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Modeling of lime production process using artificial neural network

  • Abolghasem Daeichian ORCID logo EMAIL logo , Rana Shahramfar and Elham Heidari ORCID logo
Published/Copyright: July 26, 2021
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Chemical Product and Process Modeling
From the journal Chemical Product and Process Modeling Volume 17 Issue 6

Abstract

Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

Keywords: agglomeration; artificial neural network; lime production; optimum model; rotary kiln modeling
Corresponding author: Abolghasem Daeichian, Department of Electrical Engineering, Faculty of Engineering, Arak University, 38156-8-8349 Arak, Iran; and Research Institute of Renewable Energy, Arak University, 38156-8-8349 Arak, Iran, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2021-05-05
Accepted: 2021-07-09
Published Online: 2021-07-26

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. CFD simulation of the ethylbenzene dehydrogenation reaction in the fixed bed reactor with a cylindrical catalyst of various sizes
  4. Energy and exergy optimization of oxidative steam reforming of acetone–butanol–ethanol–water mixture as a renewable source for H2 production via thermodynamic modeling
  5. A novel LSSVM-L Hammerstein model structure for system identification and nonlinear model predictive control of CSTR servo and regulatory control
  6. Environmental and thermodynamic performance assessment of biomass gasification process for hydrogen production in a downdraft gasifier
  7. Modeling of lime production process using artificial neural network
  8. Control of TITO processes using sliding mode controller tuned by ITAE minimizing criterion based Nelder-Mead algorithm
  9. To the problem of forming the equation system for pressure swing adsorption mathematical model
  10. Review
  11. A comparative study of various Smith predictor configurations for industrial delay processes
https://doi.org/10.1515/cppm-2021-0032
Keywords for this article
agglomeration; artificial neural network; lime production; optimum model; rotary kiln modeling

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. CFD simulation of the ethylbenzene dehydrogenation reaction in the fixed bed reactor with a cylindrical catalyst of various sizes
  4. Energy and exergy optimization of oxidative steam reforming of acetone–butanol–ethanol–water mixture as a renewable source for H2 production via thermodynamic modeling
  5. A novel LSSVM-L Hammerstein model structure for system identification and nonlinear model predictive control of CSTR servo and regulatory control
  6. Environmental and thermodynamic performance assessment of biomass gasification process for hydrogen production in a downdraft gasifier
  7. Modeling of lime production process using artificial neural network
  8. Control of TITO processes using sliding mode controller tuned by ITAE minimizing criterion based Nelder-Mead algorithm
  9. To the problem of forming the equation system for pressure swing adsorption mathematical model
  10. Review
  11. A comparative study of various Smith predictor configurations for industrial delay processes
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