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A Study on NOx Emission Control Methods in the Cement Firing Process Using Data Mining Techniques

데이터 마이닝을 이용한 시멘트 소성공정 질소산화물(NOx)배출 관리 방법에 관한 연구

  • Park, Chul Hong (Dept. of Industrial and Management Engineering, Kyonggi University Graduate School) ;
  • Kim, Yong Soo (Dept. of Industrial and Management Engineering, Kyonggi University)
  • 박철홍 (경기대학교 일반대학원 산업경영공학과) ;
  • 김용수 (경기대학교 산업경영공학과)
  • Received : 2018.08.31
  • Accepted : 2018.09.07
  • Published : 2018.09.30

Abstract

Purpose: The purpose of this study was to investigate the relationship between kiln processing parameters and NOx emissions that occur in the sintering and calcination steps of the cement manufacturing process and to derive the main factors responsible for producing emissions outside emission limit criteria, as determined by category models and classification rules, using data mining techniques. The results from this study are expected to be useful as guidelines for NOx emission control standards. Methods: Data were collected from Precalciner Kiln No.3 used in one of the domestic cement plants in Korea. Thirty-four independent variables affecting NOx generation and dependent variables that exceeded or were below the NOx emiision limit (>1 and <0, respectively) were examined during kiln processing. These data were used to construct a detection model of NOx emission, in which emissions exceeded or were below the set limits. The model was validated using SPSS MODELER 18.0, artificial neural network, decision treee (C5.0), and logistic regression analysis data mining techniques. Results: The decision tree (C5.0) algorithm best represented NOx emission behavior and was used to identify 10 processing variables that resulted in NOx emissions outside limit criteria. Conclusion: The results of this study indicate that the decision tree (C5.0) can be applied for real-time monitoring and management of NOx emissions during the cement firing process to satisfy NOx emission control standards and to provide for a more eco-friendly cement product.

Keywords

References

  1. Ham, S. W. 2007. "Application of SNCR Process for NOx Removal from Limestone Calcining Process." Research Review Kyungil University 16(6):995-1002.
  2. International Cement Review Magazine. 2011. "The Global Cement Reort 9th Edition." International Cement Review
  3. Kang, S.-K., Azimov, U. B. and Kim, K.-S. 2007. "NOx Reduction Study in Oscillating Combustion Burner." Journal of Korean Society of Combustion 12(4):22-30.
  4. Kim, J. K., et al. 2011. "A Study on Quality Control Using Data Mining in Steel Continuous Casting Process." Journal of the Korea Society of IT Services 10(3):113-126. https://doi.org/10.9716/KITS.2011.10.3.113
  5. Kim, Y. S., et al. 2003. "PA17) A pilot study of NOx concentration emitted from cement industry." Proceeding of the 35th Meeting of KOSAE 235-236.
  6. Lee, H. and Nam. H. S. 2006. "A Quality Data Mining System in TFT-LCD Industry." Journal of the Korean Society for Quality Management 34(1):13-21.
  7. Lee, J. H., Yu, S. J. and Park, S. C. 2001. "Design of Intelligent Material Quality Control System based on Pattern Analysis using Artificial Neural Network." Journal of the Korean Society for Quality Management 29(4):38-53.
  8. Lim, K. K., et al. 1998. "Process Analysis for DeNOx in Cement Calcination Process." Proceeding of the Meeting of KOSAE 146-147.
  9. Liu, H. B., Chen, X. D. and Gu, J. 2014. "NOx reduction review on fuel alternative in cement kiln." Advanced Materials Research 864:1626-1629.
  10. Lv, D., et al. 2016. "Effects of co-processing sewage sludge in cement kiln on NOx, NH3 and PAHs emissions." Chemosphere 159: 595-601. https://doi.org/10.1016/j.chemosphere.2016.06.062
  11. Ohno, M., Kurokawa, D. and Hirao, H. 2012. "Establishment of a cement quality predictive system-Analyses of the relationships between characteristics ana properities of cement-." Cement Science and Concrete Technology 66(1):87-94. https://doi.org/10.14250/cement.66.87
  12. Song, S.-I., Jung, H.-J. and Shin. S.-M. 2006. "Optimization Methodology Integrated Data Mining and Statistical Method." Journal of the Korean Society for Quality Management 34(4):33-41.
  13. Zhang, Y., et al. 2017. "ANN-GA approach for predictive modelling and optimization of NOx emissions in a cement precalcining kiln." International Journal of Environmental Studies 74(2):253-261. https://doi.org/10.1080/00207233.2017.1280322