Keywords
Flue-cured tobacco
Process modeling
neural networks
estimation
prediction Tabaco curado
Modelado de procesos
Redes neuronales
Estimación
Predicción
Process modeling
neural networks
estimation
prediction Tabaco curado
Modelado de procesos
Redes neuronales
Estimación
Predicción
How to Cite
Martínez Martínez, V., Baladrón, C., Gómez Gil, J., Ruiz Ruiz, G., Navas Gracia, L. M., Aguiar, J. M., & Carro, B. (2013). Artificial neural network based model to calculate the environmental variables of the tobacco drying process. Informador Tecnico, 77(1), 35. https://doi.org/10.23850/22565035.43
Abstract
This paper presents an Artificial Neural Network (ANN) based model for environmental variables related to the tobacco drying process. A fitting ANN was used to estimate and predict temperature and relative humidity inside the tobacco dryer: the estimation consists of calculating the value of these variables in different locations of the dryer and the prediction consists of forecasting the value of these variables with different time horizons. The proposed model has been validated with temperature and relative humidity data obtained from a real tobacco dryer using a Wireless Sensor Network (WSN). On the one hand, an error under 2% was achieved, obtaining temperature as a function of temperature and relative humidity in other locations in the estimation task. Besides, an error around 1.5 times lower than the one obtained with an interpolation method was achieved in the prediction task when the temperature inside the tobacco mass was predicted with time horizons over 2.5 hours as a function of its present and past values. These results show that ANN-based models can be used to improve the tobacco drying process because with these types of models the value of environmental variables can be predicted in the near future and can be estimated in other locations with low errors.References
Akpan, V. A. and Hassapis, G. D.Nonlinear model identification and adaptive model predictive control using neural networks. ISA Transactions50(2). 2011; p. 177-194.
https://doi.org/10.1016/j.isatra.2010.12.007
AL SEYAB, R. K. and CAO, Y. Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation.Journal of Process Control18(6).2008; p. 568-581.
Banga, J. R. ANDSINGH; R. PAUL. Optimization of air dryingof foods.Journal of Food Engineering23(2).(1994); p. 189-211.
https://doi.org/10.1016/0260-8774(94)90086-8
Benne, M., B.; GRONDIN- PEREZ, J. CHABRIAT, P.and HERVÉ, P. Artificial neural networks for modelling and predictive control of an industrial evaporation process. Journal of Food Engineering46(4).(2000); p. 227-234.
https://doi.org/10.1016/S0260-8774(00)00055-8
Damour, C.;BENNE.M.; GRONDIN-PEREz, B.and. CHABRIAT, J.-PNonlinear predictive control based on artificial neural network model for industrial crystallization.Journal of Food Engineering99(2).(2010);p.225-231.
https://doi.org/10.1016/j.jfoodeng.2010.02.027
Dorffner, G. Neural Networks for Time Series Processing. Neural Network World6(4).(1996); p. 447-468.
Frost, A. R.;Schofield, C. P.; BEAULAH.S. A.; MOTTRAM, T. T.; LINES, J. A.and WATHES.C. M.A review of livestock monitoring and the need for integrated systems. Computers and Electronics in Agriculture17(2).(1997); 139-159.
https://doi.org/10.1016/S0168-1699(96)01301-4
Hawks, S. N.; COLLINS.W. K.; CARBONELL, H. P.ANDMARTÍNEZ, E. C. Tabaco flue-cured: principios básicos de su cultivo y curado, Ministerio de Agricultura, Pesca y Alimentación.1986. HILL, T., M. O'CONNOR and W. REMUS. "Neural Network Models for Time Series Forecasts." Management Science42(7): 1082-1092.
Holland, E. P., J. F. Burrow, C. Dytham and J. N. Aegerter. Modelling with uncertainty: Introducing a probabilistic framework to predict animal population dynamics.Ecological Modelling220(9–10).(2009) p.1203-1217.
https://doi.org/10.1016/j.ecolmodel.2009.02.013
Kwong, K. H., WU.T.-T.; GOH.H. G.; SASLOGLOU.K.; STEPHEN.B.; GLOVER.I.; SHEN.C.; DU.W.; MICHIE, C. and ANDONOVIC, I.Practical considerations for wireless sensor networks in cattle monitoring applications. Computers and Electronics in Agriculturevol 81.(2012); p. 33-44.
https://doi.org/10.1016/j.compag.2011.10.013
Li, Z., G. S. V. Raghavan and N. Wang. Carrot volatiles monitoring and control in microwave drying.LWT - Food Science and Technology43(2).(2010); p.291-297.
https://doi.org/10.1016/j.lwt.2009.08.002
Li, Z., G. S. V. Raghavan, N. Wang and Y. Gariepy.Real-time, volatile-detection-assisted control for microwave drying." Computers and Electronics in Agriculture69(2).(2009); p.177-184.
https://doi.org/10.1016/j.compag.2009.08.002
Li, Z.; Vijaya Raghavan, G. S.ANDWANG, N.APPLE volatiles monitoring and control in microwave drying. LWT – Food Science and Technology43(4).(2010); p. 684-689.
https://doi.org/10.1016/j.lwt.2009.11.014
Movagharnejad, K. and Nikzad, M.MODELING of tomato drying using artificial neural network.Computers and Electronics in Agriculture59(1–2).(2007); p. 78-85.
https://doi.org/10.1016/j.compag.2007.05.003
NATIONS., F. A. O. O. T. U. and T. H. P. S. Commodities Trade Division Agriculture Organization of the United Nations. Raw Materials. Issues in the global tobacco economy: selected case studies. Rome, Raw Materials, Tropical and Horicultural [sic] Products Service, Commodities and Trade Division, Food and Agricultural Organization of the United Nations.2003
PAPANTONIOU, A. and COONICK, A.Fuzzy logic control of a unified power flow controller for wind farm applications. Power Electronics for Renewable Energy (Digest No: 1997/170), IEE Colloquium on.
https://doi.org/10.1049/ic:19970922
Recknagel, F.; FRENCH.M.; HARKONEN, P. and YABUNAKA, K.-I.Artificial neural network approach for modelling and prediction of algal blooms.Ecological Modelling96(1–3). (1997); p. 11-28.
https://doi.org/10.1016/S0304-3800(96)00049-X
Singh, K. P., BASANT.A.; MALIK, A. and JAIN, G.Artificial neural network modeling of the river water quality—A case study.Ecological Modelling220(6).(2009); p. 888-895.
https://doi.org/10.1016/j.ecolmodel.2009.01.004
Smith, B. A.;Hoogenboom, G.ANDMCCLENDON, R. W.Artificial neural networks for automated year-round temperature prediction.Computers and Electronics in Agriculture68(1).(2009); p. 52-61.
https://doi.org/10.1016/j.compag.2009.04.003
Song, Y., Z. Chen and YUAN.Neural Network Nonlinear Predictive Control Based on Tent-map Chaos Optimization.Chinese Journal of Chemical Engineering15(4). (2007); p. 539-544.
https://doi.org/10.1016/S1004-9541(07)60121-9
TAMBOLI, N. and YOUNIS, M.Coverage-aware connectivity restoration in mobile sensor networks.Journal of Network and Computer Applications33(4).2010; 363-374.
Toğrul, H. Suitable drying model for infrared drying of carrot. Journal of Food Engineering77(3).(2006); p. 610-619.
https://doi.org/10.1016/j.jfoodeng.2005.07.020
Yliniemi, L. Advanced Control of a Rotary Dryer. Department of Process Engineering. Oulu, Finland, University of Oulu: 114. 1999. YLINIEMI, L., J. KOSKINEN and LEIVISKA, K.Advanced control of a rotary dryer. Oxford, Pergamon Press Ltd.
YUE, T.-X.; JORGENSEN, S. E. and G. R. LAROCQUE (2011). Progress in global ecological modelling.Ecological Modelling 222(14).(1999); p. 2172-2177.
https://doi.org/10.1016/j.ecolmodel.2010.06.008
Zhou, H. F., Y. Q. Ni and KO, J. M.Structural damage alarming using auto-associative neural network technique: Exploration of environment-tolerant capacity and setup of alarming threshold." Mechanical Systems and Signal Processing25(5). (2011); p.508-1526.
Downloads
Download data is not yet available.
