Advertisement

Predictive model of gas consumption and air emissions of a lime kiln in a kraft process using the ABC/MARS-based technique

  • Víctor Manuel González Suárez
  • Esperanza García-Gonzalo
  • Ricardo Mayo Bayón
  • Paulino José García Nieto
  • Juan Carlos Álvarez Antón
ORIGINAL ARTICLE
  • 40 Downloads

Abstract

The kraft manufacturing process is the main pulping process in the paper industry. The kraft chemical recovery process is an efficient technology that enables the recycling of the pulping chemicals and the generation of electrical power. However, this process presents substantial issues related to energy consumption and environmental emissions. One of the main fundamental elements of the kraft process is the lime kiln. Lime kiln gas consumption, SO2, and NOx air emissions are key factors from the energy saving point of view (i.e., energy efficiency) and environmental pollution in this industrial process, respectively. Knowledge of the process variables involved in a lime kiln and how these are related to gas consumption and air emissions is essential to predict the kiln’s behavior and minimize its environmental effects. The aim of this research study is to build a regression model for each one of the three prime variables (gas consumption, SO2, and NOx emissions) of a lime kiln employed in the paper manufacturing process using the multivariate adaptive regression splines (MARS) method in combination with the artificial bee colony (ABC) technique. These two statistical learning techniques were combined, thereby obtaining an easy-to-interpret mathematical model with a high goodness-of-fit. A coefficient of determination greater than 0.9 is obtained for all the modeled variables. Moreover, the particular contribution or importance of the input variables in each model is also calculated. The results thus obtained are a useful instrument to gain a better understanding of the dynamics of the lime kiln and the involvement of the process variables in gas consumption and gas emissions.

Keywords

Kraft manufacturing process Lime kiln Multivariate adaptive regression splines (MARS) Artificial bee colony (ABC) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

We would like to acknowledge the active role of the employees of the company ENCE (ENergía y CElulosa S.A.) at their location in the town of Navia (Asturias) in the acquisition of the process variables and their great interest in the results of this research study. They always showed diligence and a high degree of availability to help us understand the nature and dynamics of the process. Additionally, we would like to thank Paul Barnes for his revision of English grammar and spelling of the manuscript.

References

  1. 1.
    Emple HJ (2009) Fundamentals of the kraft recovery process. Tappi Press, Peachtree Corners, GeorgiaGoogle Scholar
  2. 2.
    Boateng AA (2015) Rotary kilns: transport phenomena and transport processes. Butterworth-Heinemann, OxfordGoogle Scholar
  3. 3.
    Alcántara V, Cadavid Y, Sánchez M, Uribe C et al (2018) A study case of energy efficiency, energy profile, and technological gap of combustion systems in the Colombian lime industry. Appl Therm Eng 128:393–401CrossRefGoogle Scholar
  4. 4.
    Piedralba L, Blanco E, Alvarez E (2014) Analysis and perspectives of the pulp and paper industry in the present energetic context (in Spanish). Dissertation, University of Oviedo. http://hdl.handle.net/10651/27926. Accessed 28 May 2018
  5. 5.
    Francey S, Tran H, Berglin N (2011) Survey on lime kiln operation, energy consumption, and alternative fuel usage. TAPPI J 19(8):19–26Google Scholar
  6. 6.
    Kuparinen K, Vakkilainen E (2017) Green pulp mill: renewable alternatives to fossil fuels in lime kiln operations. Bioresources 12(2):4031–4048CrossRefGoogle Scholar
  7. 7.
    Francey S, Tran H, Jones A (2009) Current status of alternative fuel use in lime kilns. TAPPI J 8(9):33–39Google Scholar
  8. 8.
    Syamsudin, Susanto H (2013) Study on alternative fuels for lime kiln in a kraft pulp mill via direct combustion and gasification. J Selulosa 3(1):43–50Google Scholar
  9. 9.
    Mannig R, Tran H (2015) Impact of cofiring biofuels and fossil fuels on lime kiln operation. TAPPI J 14(7):475–480Google Scholar
  10. 10.
    Lundqvist P (2009) Mass and energy balances over the lime kiln in a kraft pulp mill. Dissertation, University of Upsala. http://www.diva-portal.org/smash/get/diva2:461011/FULLTEXT01.pdf. Accessed 28 May 2018
  11. 11.
    Bordado JCM, Gomes JFP (2002) Atmospheric emissions of kraft pulp mills. Chem Eng Process 41:667–671CrossRefGoogle Scholar
  12. 12.
    Bordado JCM, Gomes JFP (2003) Emission and odour control in kraft pulp mills. J Clean Prod 11:797–801CrossRefGoogle Scholar
  13. 13.
    Hakkarainen T (2014) Reduction of nitrogen oxide emissions in lime kiln. Dissertation, Lappeenranta University of Technology. http://www.doria.fi/handle/10024/102230. Accessed 28 May 2018
  14. 14.
    Choudhary AK, Harding JA, Tiwari MK (2009) Data mining in manufacturing: a review based on the kind of knowledge. J Intell Manuf 20(5):501–521CrossRefGoogle Scholar
  15. 15.
    Yongchang C (2017) Modeling for the calcination process of industry rotary kiln using ANFIS coupled with a novel hybrid clustering algorithm. Math Probl Eng 2017(1067351):1–8.  https://doi.org/10.1155/2017/1067351 CrossRefGoogle Scholar
  16. 16.
    Zang L, Chengjin Z, Qingyang X, Chaoyang W (2014) Modelling of lime kiln using subspace method with new order selection criterion. Math Probl Eng 2014(816831):1–11.  https://doi.org/10.1155/2014/816831 CrossRefGoogle Scholar
  17. 17.
    Yongchang C (2012) Research on soft measurement modeling for industry rotary kiln based on flexible neural network. In: 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE). IEEE, pp 343–346.  https://doi.org/10.1109/ICCSEE.2012.358
  18. 18.
    Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–141MathSciNetCrossRefGoogle Scholar
  19. 19.
    Hastie T, Tibshirani R, Friedman JH (2003) The elements of statistical learning. Springer Verlag, New YorkzbMATHGoogle Scholar
  20. 20.
    Vapnik VN (1998) Statistical learning theory. Wiley, New YorkzbMATHGoogle Scholar
  21. 21.
    Karaboga D, Akay B (2009) A survey: algorithms simulating bee swarm intelligence. Artif Intell Rev 31:68–85CrossRefGoogle Scholar
  22. 22.
    Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23:1001–1014CrossRefGoogle Scholar
  23. 23.
    Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42:21–57CrossRefGoogle Scholar
  24. 24.
    Brajevic I, Tuba M (2013) An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems. J Intell Manuf 24:729–740CrossRefGoogle Scholar
  25. 25.
    Milborrow S (2013) Earth: multivariate adaptive regression spline models, R package v3.2–7 comprehensive R archive network. https://cran.r-project.org/web/packages/earth/earth.pdf Acessed 28 May 2018
  26. 26.
    Vega G, Muñoz E (2013) ABCoptim:implementation of artificial bee colony (ABC) optimization. R package version 0.13.11 comprehensive R archive network. https://cran.r-project.org/web/packages/ABCoptim/ABCoptim.pdf. Accessed 28 May 2018
  27. 27.
    Friedman JH, Roosen CB (1995) An introduction to multivariate adaptive regression splines. Stat Methods Med Res 4:197–217CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Víctor Manuel González Suárez
    • 1
  • Esperanza García-Gonzalo
    • 2
  • Ricardo Mayo Bayón
    • 1
  • Paulino José García Nieto
    • 2
  • Juan Carlos Álvarez Antón
    • 1
  1. 1.Department of Electrical EngineeringUniversity of OviedoGijónSpain
  2. 2.Department of MathematicsUniversity of OviedoOviedoSpain

Personalised recommendations