Advertisement

Prediction of corn drying performance for a combined IRC dryer with a genetically-optimized SVR algorithm

  • Aini Dai
  • Xiaoguang ZhouEmail author
  • Zidan WuEmail author
Special Issue

Abstract

Grain drying process is a complex nonlinear system which is characterized by long delay process, multi disturbance and strong coupling. In order to explore the modelling of an uncertain system, such as those used in grain drying, and to study the application of the support vector regress algorithm, a corn drying process conducted in a side-heat Infrared Radiation and Convection dryer was modelled by using a support vector regress algorithm combined with a genetic algorithm which is abbreviated as GA-SVR. The algorithm was trained by using the input and output data collected from the practical experiment of corn drying. The predicted performance comparisons between the GA-SVR modelling method and the other two modelling methods (the neural network of BP model and the SVR model based on the grid search algorithm) were also made. Moreover, we successfully used the method to design a model of concurrent-counter flow drying. The designed GA-SVR model has achieved higher modelling prediction accuracy according to the prediction results which have verified the feasibility of the proposed modelling algorithm for modelling the grain drying. The modelling method can also realize the performance prediction of different drying techniques and can be applied in the model prediction control of the grain drying.

Keywords

Infrared radiation drying Grain drying Support vector regression Genetic algorithm Modelling 

Abbreviations

GA

Genetic algorithm

SVR

Support vector regress

GA-SVR

SVR algorithm combined with a genetic algorithm

GS-SVR

The SVR model by using a grid search method

IRC

Infrared radiation and convection

ANN-BP

The artificial neural network model of back propagation

GS-SVR

The SVR model by using a grid search method

PDE

Partial differential equation

DP

Distributed parameter

ANN

Artificial neural network

ML

Machine learning

SVM

Support vector machine

SVC

Support vector classifier

wb

Wet basis

MC

Moisture content

RBF

Radial basis function

KKT

Karush–Kuhn–Tucker

GS-CV

Grid Search method based on a cross-validation

MSE

Mean squared error

RMSE

Root mean squared error

Notes

Acknowledgements

The authors of this paper would like to thank the support of Qingdao Agricultural University High-level Talents Research Fund (1118037), and the support of National Key Research and Development Program of China under Grant (2016YFC0803206).

References

  1. 1.
    Junfu L (2006) Present status and strategies of grain drying machinery in China. J Agric Mech Res 9:44–46Google Scholar
  2. 2.
    Lutfy OF, Selamat H, Noor SBM (2015) Intelligent modelling and control of a conveyor belt grain dryer using a simplified type 2 neuro-fuzzy controller. Dry Technol 33(10):1210–1222CrossRefGoogle Scholar
  3. 3.
    Kumar C, Joardder MUH, Farrell TW et al (2016) Mathematical model for intermittent microwave convective drying of food materials. Dry Technol 34(8):962–973CrossRefGoogle Scholar
  4. 4.
    Riadh MH, Ahmad SAB, Marhaban MH et al (2015) Infrared heating in food drying: an overview. Dry Technol 33(3):322–335CrossRefGoogle Scholar
  5. 5.
    Mujumdar AS (1995) Handbook of industrial drying, revised and expanded, vol 1, 2nd edn. Marcel Dekker, Inc., New York, pp 7–424Google Scholar
  6. 6.
    Rarità L, Piccoli B, Marigo A, Cascone A (2010) Decentralized optimal routing for packets flow on data networks. Discret Contin Dyn Syst Ser B DCDS-B 13(1):59–78MathSciNetzbMATHGoogle Scholar
  7. 7.
    Cutolo A, Piccoli B, Rarità L (2011) An Upwind-Euler scheme for an ODE-PDE model of supply chains. SIAM J Comput 33(4):1669–1688MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Liu Q, Bakker-Arkoma FW (2001) A model-predictive controller for grain drying. J Food Eng 49:321–326CrossRefGoogle Scholar
  9. 9.
    Das I, Das SK, Bal S (2004) Drying performance of a batch type vibration aided infrared dryer. J Food Eng 64(1):129–133CrossRefGoogle Scholar
  10. 10.
    Wang J (2002) A single-layer model for far-infrared radiation drying of onion slices. Dry Technol 20(10):1941–1953CrossRefGoogle Scholar
  11. 11.
    Wu Z, Li H, Luo Y et al (2014) Drying characteristics of tremella fuciformis under infrared ray and its kinetics model. Nat Prod Res Dev 26(4):471–474, 503Google Scholar
  12. 12.
    Lin X, Wang XY (2010) Modelling and evaluation of infrared radiation drying for apple slices. Trans Chin Soc Agric Mach 41(6):128–132Google Scholar
  13. 13.
    Zhang L, Wang XY, Wei ZC et al (2016) Structural properties research of infrared radiation drying for carrot slices. Trans Chin Soc Agric Mach 47(7):246–251Google Scholar
  14. 14.
    Thaker KS (2007) A diffusion model for a drum dryer subjected to conduction, convection, and radiant heat input. Dry Technol 25(6):1033–1043CrossRefGoogle Scholar
  15. 15.
    Ranjan R, Irudayaraj J, Jun S (2002) Simulation of infrared drying process. Dry Technol 20(2):363–379CrossRefGoogle Scholar
  16. 16.
    Afzal TM, Abe T (1999) Some fundamental attributes of far infrared radiation drying of potato. Dry Technol 17(1–2):138–155CrossRefGoogle Scholar
  17. 17.
    Markku JL, Ojala Kapi T, Esai K (1991) Modelling and measurements of infrared dryers for coated paper. Dry Technol 9(4):973–1017CrossRefGoogle Scholar
  18. 18.
    Dhib R (2007) Infrared drying: from process modelling to advanced process control. Dry Technol 25(1):97–105CrossRefGoogle Scholar
  19. 19.
    Charun L, Athapol N (2011) Effects of simultaneous parboiling and drying by infrared radiation heating on parboiled rice quality. Dry Technol 29(9):1066–1075CrossRefGoogle Scholar
  20. 20.
    Farkas I, Remenyi P, Biro A (2000) Modelling aspects of grain drying with a neural network. Comput Electron Agric 29(1–2):99–113CrossRefGoogle Scholar
  21. 21.
    Movagharnejad K, Nikzad M (2007) Modelling of tomato drying using artificial neural network. Comput Electron Agric 59(1–2):78–85CrossRefGoogle Scholar
  22. 22.
    Çakmak G, Yıldız C (2011) The prediction of seedy grape drying rate using a neural network method. Comput Electron Agricul 75(1):132–138CrossRefGoogle Scholar
  23. 23.
    Patil AP, Deka PC (2016) An extreme learning machine approach for modelling evapotranspiration using extrinsic inputs. Comput Electron Agric 121:385–392CrossRefGoogle Scholar
  24. 24.
    Colman E, Waegeman W, De Baets B et al (2015) Prediction of subacute ruminal acidosis based on milk fatty acids. Comput Electron Agric 111(C):179–185CrossRefGoogle Scholar
  25. 25.
    Hou XR, Zou ZJ (2016) Parameter identification of nonlinear roll motion equation for floating structures in irregular waves. Appl Ocean Res 55:66–75CrossRefGoogle Scholar
  26. 26.
    Gaeta M, Loia V, Tomasiello S, Tomasiello S (2013) A generalized functional network for a classifier quantifiers scheme in a gas-sensing system. Int J Intell Syst 28(10):988–1009CrossRefGoogle Scholar
  27. 27.
    Liu B, Huang S, Wu R, Fu P (2020) Implementation method of SVR algorithm in resource-constrained platform. In: Pan JS, Li J, Tsai PW, Jain L (eds) Advances in intelligent information hiding and multimedia signal processing. Smart innovation, systems and technologies, vol 157. Springer, Singapore, pp 85–93Google Scholar
  28. 28.
    Zhang Y, Li Q (2020) A regressive convolution neural network and support vector regression model for electricity consumption forecasting. In: Arai K, Bhatia R (eds) Advances in information and communication. FICC 2019. Lecture notes in networks and systems, vol 70. Springer, ChamGoogle Scholar
  29. 29.
    Alonso J, Bahamonde A (2013) Support vector regression to predict carcass weight in beef cattle in advance of the slaughter. Comput Electron Agric 91(2):116–120CrossRefGoogle Scholar
  30. 30.
    Liu N, Cui X, Bryant DM et al (2015) Inferring deep-brain activity from cortical activity using functional near-infrared spectroscopy. Biomed Opt Express 6(3):1074–1089CrossRefGoogle Scholar
  31. 31.
    Jiang ZB, Yang Q (2016) A discrete fruit fly optimization algorithm for the traveling salesman problem. PLoS ONE 11(11):e0165804.  https://doi.org/10.1371/journal.pone.0165804 CrossRefGoogle Scholar
  32. 32.
    Ghasemi E, Kalhori H, Bagherpour R (2016) A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting. Eng Comput 32:1–8CrossRefGoogle Scholar
  33. 33.
    Tan P, Zhang C, Xia J, Fang QY, Chen G (2015) Estimation of higher heating value of coal based on proximate analysis using support vector regression. Fuel Process Tech 138:298–304CrossRefGoogle Scholar
  34. 34.
    Rajaee T, Boroumand A (2015) Forecasting of chlorophyll-a concentrations in South San Francisco Bay using five different models. Appl Ocean Res 53:208–217CrossRefGoogle Scholar
  35. 35.
    Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222MathSciNetCrossRefGoogle Scholar
  36. 36.
    Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(10):988–999CrossRefGoogle Scholar
  37. 37.
    Li XF, Lu ZM (2016) Optimizing the controllability of arbitrary networks with genetic algorithm. Phys A 447:422–433MathSciNetzbMATHCrossRefGoogle Scholar
  38. 38.
    Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(27):1–27CrossRefGoogle Scholar
  39. 39.
    Dai A, Zhou X, Liu X et al (2017) Model of drying process for combined side-heat infrared radiation and convection grain dryer based on BP neural network. Trans Chin Soc Agric Mach 48(3):351–360Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Science and Information CollegeQingdao Agricultural UniversityQingdaoChina
  2. 2.School of Economics and ManagementMinjiang UniversityFuzhouChina
  3. 3.School of AutomationBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations