Dynamic Modeling of the Drying Process of Corn Grains using Neural Networks

https://doi.org/10.22146/agritech.44483

Galih Kusuma Aji(1*), Wildan Fajar Bachtiar(2), Henry Yuliando(3), Endy Suwondo(4)

(1) Department of Bioresource Technology and Veterinary, Vocational College, Universitas Gadjah Mada, Jl. Yacaranda, Sekip Unit II, Yogyakarta 55281
(2) Department of Bioresource Technology and Veterinary, Vocational College, Universitas Gadjah Mada, Jl. Yacaranda, Sekip Unit II, Yogyakarta 55281
(3) Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jl. Flora No. 1 Bulaksumur, Yogyakarta 55281
(4) Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jl. Flora No. 1 Bulaksumur, Yogyakarta 55281
(*) Corresponding Author

Abstract


This study examines the model development of the drying process of corn grains as a dynamic system. The appropriate use of a dynamic model for the drying process of corn grains could lead to an effective method for optimizing the system. The optimal control strategy can be determined by predicting the future behaviors of the process using a dynamic model. In this work, the dynamic characteristic of the water loss of corn grains during dynamics treatment of temperature in the drying process was measured in a continuous manner using a precise load cell. The nonlinear autoregressive with external input (NARX) neural network is used to identify and develop a model of dynamic characteristics of the drying process of corn grains. Then for model training and validation, the dynamic responses of the rate of water loss of corn grains to drying temperature were used. A three-layered NARX neural network model consists of 1-10-1 number neurons of each layer with two times delay was successfully developed to identify and make a model such a complex system. The developed model showed the accuracy of the rate water loss of corn grains during the drying process with the mean square error (MSE), and coefficient of determination (R-squared) values are 1.88892 x 10-4 and 0.891594 consecutively.


Keywords


Drying temperature; neural networks; drying process; dynamic model; the rate of water loss

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References

Aghbashlo, M., Hosseinpour, S., & Mujumdar, A. S. (2015). Application of Artificial Neural Networks (ANNs) in Drying Technology: A Comprehensive Review. Drying Technology. https://doi.org/10.1080/07373937.2015.1036288

Almási, A.-D., Woźniak, S., Cristea, V., Leblebici, Y., & Engbersen, T. (2016). Review of advances in neural networks: Neural design technology stack. Neurocomputing, 174, 31–41. https://doi.org/10.1016/J.NEUCOM.2015.02.092

Baloch, M. S., Morimoto, T., & Hatou, K. (2006). Stress Application Loss of Fruits Technique during for Storage Reducing Water. Environmental Control in Biology, 44(1), 31–40.

Beale, M. H., Hagan, M. T., & Demuth, H. B. (2016). Neural Network Toolbox (TM) User’s Guide. MathWorks (R2018a ed.). The MathWorks, Inc. https://doi.org/10.1002/0471221546

Chan, R. W. K., Yuen, J. K. K., Lee, E. W. M., & Arashpour, M. (2015). Application of Nonlinear-Autoregressive-Exogenous model to predict the hysteretic behaviour of passive control systems. Engineering Structures, 85, 1–10. https://doi.org/10.1016/j.engstruct.2014.12.007

Dai, A., Zhou, X., & Liu, X. (2017). A GODFIP Control Algorithm for an IRC Grain Dryer. Mathematical Problems in Engineering, 2017, 1–14. https://doi.org/10.1155/2017/1406292

Ekhwan Toriman, M., Ekhwan Toriman, M., Juahir, H., Mokhtar, M., Barzani Gazim, M., Mastura Syed Abdullah, S., & Jaafar, O. (2009). Predicting for Discharge Characteristics in Langat River, Malaysia Using Neural Network Application Model. Research Journal of Earth Sciences, 1(1), 15–21.

Fasol, K. H., & Jörgl, H. P. (1980). Principles of model building and identification. Automatica, 16(5), 505–518. https://doi.org/10.1016/0005-1098(80)90074-6

Iooss, B., & Lemaître, P. (2015). A review on global sensitivity analysis methods. Operations Research/ Computer Science Interfaces Series. https://doi.org/10.1007/978-1-4899-7547-8_5

Isermann, R., Ernst, S., & Nelles, O. (1998). Identification with dynamic neural networks - Architectures, comparisons, applications. (Sysid’97): System Identification, Vols 1-3, 947–972.

Kuhn, M., & Johnson, K. (2013). Measuring Performance in Regression Models. In Applied Predictive Modeling. https://doi.org/10.1007/978-1-4614-6849-3_5

Lin, T., Horne, B. G., Tiňo, P., & Giles, C. L. (1996). Learning long-term dependencies in NARX recurrent neural networks. IEEE Transactions on Neural Networks, 7(6), 1329–1338. https://doi.org/10.1109/72.548162

Ljung, L. (2010). Perspectives on system identification. Annual Reviews in Control, 34(1), 1–12. https://doi.org/10.1016/J.ARCONTROL.2009.12.001

Mai, C. V., Spiridonakos, M. D., Chatzi, E. N., & Sudret, B. (2016). Surrogate modeling for stochastic dynamical systems by combining nonlinear autoregressive with exogenous input models and polynomial chaos expansions. International Journal for Uncertainty Quantification, 6(4).

Mohd, N., & Aziz, N. (2016). Performance and robustness evaluation of Nonlinear Autoregressive with Exogenous input Model Predictive Control in controlling industrial fermentation process. Journal of Cleaner Production, 136, 42–50. https://doi.org/10.1016/j.jclepro.2016.06.191

Morimoto, T, Islam, P., Suyantohadi, A., & Ouchi, Y. (2010). Dynamic optimization of watering for maximizing the sugar content and size of Satsuma mandarin using intelligent approaches. IFAC Proceedings Volumes (Vol. 43). https://doi.org/10.3182/20101206-3-JP-3009.00043

Morimoto, T, & Hashimoto, Y. (2009). Speaking plant/fruit approach for greenhouses and plant factories. Environmental Control in Biology, 47(2), 55–72.

Ng, B. C., Darus, I. Z. M., Jamaluddin, H., & Kamar, H. M. (2014). Dynamic modelling of an automotive variable speed air conditioning system using nonlinear autoregressive exogenous neural networks. Applied Thermal Engineering, 73(1), 1253–1267. https://doi.org/10.1016/j.applthermaleng.2014.08.043

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (2013). Learning Internal Representations by Error Propagation. In Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence (pp. 399–421). https://doi.org/10.1016/B978-1-4832-1446-7.50035-2

Yumeina, D., Aji, G. K., & Morimoto, T. (2017). Dynamic optimization of water temperature for maximizing leaf water content of tomato in hydroponics using an intelligent control technique. Acta Horticulturae (Vol. 1154). https://doi.org/10.17660/ActaHortic.2017.1154.8



DOI: https://doi.org/10.22146/agritech.44483

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