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Estimating Drying Curves and Diffusion Coefficients in Coffee Drying (Castilla Variety) through Global Optimization Strategies


Affiliations
1 Universidad Industrial de Santander, UIS, Santander, Colombia
2 Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico
 

Background/Objectives: We present an alternative for estimating drying curves and diffusion coefficients of coffee beans (Castilla variety), based on global optimization strategies. Methods: Four optimization algorithms were tested for adjusting drying curves. Based on the parameters that were found, we determined the diffusion coefficient. Algorithms were tuned up on 11 non-linear systems, prior to using them for adjusting the curves. Their performance were assessed through error dispersion analysis, as well as through the number of evaluations of the objective function and run time. Findings: On non-linear systems, Particle Swarm Optimization (PSO) and Drone Squadron Optimization (DSO) exhibited the best performance in terms of error. When used for estimating drying curves, PSO, DSO and Genetic Algorithms (GA) achieved determination coefficients beyond 0.99. Even so, GA had the lowest run time. Applications: Our experiments offer an alternative with excellent precision for estimating parameters of the drying function and of its diffusion coefficients for different coffee beans.
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  • Arunsandeep G, Lingayat A, Chandramohan VP, Raju VRK, Reddy KS. A numerical model for drying of spherical object in an indirect type solar dryer and estimating the drying time at different moisture level and air temperature, International Journal of Green Energy. 2018; 15(3):189−200. https://doi.org/10.1080/15435075.2018.143 3181.

  • Zuluaga-Bedoya C, Gomez LM. Dynamic modeling of coffee beans dryer. In: 2015 IEEE 2nd Colombian Conference on Automatic Control (CCAC) IEEE; 2015. p. 1−6. https://doi.org/10.1109/CCAC.2015.7345214.

  • Srivastava VK, John J. Deep bed grain drying modeling, Energy Conversion and Management. 2002; 43(13):1689−708. https://doi.org/10.1016/S01968904(01)00095-4.

  • Parry JL. Mathematical modelling and computer simulation of heat and mass transfer in agricultural grain drying: A review, Journal of Agricultural Engineering Research. 1985; 32(1):1−29. https://doi.org/10.1016/0021-8634(85)90116-7.

  • Prakash O, Laguri V, Pandey A, Kumar A, Kumar A. Review on various modelling techniques for the solar dryers, Renewable and Sustainable Energy Reviews. 2016; 62:396–417. https://doi.org/10.1016/j.rser.2016.04.028.

  • Parra-Coronado A, Roa-Mejía G, Oliveros-Tascon CE. SECAFÉ Parte I: Modelamiento y simulación matemáticaen el secado mecanico de cafe pergamino, Revista Brasileira Engenharia Agrícola e Ambiental. 2008; 12(4):415−27. https://doi.org/10.1590/S1415-43662008000400013.

  • Thompson TL, Peart RM, Foster GH. Mathematical Simulation of Corn Drying - A New Model, Transaction American Society of Agricultural and Biological Engineers (ASAE). 1968; 11(4):0582−86.

  • Ramadas GCV, Fernandes EMGP, Rocha AMAC. Finding Multiple Roots of Systems of Nonlinear Equations by a Hybrid Harmony Search-Based Multistart Method, Applied Mathematics and Information Sciences. 2018; 12(1):21−32. https://doi.org/10.18576/amis/120102.

  • Adekanmbi O, Green P. Conceptual Comparison of Population Based Metaheuristics for Engineering Problems, Scientific World Journal. 2015; 1−9. https://doi.org/10.1155/2015/936106. PMid: 25874265, PMCid: PMC4383342.

  • Tsoulos IG, Stavrakoudis A. On locating all ischolar_mains of systems of nonlinear equations inside bounded domain using global optimization methods, Nonlinear Analysis Real World Applications. 2010; 11(4):2465−71. https://doi.org/10.1016/j.nonrwa.2009.08.003.

  • Yang X-S. Nature-Inspired Metaheuristic Algorithms. Luniver Press Frome; 2010. p. 1−148.

  • de Melo VV, Banzhaf W. Drone Squadron Optimization: a novel self-adaptive algorithm for global numerical optimization, Neural Computing and Applications. 2018; 30(10):3117−44. https://doi.org/10.1007/s00521-017-2881-3.

  • Amaya I, Cruz J, Correa R. Real Roots of Nonlinear Systems of Equations through a Metaheuristic Algorithm, Dyna. 2011; 78(170):15−23.

  • Grosan C, Abraham A. A New Approach for Solving Nonlinear Equations Systems, IEEE Transactions Systems. 2008; 38(3):698−714. https://doi.org/10.1109/TSMCA.2008.918599.

  • John C. The mathematics of diffusion. Oxford University Press; 1979. p. 1−414.

  • Mu-oz M, Correa R, Roa M. Thermal Analysis of Coffee Hulls and their Effect on the Drying Process in Conventional Ovens, Indian Journal of Science and Technology. 2018; 11(36):1−13. https://doi.org/10.17485/ijst/2018/v11i36/131682.


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  • Estimating Drying Curves and Diffusion Coefficients in Coffee Drying (Castilla Variety) through Global Optimization Strategies

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Authors

Milton Munoz
Universidad Industrial de Santander, UIS, Santander, Colombia
Ivan Amaya
Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico
Rodrigo Correa
Universidad Industrial de Santander, UIS, Santander, Colombia

Abstract


Background/Objectives: We present an alternative for estimating drying curves and diffusion coefficients of coffee beans (Castilla variety), based on global optimization strategies. Methods: Four optimization algorithms were tested for adjusting drying curves. Based on the parameters that were found, we determined the diffusion coefficient. Algorithms were tuned up on 11 non-linear systems, prior to using them for adjusting the curves. Their performance were assessed through error dispersion analysis, as well as through the number of evaluations of the objective function and run time. Findings: On non-linear systems, Particle Swarm Optimization (PSO) and Drone Squadron Optimization (DSO) exhibited the best performance in terms of error. When used for estimating drying curves, PSO, DSO and Genetic Algorithms (GA) achieved determination coefficients beyond 0.99. Even so, GA had the lowest run time. Applications: Our experiments offer an alternative with excellent precision for estimating parameters of the drying function and of its diffusion coefficients for different coffee beans.

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DOI: https://doi.org/10.17485/ijst%2F2018%2Fv11i45%2F138198