Published Jun 12, 2017



PLUMX
Almetrics
 
Dimensions
 

Google Scholar
 
Search GoogleScholar
Downloads


Edgar Garcia-Morantes, MSc

Ivan Amaya-Contreras, PhD

Rodrigo Correa-Cely, PhD

##plugins.themes.bootstrap3.article.details##

Abstract

Introduction: This work considered real-time prediction of physicochemical parameters for a sample heated in a uniform electromagnetic field. Methodology: This work initiated with a literature search, which showed a steadily increasing of research works dealing with inverse problems. As a demonstrative model, we estimated the thermal conductivity and the volumetric heat capacity (A sample (with known geometry) was subjected to electromagnetic radiation, generating a uniform and time constant volumetric heat flux within it. Real temperatura profile was simulated adding white Gaussian noise to the original data, obtained from the theoretical model. For solving the objective function, simulated annealing and genetic algorithms, along with the traditional Levenberg-Marquardt method were used for comparative purposes. Results: results showed similar findings of all algorithms for three simulation scenarios, as long as the signal-to-noise-ratio sits at least at 30 [dB]. Furthermore, Genetic Algorithms gave acceptable results, and improve the search space of the other two methods. Conclusion: Finally, for practical purposes, the estimation procedure presented here requires both, a good experimental design and a correctly specified electronic instrumentation. If both requirements are satisfied simultaneously, it is posible to estimate these type of parameters on-line, without need for an additional experimental setup.

Keywords

calentamiento con microondas, problemas inversos, estimación de parámetros, campo electromagnéticoMicrowave heating, inverse problems, parameter estimation, electromagnetic field

References
[1] M. J. Colaço, H. R. B. Orlande, and G. S. Dulikravich, “Inverse and optimization problems in heat transfer,” J. Brazilian Soc. Mech. Sci. Eng., vol. 28, no. 1, pp. 1–24, 2006.
[2] M. A. Ahmadi, M. Ebadi, A. Shokrollahi, and S. M. Javad Majidi, “Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir,” Appl. Soft Comput. J., vol. 13, no. 2, pp. 1085–1098, 2013.
[3] M. A. Ahmadi, R. Soleimani, M. Lee, T. Kashiwao, and A. Bahadori, “Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool,” Petroleum, vol. 1, no. 2, pp. 118–132, 2015.
[4] M. A. Ahmadi, M. Zahedzadeh, S. R. Shadizadeh, and R. Abbassi, “Connectionist model for predicting minimum gas miscibility pressure: Application to gas injection process,” Fuel, vol. 148, pp. 202–211, 2015.
[5] M. A. Ahmadi, B. Pouladi, Y. Javvi, S. Alfkhani, and R. Soleimani, “Connectionist technique estimates H2S solubility in ionic liquids through a low parameter approach,” J. Supercrit. Fluids, vol. 97, pp. 81–87, 2014.
[6] A. Shafiei, M. A. Ahmadi, S. H. Zaheri, A. Baghban, A. Amirfakhrian, and R. Soleimani, “Estimating hydrogen sulfide solubility in ionic liquids using a machine learning approach,” J. Supercrit. Fluids, vol. 95, pp. 525–534, 2014.
[7] M. A. Ahmadi and M. Ebadi, “Evolving smart approach for determination dew point pressure through condensate gas reservoirs,” Fuel, vol. 117, no. PARTB, pp. 1074–1084, 2014.
[8] P. Duda, “Solution of inverse heat conduction problem using the Tikhonov regularization method,” J. Therm. Sci., vol. 26, no. 1, pp. 60–65, 2017.
[9] M. Cui, K. Yang, X. Xu, S. Wang, and X. Gao, “A modified Levenberg–Marquardt algorithm for simultaneous estimation of multi-parameters of boundary heat flux by solving transient nonlinear inverse heat conduction problems,” Int. J. Heat Mass Transf., vol. 97, pp. 908–916, 2016.
[10] M. Shusser, “Using a double genetic algorithm for correlating thermal models,” Heat Transf. Eng., vol. 37, no. 10, pp. 889–899, 2016.
[11] E. Hetmaniok, “Solution of the two-dimensional inverse problem of the binary alloy solidification by applying the Ant Colony Optimization algorithm,” Int. Commun. Heat Mass Transf., vol. 67, pp. 39–45, 2015.
[12] R. Dasa, “Identification of materials in a hyperbolic annular fin for a given temperatura requirement,” Inverse Probl. Sci. Eng., vol. 24, no. 2, pp. 213–233, 2016.
[13] P . Mayeli, M. Nili-Ahmadabadi, H. Besharati-Foumani, “Inverse shape design for heat conduction problems via the ball spine algorithm,” Numer. Heat Transf. Part B Fundam. An Int. J. Comput. Methodol., vol. 69, no. 3, pp. 1–21, 2016. [Online]. doi: http://dx.doi.org/10.1080/10407790.2015.1096690
[14] W. Adamczyk, R. Białecki, and T. Kruczek, “Retrieving thermal conductivities of isotropic and orthotropic materials,” Appl. Math. Model., vol. 40, no. 4, pp. 3410–3421, 2016.
[15] S. Moaveni and J. Kim, “An inverse solution for reconstruction of the heat transfer coefficient from the knowledge of two temperature values in a solid substrate,” Inverse Probl. Sci. Eng., pp. 1–25, 2016.
[16] M. J. Huntul, D. Lesnic, and M. S. Hussein, “Reconstruction of time-dependent coefficients from heat moments,” Appl. Math. Comput., vol. 301, pp. 233–253, 2017.
[17] M. Kant and P. R. von Rohr, “Determination of surface heat flux distributions by using surface temperature measurements and applying inverse techniques,” Int. J. Heat Mass Transf., vol. 99, pp. 1–9, 2016.
[18] A. Wróblewska, A. Frąckowiak, and M. Ciałkowski, “Regularization of the inverse heat conduction problem by the discrete Fourier transform,” Inverse Probl. Sci. Eng., vol. 24, no. 2, pp. 195–212, 2016.
[19] E. Aarts, J. Korst, and W. Michiels, “Simulated annealing,” in Search Methodologies Introductory Tutorials in Optimization and Decision Support Techniques, 2nd ed., 2014, pp. 265–285.
[20] K. Sastry, D. E. Goldberg, and G. Kendall, “Genetic Algorithms,” in Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, 2nd ed. New York: Springer, 2014, pp. 93–118.
How to Cite
Garcia-Morantes, E., Amaya-Contreras, I., & Correa-Cely, R. (2017). Real-time estimation of some thermodynamics properties puring a microwave heating process. Ingenieria Y Universidad, 21(2), 231-256. https://doi.org/10.11144/Javeriana.iyu21-2.rest
Section
Electrical and computer engineering