Advertisement

Heat and Mass Transfer

, Volume 54, Issue 11, pp 3297–3305 | Cite as

Neural network modeling of drying of rice in BAU-STR dryer

  • Md. Ashraful Alam
  • Chayan Kumer Saha
  • Md. Monjurul Alam
  • Md. Ali Ashraf
  • Bilash Kanti Bala
  • Jagger Harvey
Original
  • 100 Downloads

Abstract

The experimental performance and artificial neural network modeling of rice drying in BAU-STR dryer is presented in this paper. The dryer consists of a biomass stove as a heat source, a perforated inner bin and a perforated outer bin with annular space for grains, and a blower (1 hp) to supply heated air. The dryer capacity was 500 kg of freshly harvested rice. Twenty experimental runs were conducted to investigate the experimental performance of the dryer for drying of rice. An independent multilayer neural network approach was used to predict the performance of the BAU-STR dryer for drying of rice. Ten sets of experimental data were used for training using back propagation algorithm and another ten sets of data were used for testing the artificial neural network model. The prediction of the performance of the dryer was found to be excellent after it was adequately trained. The statistical analysis showed that the errors (MSE and RMSE) were within and acceptable range of ±5% with a coefficient of determination (R2) of 99%. The model can be used to predict the potential of the dryer for different locations, and can also be used in a predictive optimal control algorithm.

Nomenclature

Tm,0

Temperature at the center line of inner bin, °C.

T,top

Temperature in top layer of grain bin, °C.

T,bottom

Temperature in bottom layer of grain bin, °C.

Tm,1

Temperature in middle layer of grain bin at 0.26 m distance from the center line, °C.

Tm,2

Temperature in middle layer of grain bin at 0.32 m distance from the center line, °C.

Tm,3

Temperature in middle layer of grain bin at 0.38 m distance from the center line, °C.

Tm,4

Temperature in middle layer of grain bin at 0.44 m distance from the center line, °C.

Tm,5

Temperature in middle layer of grain bin at 0.50 m distance from the center line, °C.

M,top

moisture in top layer of grain bin, %.

M,bottom

moisture in bottom layer of grain bin, %.

Mm,1

moisture in middle layer of grain bin at 0.27 m distance from the center line, %.

Mm,2

moisture in middle layer of grain bin at 0.38 m distance from the center line, %.

Mm,3

moisture in middle layer of grain bin at 0.47 m distance from the center line, %.

hr

hour.

hp

horse power.

kg

kilogram.

wb

wet basis.

bd

dry basis.

°C

degree Celsius.

m

meter.

%

percentage.

m/s

meter per second.

Notes

Acknowledgements

This study is made possible by the support of the American People provided to the Feed the Future Innovation Lab for the Reduction Post-Harvest Loss through the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by the United States Agency for International Development (USAID) under Cooperative Agreement No. (Grant Number: UReRA 2015-05296-01-00).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

231_2018_2368_MOESM1_ESM.xlsx (15 kb)
ESM 1 (XLSX 14 kb)
231_2018_2368_MOESM2_ESM.xlsx (15 kb)
ESM 2 (XLSX 15 kb)
231_2018_2368_MOESM3_ESM.xlsx (18 kb)
ESM 3 (XLSX 17 kb)
231_2018_2368_MOESM4_ESM.xlsx (9 kb)
ESM 4 (XLSX 9 kb)
231_2018_2368_MOESM5_ESM.xlsx (9 kb)
ESM 5 (XLSX 9 kb)
231_2018_2368_MOESM6_ESM.xlsx (9 kb)
ESM 6 (XLSX 9 kb)

References

  1. 1.
    Aghbashlo M, Hosseinpour S, Mujumdar AS (2015) Application of artificial neural networks (ANNs) in drying technology: a comprehensive review. Dry Technol 33(12):1397–1462CrossRefGoogle Scholar
  2. 2.
    Anggraeni ET, Zakaria M, Ulya N, Hendrawan Y (2017) Applied back propagation neural network and machine vision for modeling and controlling turmeric (Curcuma domestica val.) drying process. Proceedings of the International Conference on Industrial Engineering and Operations Management. Rabat, Morocco. April 11–13, 2017Google Scholar
  3. 3.
    Assidjo E, Yao B, Kisselmina K, Amane D (2008) Modeling of an industrial drying process by artificial neural networks. Braz J Chem Eng 25(3):515–522CrossRefGoogle Scholar
  4. 4.
    Bala BK (2017) Drying and storage of cereal grains, 2nd edn. Wiley and Sons, UKGoogle Scholar
  5. 5.
    Bala BK, Ashraf MA, Uddin MA, Janjai S (2005) Experimental and neural network prediction of the performance of solar tunnel drier for drying jackfruit bulbs and leather. J Food Process Eng 28:552–556CrossRefGoogle Scholar
  6. 6.
    Bala BK, Woods JL (1984) Simulation of deep bed malt drying. J Agric Eng Res 30:235–244CrossRefGoogle Scholar
  7. 7.
    Baughman DR, Liu YA (1995) Neural networks in bio-processing and chemical engineering. Academic Press, New YorkGoogle Scholar
  8. 8.
    Behroozi-Khazaei N, Nasirahmadi A (2017) A neural network based model to analyze rice parboiling process with small dataset. J Food Sci Technol 54(8):2562–2569CrossRefGoogle Scholar
  9. 9.
    Bishop CM (1996) Neural networks for pattern recognition. Clarendon Press, Oxford, UKzbMATHGoogle Scholar
  10. 10.
    Cakmak G, Yildis C (2011) The prediction of seedy grape drying rate using a neural network method. Comput Electron Agric 75:132–138CrossRefGoogle Scholar
  11. 11.
    Chegini GR, Khazaei J, Ghobadian B, Goudarzi AM (2008) Prediction of process and product parameters in an orange juice spray dryer using artificial neural networks. J Food Eng 84:534–543CrossRefGoogle Scholar
  12. 12.
    Chen CR, Ramaswamy HS (2002) Modeling and optimization of variable retort temperature (VRT) thermal processing using coupled neural networks and geneti algorithms. J Food Eng 28:552–566Google Scholar
  13. 13.
    Coit DW, Jackson BT, Smith AE (1998) Static neural network process models: considerations and case studies. Int J Prod Res 36(1):2953–2967CrossRefGoogle Scholar
  14. 14.
    Erenturk K, Erenturk S (2007) Comparison of genetic algorithm and neural network approaches for drying process of carrot. J Food Eng 78:905–912CrossRefGoogle Scholar
  15. 15.
    Erenturk K, Erenturk S, Tabil LG (2004) A comparative study for the estimation of dynamical drying behavior of Echinacea angustifolia: regression analysis and neural network. Comput Electron Agric 45:71–90CrossRefGoogle Scholar
  16. 16.
    Erzin Y, Rao HB, Singh DN (2008) Artificial neural network models for predicting of thermal resistivity. Int J Therm Sci 47:1347–1358CrossRefGoogle Scholar
  17. 17.
    Farkas I, Reményi P, Biro A (2000) Modelling aspects of grain drying with a neural network. Comput Electron Agric 29:99–113CrossRefGoogle Scholar
  18. 18.
    Guine RPF, Cruz AC, Mendes M (2014) Convective drying of apples: kinetic study, evaluation of mass transfer properties and data analysis using artificial neural networks. Int J Food Eng 10(2):281–290CrossRefGoogle Scholar
  19. 19.
    Hecht-Nielsen R (1989) Theory of back propagation neural network. In Proceeding of International Joint Conference on neural Networks Washington DC, 593–605Google Scholar
  20. 20.
    Hernandez JA (2009) Optimum operating conditions for heat and mass transfer in foodstuffs drying by means of neural network inverse. Food Control 20(4):435–438CrossRefGoogle Scholar
  21. 21.
    Hernández-Pérez JA, Garcia-Alvarado MA, Trystram G, Heyd B (2004) Neural networks for the heat and mass transfer prediction during drying of cassava and mango. Inn Food Sci Emerg Technol 5:57–64CrossRefGoogle Scholar
  22. 22.
    Hossain MA, Woods JL, Bala BK (2005) Simulation of solar drying of chilli in solar tunnel drier. Int J Sustain Energ 24(3):142–153CrossRefGoogle Scholar
  23. 23.
    Huang B, Mujumdar AS (1993) Use of neural network to predict industrial dryer performance. Dry Technol 11:525–541CrossRefGoogle Scholar
  24. 24.
    Hussain MA, Rahman MS, Ng CW (2002) Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network. J Food Eng 51:239–248CrossRefGoogle Scholar
  25. 25.
    Izadifar M, Jahromi MZ (2007) Application of genetic algorithm for optimization of vegetable oil hydrogenation process. J Food Eng 78:1–8CrossRefGoogle Scholar
  26. 26.
    Kaminisky W, Strumillo P, Tomczak E (1998) Neural computing approaches to modeling of drying process dynamics. Dry Technol 16:967–992CrossRefGoogle Scholar
  27. 27.
    Khazaei J, Naghavi M, Jahansouz M, Salimi-Khorshidi G (2008) Yield estimation and clustering of chickpea genotypes using soft computing techniques. Agron J 100:1077–1087CrossRefGoogle Scholar
  28. 28.
    Khazaei NB, Tavakoli T, Ghassemian H, Khoshtaghaza MH, Banakar A (2013) Applied machine vision and artificial neural network for modeling and controlling of the grape drying process. Comput Electron Agric 98:205–213CrossRefGoogle Scholar
  29. 29.
    Khoshhal A, Dakhel AA, Etemadi A, Zereshki S (2010) Artificial neural network modeling of apple drying process. J Food Eng 33(s1):298–313CrossRefGoogle Scholar
  30. 30.
    Mansouri A, Fadavi A, Mortazavian SMM (2016) An artificial intelligence approach for modeling volume and fresh weight of callus–a case study of cumin (Cuminum cyminumL.). J Theor Biol 397:199–205CrossRefGoogle Scholar
  31. 31.
    Mert I, Arat HT (2012) Prediction of heat transfer coefficients by ANN for Aluminum & Steel material. Int J Sci Knowledge 5(2):53–63Google Scholar
  32. 32.
    Momenzadeh L, Zomorodian A, Mowla D (2012) Applying artificial neural network for drying time prediction of green pea in a microwave assisted fluidized bed dryer. J Agric Sci Technol 14:513–522Google Scholar
  33. 33.
    Movagharnejad K, Nikzad M (2007) Modeling of tomato drying using artificial neural network. Comput Electron Agr 59:78–85CrossRefGoogle Scholar
  34. 34.
    Nadian MH, Rafiee S, Aghbashlo M, Hosseinpour S, Mohtasebi SS (2015) Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying. Food Bioprod Process 94:263–274CrossRefGoogle Scholar
  35. 35.
    O’Callaghan JRO, Menzies DJ, Bailey PH (1971) Digital simulation of agricultural drier performance. J Agric Eng Res 16(3):223–244CrossRefGoogle Scholar
  36. 36.
    Ratti C, Mujumdar AS (1997) Solar drying of foods: modeling and numerical simulation. Sol Energy 60:151–157CrossRefGoogle Scholar
  37. 37.
    Sander A, Skansi D, Bolf N (2003) Heat and mass transfer models in convection drying of clay slabs. Ceram Int 29(6):641–653CrossRefGoogle Scholar
  38. 38.
    Satish S, Setty PY (2004) Modeling of a continuous fluidized bed dryer using artificial neural networks. Int Commun Heat Mass Transfer 32:539–547CrossRefGoogle Scholar
  39. 39.
    Tohidi M, Sadeghi M, Mousavi SR, Mireei SA (2012) Artificial neural network modeling of process and product indices in deep bed drying of rough rice. Turk J Agric For 36:738–748Google Scholar
  40. 40.
    Trelea IC, Courtois F, Trystram G (1997) Dynamic models for drying and wet milling quality degradation of corn using neural networks. Dry Technol 15:1095–1102CrossRefGoogle Scholar
  41. 41.
    Tripathy PP, Kumar S (2008) Neural network approach for food temperature prediction during solar drying. Int J Thermal Sci 48:1452–1459CrossRefGoogle Scholar
  42. 42.
    Wasserman PD (1989) Neural computation, theory and practice. Van Nostrand Reinhold, New York, NYGoogle Scholar
  43. 43.
    Wen L, Yang B, Cui C, You L, Zhao M (2012) Ultrasound-assisted extraction of phenolics from longan (Dimocarpus longan Lour.) fruit seed with artificial neural network and their antioxidant activity. Food Anal Methods 5(6):1244–1251CrossRefGoogle Scholar
  44. 44.
    Zhang QS, Yang X, Mittal GS, Yi S (2002) Prediction of performance indices and optimal parameters of rough rice drying using neural networks. Biosyst Eng 83:281–290CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Md. Ashraful Alam
    • 1
    • 2
  • Chayan Kumer Saha
    • 1
  • Md. Monjurul Alam
    • 1
  • Md. Ali Ashraf
    • 3
  • Bilash Kanti Bala
    • 4
  • Jagger Harvey
    • 5
  1. 1.Department of Farm Power and MachineryBangladesh Agricultural University (BAU)MymensinghBangladesh
  2. 2.Farm Machinery and Post-harvest Technology DivisionBangladesh Rice Research Institute (BRRI)GazipurBangladesh
  3. 3.Department of Farm Structure and Environmental EngineeringBangladesh Agricultural University (BAU)MymensinghBangladesh
  4. 4.Department of Electrical and Electronic EngineeringBangabandhu Sheikh Mujibur Rahman Science and Technology UniversityGopalganjBangladesh
  5. 5.Feed the Future Innovation Lab for the Reduction of Post-Harvest LossKansas State UniversityManhattanUSA

Personalised recommendations