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Modelling of a continuous veneer drying unit of industrial scale and model-based ANOVA of the energy efficiency

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  • Gradov, Dmitry Vladimirovich
  • Yusuf, Yusuf Oluwatoki
  • Ohjainen, Jussi
  • Suuronen, Jarkko
  • Eskola, Roope
  • Roininen, Lassi
  • Koiranen, Tuomas

Abstract

Drying, a crucial step in process engineering aimed at producing optimal product moisture content, has evolved over time from batch processing methods to continuous processing alternatives. Continuous drying methods offer uniform moisture content of the product at lower operational cost. In this study, a continuous veneer drying model was developed based on mass and energy balances. The simulated veneer dryer is a semiautomatic machine designed to maximise the drying process efficiency via control mechanisms such as the veneer transport rate, fan speed, opening angle of the inlet and outlet dampers, and radiator temperature. In the dryer, veneer plates are conveyed horizontally through the number of connected chambers where hot air is blown transversely. The optimal drying process is dynamically maintained via the Proportional–integral–derivative controllers, manipulating the rate of the damper lids opening, that are connected to the sensors monitoring the air properties in the chambers of the drying unit. The model-based sensitivity analysis ANOVA was carried out for energy optimisation purposes. The analysis outcomes indicated that radiator temperature, initial moisture content of veneer sheets and conveyor speed are the most influential parameters affecting the drying rate. Automatic control of damper lids provides optimal temperature and moisture content of drying environment at lower energy costs.

Suggested Citation

  • Gradov, Dmitry Vladimirovich & Yusuf, Yusuf Oluwatoki & Ohjainen, Jussi & Suuronen, Jarkko & Eskola, Roope & Roininen, Lassi & Koiranen, Tuomas, 2022. "Modelling of a continuous veneer drying unit of industrial scale and model-based ANOVA of the energy efficiency," Energy, Elsevier, vol. 244(PA).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pa:s0360544221029224
    DOI: 10.1016/j.energy.2021.122673
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    References listed on IDEAS

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    1. Kleijnen, J.P.C., 1995. "Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments," Other publications TiSEM 87ee6ee0-592c-4204-ac50-6, Tilburg University, School of Economics and Management.
    2. Kleijnen, Jack P. C., 2005. "An overview of the design and analysis of simulation experiments for sensitivity analysis," European Journal of Operational Research, Elsevier, vol. 164(2), pages 287-300, July.
    3. Juan Zhang & Junping Yin & Ruili Wang, 2020. "Basic Framework and Main Methods of Uncertainty Quantification," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-18, August.
    4. Laurijssen, Jobien & De Gram, Frans J. & Worrell, Ernst & Faaij, Andre, 2010. "Optimizing the energy efficiency of conventional multi-cylinder dryers in the paper industry," Energy, Elsevier, vol. 35(9), pages 3738-3750.
    5. Euh, Seung Hee & Choi, Yun Sung & Nam, Yun Sung & Lee, Chung Gun & Lee, Sang Yeol & Oh, Kwang Cheol & Oh, Jae Heun & Kim, Dae Hyun, 2018. "Development of a real-time drying control system for a pneumatic conveying dryer for sawdust in pellet production," Energy, Elsevier, vol. 161(C), pages 10-16.
    6. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    7. Johnsson, Simon & Andersson, Elias & Thollander, Patrik & Karlsson, Magnus, 2019. "Energy savings and greenhouse gas mitigation potential in the Swedish wood industry," Energy, Elsevier, vol. 187(C).
    8. Di Marco, Paolo & Frigo, Stefano & Gabbrielli, Roberto & Pecchia, Stefano, 2016. "Mathematical modelling and energy performance assessment of air impingement drying systems for the production of tissue paper," Energy, Elsevier, vol. 114(C), pages 201-213.
    9. David C. Cox & Paul Baybutt, 1981. "Methods for Uncertainty Analysis: A Comparative Survey," Risk Analysis, John Wiley & Sons, vol. 1(4), pages 251-258, December.
    10. Gluesenkamp, Kyle R. & Boudreaux, Philip & Patel, Viral K. & Goodman, Dakota & Shen, Bo, 2019. "An efficient correlation for heat and mass transfer effectiveness in tumble-type clothes dryer drums," Energy, Elsevier, vol. 172(C), pages 1225-1242.
    11. Lamrani, Bilal & Kuznik, Frédéric & Ajbar, Abdelhamid & Boumaza, Mourad, 2021. "Energy analysis and economic feasibility of wood dryers integrated with heat recovery unit and solar air heaters in cold and hot climates," Energy, Elsevier, vol. 228(C).
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    1. Li, Mengjie & Liu, Ming & Xu, Can & Wang, Jinshi & Yan, Junjie, 2023. "Thermodynamic and sensitivity analyses on drying subprocesses of various evaporative dryers: A comparative study," Energy, Elsevier, vol. 284(C).

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