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

• Published in: International journal of advanced manufacturing technology Vol. 100; no. 5-8; pp. 1549 - 1562
• Main Authors: González Suárez, Víctor Manuel, García-Gonzalo, Esperanza, Mayo Bayón, Ricardo, García Nieto, Paulino José, Álvarez Antón, Juan Carlos
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2019; Springer Nature B.V
• Subjects: CAE) and Design; Computer-Aided Engineering (CAD; Energy conservation; Energy consumption; Engineering; Environmental effects; Goodness of fit; Industrial and Production Engineering; Kilns; Kraft process (woodpulp); Lime; Mechanical Engineering; Media Management; Nitrogen oxides; Organic chemistry; Original Article; Power consumption; Prediction models; Process variables; Pulp & paper industry; Pulping; Regression models; Splines; Statistical analysis; Sulfur dioxide; Swarm intelligence; Lime kiln; Multivariate adaptive regression splines (MARS); Artificial bee colony (ABC); Kraft manufacturing process
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-018-2773-4

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, SO 2 , and NO x 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, SO 2 , and NO x 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.