Modeling and Simulation of Biomass Drying Using Artificial Neural Networks

  • Sławomir Francik
  • Bogusława Łapczyńska-Kordon
  • Renata Francik
  • Artur Wójcik
Conference paper
Part of the Springer Proceedings in Energy book series (SPE)


Willow (Salix viminalis) is a moist material after the crops. Therefore, the content of water in this type of material has to be lowered by drying before any further mechanical or thermal processing, in order to increase its calorific value. The process of drying is energy-intensive. Thus it is advisable to search for optimal methods and parameters of drying. The optimisation requires evolving a model that is based on the crucial parameters of the process. One of the possible solutions is to apply models of Artificial Neural Networks. Artificial Neural Networks belong to the group of methods of artificial computational intelligence and are often used in modelling various phenomena and processes. The aim of this work was to develop models using Artificial Neural Networks to describe the process of convective drying of the willow woodchips. As a result of presented work we obtained neural models describing alterations of water content, changes of the temperature and the mass of the chips. The presented models are highly accurate. We used experimentally obtained data in order to validate the models. It is important to underline that the data were not applied while the artificial neural networks were being developed. Subsequently, the models were used to simulate the process of drying what allowed us to define the optimal parameters of drying willow woodchips characterised by different moisture content.


Biomass Willow (Salix viminalis) chips Drying Model Artificial neural networks 



This research was financed by the Ministry of Science and Higher Education of the Republic of Poland (statutory activities DS-3600/WIPiE/2017, Faculty of Production and Power Engineering, University of Agriculture in Krakow).


  1. 1.
    Ellabban, O., Abu-Rub, H., Blaabjerg, F.: Renewable energy resources: current status, future prospects and their enabling technology. Renew. Sustain. Energy Rev. 39, 748–764 (2014)CrossRefGoogle Scholar
  2. 2.
    Kubica, K., Jewiarz, M., Kubica, R., Szlȩk, A.: Straw combustion: pilot and laboratory studies on a straw-fired grate boiler. Energy Fuels 30(6), 4405–4410 (2016)CrossRefGoogle Scholar
  3. 3.
    Wrobel, M., Fraczek, J., Francik, S., Slipek, Z., Mudryk, K.: Influence of degree of fragmentation on chosen quality parameters of briquette made from biomass of cup plant Silphium perfoliatum L. In: Engineering for Rural Development (2013), pp. 653–657Google Scholar
  4. 4.
    Mudryk, K., Fraczek, J., Slipek, Z., Francik, S., Wrobel, M.: Chosen physico-mechanical properties of cutleaf coneflower (Rudbeckia laciniata L.) shoots. In: Engineering for Rural Development (2013), pp. 658–662Google Scholar
  5. 5.
    Ivanova, T., Kolarikova, M., Havrland, B., Passian, L.: Mechanical durability of briquettes made of energy crops and wood residues. In: Engineering for Rural Development, vol. 13 (2014), pp. 131–136Google Scholar
  6. 6.
    Kolarikova, M., Ivanova, T., Havrland, B.: Energy balance of briquettes made of hemp (Cannabis sativa L.) cultivars (Ferimon, Bialobrzeskie) from autumn harvest to produce heat for household use. In: Engineering for Rural Development, vol. 2 (2013), pp. 504–508Google Scholar
  7. 7.
    Swietochowski, A., Lisowski, A., Dabrowska-Salwin, M.: Strength of briquettes and pellets from energy crops. In: Engineering for Rural Development (2016), pp. 547–551Google Scholar
  8. 8.
    Gigler, J.K., van Loon, W.K.P., Seres, I., Meerdink, G., Coumans, W.J.: Drying characteristics of willow chips and stems. J. Agric. Eng. Res. 77(4), 391–400 (2000)CrossRefGoogle Scholar
  9. 9.
    Gigler, J.K., Van Loon, W.K.P., Vissers, M.M., Bot, G.P.A.: Forced convective drying of willow chips. Biomass Bioenerg. 19(4), 259–270 (2000)CrossRefGoogle Scholar
  10. 10.
    De Fusco, L., Jeanmart, H., Blondeau, J.: A modelling approach for the assessment of an air-dryer economic feasibility for small-scale biomass steam boilers. Fuel Process. Technol. 134, 251–258 (2015)CrossRefGoogle Scholar
  11. 11.
    Iqbal, M., Azam, M., Naeem, M., Khwaja, A.S., Anpalagan, A.: Optimization classification, algorithms and tools for renewable energy: a review. Renew. Sustain. Energy Rev. 39, 640–654 (2014)CrossRefGoogle Scholar
  12. 12.
    Jirjis, R.: Storage and drying of woodfuel. Biomass Bioenerg. 9(1–5), 181–190 (1995)CrossRefGoogle Scholar
  13. 13.
    Johansson, A., Fyhr, C., Rasmuson, A.: High temperature convective drying of wood chips with air and superheated steam. Int. J. Heat Mass Transf. 40(12), 2843–2858 (1997)CrossRefGoogle Scholar
  14. 14.
    Le Lostec, B., Galanis, N., Baribeault, J., Millette, J.: Wood chip drying with an absorption heat pump. Energy 33(3), 500–512 (2008)CrossRefGoogle Scholar
  15. 15.
    Peters, B., Bruch, C.: Drying and pyrolysis of wood particles: experiments and simulation. J. Anal. Appl. Pyrol. 70, 233–250 (2003)CrossRefGoogle Scholar
  16. 16.
    Gebreegziabher, T., Oyedun, A.O., Hui, C.W.: Optimum biomass drying for combustion—a modeling approach. Energy 53, 67–73 (2013)CrossRefGoogle Scholar
  17. 17.
    Sridhar, D., Madhu, G.M.: Drying kinetics and mathematical modeling of Casuarina equisetifolia wood chips at various temperatures. Periodica Polytech. Chem. Eng. 59(4), 288–295 (2015)CrossRefGoogle Scholar
  18. 18.
    Gigler, J.K., Van Loon, W.K.P., Sonneveld, C.: Experiment and modelling of parameters influencing natural wind drying of willow chunks. Biomass Bioenerg. 26(6), 507–514 (2004)CrossRefGoogle Scholar
  19. 19.
    Pakowski, Z., Adamski, R., Kokocinska, M.: Cross-fiber dry wood darcy permeability of energetic willow Salix viminalis v. Orm. Drying Technol. 27(12), 1379–1383 (2009)CrossRefGoogle Scholar
  20. 20.
    Pakowski, Z., Krupinska, B., Adamski, R.: Prediction of sorption equilibrium both in air and superheated steam drying of energetic variety of willow Salix viminalis in a wide temperature range. Fuel 86(12–13), 1749–1757 (2007)CrossRefGoogle Scholar
  21. 21.
    Francik, S., Ślipek, Z., Frączek, J., Knapczyk, A.: Present trends in research on application of artificial neural networks in agricultural engineering. Agric. Eng. 20(4), 15–25 (2016)Google Scholar
  22. 22.
    Farkas, I., Reményi, P., Biró, A.: A neural network topology for modelling grain drying. Comput. Electron. Agric. 26(2), 147–158 (2000)CrossRefGoogle Scholar
  23. 23.
    Khazaei, J., Daneshmandi, S.: Modeling of thin-layer drying kinetics of sesame seeds: mathematical and neural networks modeling. Int. Agrophys. 21, 335–348 (2007)Google Scholar
  24. 24.
    Gorjian, S., Tavakkoli Hashjin, T., Khoshtaghaza, M.H.: Designing and optimizing a back propagation neural network to model a thin-layer drying process. Int. Agrophys. 25, 13–19 (2011)Google Scholar
  25. 25.
    Khoshhal, A., Dakhel, A.A., Etemadi, A., Zereshki, S.: Artificial neural network modeling of apple drying process. J. Food Process Eng. 33(Suppl. 1), 298–313 (2010)CrossRefGoogle Scholar
  26. 26.
    Jafari, S.M., Ganje, M., Dehnad, D., Ghanbari, V.: Mathematical, fuzzy logic and artificial neural network modeling techniques to predict drying kinetics of onion. J. Food Process. Preserv. 40(2), 329–339 (2016)CrossRefGoogle Scholar
  27. 27.
    Assidjo, E., Yao, B., Kisselmina, K., Amané, D.: Modeling of an industrial drying process by artificial neural networks. Braz. J. Chem. Eng. 25(3), 515–522 (2008)CrossRefGoogle Scholar
  28. 28.
    Ge, L., Chen, G.S.: Control modeling of ash wood drying using process neural networks. Optik 125(22), 6770–6774 (2014)CrossRefGoogle Scholar
  29. 29.
    Ozsahin, S., Aydin, I.: Prediction of the optimum veneer drying temperature for good bonding in plywood manufacturing by means of artificial neural network. Wood Sci. Technol. 48(1), 59–70 (2014)CrossRefGoogle Scholar
  30. 30.
    Watanabe, K., Matsushita, Y., Kobayashi, I., Kuroda, N.: Artificial neural network modeling for predicting final moisture content of individual Sugi (Cryptomeria japonica) samples during air-drying. J. Wood Sci. 59(2), 112–118 (2013)CrossRefGoogle Scholar
  31. 31.
    Mudryk, K., Francik, S., Fraczek, J., Slipek, Z., Wrobel, M.: Model of actual contact area of rye and wheat grains with flat surface. In: Renewable and Sustainable Energy Reviews (2013), pp. 292–296Google Scholar
  32. 32.
    Fraczek, J., Francik, S., Slipek, Z., Knapczyk, A.: Application of artificial neural networks in modelling the contact area of grain seeds. Agric. Eng. 20(4), 27–37 (2016)Google Scholar
  33. 33.
    Łapczyńska-Kordon, B., Francik, S., Frączek, J., Ślipek, Z.: Modeling drying shrinkage for selected root vegetables using neural networks (in Polish). Inżynieria Rolnicza/Agric. Eng. 13, 303–311 (2006)Google Scholar
  34. 34.
    Łapczyńska-Kordon, B., Francik, S.: Neural model of changes in water content in willow chips during convection drying (in Polish). Inżynieria Rolnicza/Agric. Eng. 11(109), 143–148 (2008)Google Scholar
  35. 35.
    Łapczyńska-Kordon, B., Francik, S., Ślipek, Z.: Neural model of temperature changes during convection drying of energy willow chips (in Polish). Inżynieria Rolnicza/Agric. Eng. 11(109), 149–155 (2008)Google Scholar
  36. 36.
    Zlobecki, A., Francik, S.: Defining the damaging process of cereal grains on the basis of artificial neural network. Int. Agrophys. 15, 219–223 (2001)Google Scholar
  37. 37.
    Francik, S., Fraczek, J.: Model development of the external friction of granular vegetable materials on the basis of artificial neural networks. Int. Agrophys. 15, 231–236 (2001)Google Scholar
  38. 38.
    Wrobel, M., Fraczek, J., Francik, S., Slipek, Z., Krzysztof, M.: Modelling of unit contact surface of bean seeds using Artificial Neural Networks. In: Renewable and Sustainable Energy Reviews (2013), pp. 287–291Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Sławomir Francik
    • 1
  • Bogusława Łapczyńska-Kordon
    • 1
  • Renata Francik
    • 2
  • Artur Wójcik
    • 1
  1. 1.Department of Mechanical Engineering and Agrophysics, Faculty of Production and Power EngineeringUniversity of Agriculture in KrakowKrakówPoland
  2. 2.Chair of Organic Chemistry, Department of Bioorganic ChemistryJagiellonian University Medical CollegeKrakowPoland

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