Optimum Air Velocity, Air Temperature and Maize Layer Thickness for Highest Moisture Removal Rate and Drying Efficiency in a Forced Convection Grain Dryer

Booker Osodo^1, , Daudi Nyaanga², Jeremiah Kiplagat³ and Joseph Muguthu⁴

¹Department of Industrial and Energy Engineering, Egerton University, Kenya

²Department of Agricultural Engineering, Egerton University, Kenya

³Institute of Energy Research, Nairobi, Kenya

⁴Department of Energy Technology, Kenyatta University

Cite this paper:
Booker Osodo, Daudi Nyaanga, Jeremiah Kiplagat and Joseph Muguthu. Optimum Air Velocity, Air Temperature and Maize Layer Thickness for Highest Moisture Removal Rate and Drying Efficiency in a Forced Convection Grain Dryer. American Journal of Food Science and Technology. 2018; 6(6):263-273. doi: 10.12691/ajfst-6-6-6

Abstract

The performance of a forced convection grain dryer may be evaluated based on different criteria, such as drying rate, moisture removal rate and efficiency. This performance is dependent upon various drying parameters, such drying air velocity and temperature as well as grain layer thickness. It is necessary to apply an optimal combination of levels of the various parameters in order to achieve improved performance of such a dryer. This study developed an experimental grain dryer and investigated its performance under different drying conditions. The Taguchi approach was used to determine the optimal combination of drying air velocity, temperature and grain layer thickness that could be used to ensure greatest drying efficiency and moisture removal rate (MRR). ANOVA and LSD tests were used to determine whether change of air velocity and grain layer thicknesses significantly affected drying efficiency as well as MRR.The experimental grain dryer developed was of dimensions 0.5 m x 0.5 m x 1.0 m and was equipped with a 0.7 kW centrifugal fan. It was found that the optimal combination of grain layer thickness and air velocity were 0.04 m and 0.34 m/s respectively for solar drying, if drying efficiency was the determining criterion. When drying was done under laboratory conditions, a combination of 0.41 m/s air velocity, 45°C air temperature and 0.02m layer thickness resulted in greatest MRR and drying efficiency. These findings are useful because use of combination enable the design and use of such a dryer in a manner that ensures minimal energy wastage. Appropriate time management is also facilitated as drying can be undertaken at the shortest possible time.

Keywords:
forced convection grain dryer moisture removal rate drying efficiency taguchi approach of optimisation

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Bolaji, B.O., Olalusi, A.P. “Performance evaluation of a mixed mode solar dryer”, AU.J.T, 11(4). 225-231. 2008.
[2]	Hodges, R. Supplying cereal grain postharvest losses information: the examples of alphis (African postharvest loss information system), Natural Resources Institute, UK. 2009 (Retrieved from. http:/www.rsis.edu.sg/nts/doc/session%20420Hodgespdf on 13th May, 2013).
[3]	Tiwari, G.N. Solar energy: fundamentals, design, modeling and applications, Alpha Science International Limited, Parybourne. 2002.
[4]	Twidell, J.W. and Weir, A.D. Renewable energy resources, English Language Book Society, London. 2006.
[5]	Barawal, P., Tiwari, G.N. “Grape drying by using photovoltaic thermal (pv/t) greenhouse dryer: an experimental study of solar energy”, Solar Energy, 62(12). 1131-1144. 2008.
[6]	Akinona, O.A., Akinyemi, A.A. and Bolaji, B.O. “Evaluation of traditional and solar fish drying systems towards enhancing fish storage and preservation in Nigeria”, Fish International: Pakistan, 1(3). 44-49. 2006.
[7]	Mercer, D.G. “A comparison of the kinetics of mango drying in open-air, solar and forced air dryers”, African Journal of Food, Agriculture, Nutrition and Development, 12(7). 6835- 6852. 2012.
[8]	Sallam, Y.I., Aly, M.H., Nassar, A.F. and Mohamed, E.A. “Solar drying of whole mint plant under natural and forced convection”, Journal of Advanced Research, 6. 171-178. 2013.
[9]	Mohanraj, M. and Chandrasekar, P. “Performance of a forced convection solar dryer integrated with gravel as heat storage material for chili drying”, Journal of Engineering Science and Technology, 4(3). 305-314. 2009.
[10]	Kassem, A.S., Al-Sulaiman, M.A., Aboukarima, A.M. and Kassem, S.S. “Predicting drying efficiency during solar drying process of grapes clusters in a box dryer using artificial neural network”, Australian Journal of Basic and Applied Sciences, 5(6). 230-241. 2011.
[11]	Parkinson, A.R., Balling, R.J. and Hedengren, J.D. Optimisation methods for Engineering design: applications and theory, Brigham Young University. 2013.
[12]	Olason, A. and Tidman, D. Methodology for topology and shape optimisation in the design process. Masters Thesis. Chalmers University of Technology. Goteborg, Sweden. 2011.
[13]	Fang, X. Engineering design using genetic algorithms, Doctor of Philosophy Dissertation, Iowa State University, Iowa, USA. 2007.
[14]	Brackett, D., Ashcroft, I. and Hague, R. Topology optimization for additive manufacturing. Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Leiceterture, UK. 2011.
[15]	Deb, K. Introduction to genetic algorithms for engineering optimisation. link.springer.com/chapter/10.1007%2F978-3-540-39980-8_2. 2004. Retrieved on 10th March 2017.
[16]	Mahajan, R. and Kaur, G. “Neural networks using genetic algorithms”, International Journal of Computer Applications 77(14). 6-11. 2013.
[17]	Berke, L., Patnaik, S.N. and Murthy, P.L.N. Application of artificial neural networks to the design optimisation of aerospace structural components. National Aeronotics and Space Administration (NASA) Technical Memorandum, NASA. https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19930012642.pdf. 1993. (Retrieved on 10th March 2017).
[18]	Somasiri, N.P., Chen, X. and Rezazadeh, A.A. “Neural network modeller for design optimisation of multilayer patch antennas”, In IEE Proceedings. Ieeexplore.iee.org/stamp/stamp.jsp?arnumber=1387753. 2004. (Retrieved on 10th March 2017).
[19]	Fraley, S., Oom, M., Terrien, B. and Zelewski, J. Design of Experiments via Taguchi Methods: Orthogonal Arrays. https://controls.engin.umich.edu/wiki/index.php/Design_of_experiments_via_taguchi_methods:_orthogonal_arrays. Retrieved on 5th January 2016.
[20]	Kamaruddin, S., Khan, Z.A. and Foong, S.H. “Application of Taguchi method in the optimisation of injection moulding parameters for manufacturing products from plastic blend”, International Journal of Engineering and Technology, 2(6). 574-580. 2010.
[21]	Muguthu, J.N., Dong, G., and Ikua, B.W. “Optimisation of machining parameters influencing machinability of AI2124SiCp (45 % wt) metal matrix composite”, Journal of Composite Materials, 49 (2). 217-229. 2015.
[22]	Singaravel, B. and Selvaraj, T. “Application of Taguchi method for optimisation of parameters in turning operation”, Journal of Manufacturing Science and Production, 16 (3). 183-187. 2016.
[23]	Sunil, S.N., Garg, A. and Kumar, S. “An overview of optimisation techniques used in solar drying”, Asian Journal of Science and Applied Technology, 1(1). 5-11. 2013.
[24]	Sevik, S. “Design, experimental investigation and analysis of a solar dryer system”, Energy Conversion and Management, 68. 227-234. 2013.
[25]	Aissa, W., El-Sallak, M. and Elhakem, A. “Performance of solar dryer chamber used for convective drying of sponge-cotton”, Thermal Science, 18 (2). 451-462. 2014.
[26]	Balmine, M., Marcel, E., Alexis, K. and Belkacem, Z. “Experimental evaluation of the thermal performance of dryer airflow configuration”, International Journal of Energy Engineering, 5(4). 80-86. 2015.
[27]	Murthy, M.V.R. “A review of new technologies, models and experimental of solar dryers”, Renewable and Sustainable Energy Reviews. 5.835-844. 2009.
[28]	Tasirin, S.M., Puspasari I., Xing, L.J., Yaaakob, Z. and Ghani, J.A. “Energy optimisation of fluidised bed drying of orange peel using Taguchi method”, World Applied Sciences Journal, 26(12). 1602-1609. 2013.
[29]	Nazgelichi, T., Jafari, A., Kianmehr M.H. and Aghbashlo M. “CFD Simulation and optimisation of factors affecting the performance of a fluidised bed dryer”, Iran Journal of Chemical Engineering, 32(4). 81-92. 2013.