Predictive model of gas consumption and air emissions of a lime kiln in a kraft process using the ABC/MARS-based technique
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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.
KeywordsKraft manufacturing process Lime kiln Multivariate adaptive regression splines (MARS) Artificial bee colony (ABC)
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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.
- 1.Emple HJ (2009) Fundamentals of the kraft recovery process. Tappi Press, Peachtree Corners, GeorgiaGoogle Scholar
- 2.Boateng AA (2015) Rotary kilns: transport phenomena and transport processes. Butterworth-Heinemann, OxfordGoogle Scholar
- 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.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
- 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.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.Mannig R, Tran H (2015) Impact of cofiring biofuels and fossil fuels on lime kiln operation. TAPPI J 14(7):475–480Google Scholar
- 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
- 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
- 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
- 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.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