang, Y. Yan, H. Zhou, and H. Zheng. 2010. Investigations into the ignition behaviours of pulverized coals and coal blends in a drop tube furnace using flame monitoring techniques. Fuel 89:743–51. doi:10.1016/j.fuel.2009.06.010.
  • Feng, Y., Z. Luo, Y. Li, and M. Zhou. 2017. Coal type identification based on the emission spectra of a furnace flame. J. Zhejiang Univ.Sci. A 18:113–23. doi:10.1631/jzus.A1500306.
  • Friedman, J. H. 2001. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29:1189–232. doi:10.1214/aos/1013203451.
  • Gaydon, A., and H. Wolfhad. 1994. Flames- their structure, radiation and temperature, 34–56. Beijing, China: China Science and Technology Press.
  • Geurts, P., D. Ernst, and L. Wehenkel. 2006. Extremely randomized trees. Mach. Learn. 63:3–42. doi:10.1007/s10994-006-6226-1.
  • Huang, Y., S. Liu, J. Li, L. Jia, and Z. Li. 2011. Fuel identification based on the least squares support vector machines. Adv. Mater. Res. 317–319:1237–40. doi:10.4028/www.scientific.net/AMR.317-319.1237.
  • Li, N., G. Lu, X. Li, and Y. Yan. 2015a. Prediction of pollutant emissions of biomass flames through digital imaging, contourlet transform, and support vector regression modeling. IEEE Trans. Instrum. Meas. 64:2409–16. doi:10.1109/TIM.2015.2411999.
  • Li, N., G. Lu, X. Li, and Y. Yan. 2015b. Prediction of NOx emissions from a biomass fired combustion process based on flame radical imaging and deep learning techniques. Combust. Sci. Technol. 188:233–46. doi:10.1080/00102202.2015.1102905.
  • Li, X., N. Li, G. Lu, and Y. Yan 2013. On-line identification of biomass fuels based on flame radical and application of support vector machine techniques. In: 2nd IET Renewable Power Generation Conference; Septemper 1–4; Beijing, China. p. 9–11.
  • Li, X., Y. Li, G. Lu, and Y. Yan. 2018. Biomass fuel identification based on flame spectroscopy and feature engineering. Proc. CSEE (in Chinese) 38:4474–81.
  • Li, X., M. Wu, G. Lu, Y. Yan, and S. Liu. 2015c. On-line identification of biomass fuels based on flame radical imaging and application of radical basis function neural network techniques. IET Renewable Power Gener. 9:323–30. doi:10.1049/iet-rpg.2013.0392.
  • Quinlan, J. R. 1986. Induction of decision trees. Mach. Learn. 1:81–106. doi:10.1007/BF00116251.
  • Suchetana, B., B. Rajagopalan, and J. Silverstein. 2017. Assessment of wastewater treatment facility compliance with decreasing ammonia discharge limits using a regression tree model. Sci. Total Environ. 598:249–57. doi:10.1016/j.scitotenv.2017.03.236.
  • Tan, C., L. Xu, X. Li, and Y. Yan. 2012. Independent component analysis-based fuel type identification for coal-fired power plant. Combust. Sci. Technol. 184:277–92. doi:10.1080/00102202.2011.635613.
  • Vapnik, V. 1998. Statistical learning theory, 2–3. New York, USA: Springer.
  • Xu, L., C. Tan, and X. Li. 2012. Fuel-type identification using joint probability density arbiter and soft-computing techniques. IEEE Trans. Instrum. Meas. 61:286–96. doi:10.1109/TIM.2011.2164836.
  • Xu, L., Y. Yan, S. Cornwell, and G. Riley. 2004. Online fuel identification using digital signal processing and fuzzy inference techniques. IEEE Trans. Instrum. Meas. 53:1316–20. doi:10.1109/TIM.2004.830573.
  • Xu, L., Y. Yan, S. Cornwell, and G. Riley. 2005. Online tracking by combing principal component analysis and neural network techniques. IEEE Trans. Instrum. Meas. 54:1640–45. doi:10.1109/TIM.2005.851203.
  • Zhou, H., Q. Tang, L. Yang, Y. Yan, G. Lu, and K. Cen. 2014. Support vector machine based online coal identification through advanced flame monitoring. Fuel 117:944–51. doi:10.1016/j.fuel.2013.10.041.
  • Zumel, N., and J. Mount. 2016. Practical data science with R. Beijing: Mechanical Industry Press.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.