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Decision Support System for the Production of Miscanthus and Willow Briquettes

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  • Sławomir Francik

    (Department of Mechanical Engineering and Agrophysics, University of Agriculture in Krakow, Balicka 120, 31-120 Kraków, Poland)

  • Adrian Knapczyk

    (Department of Mechanical Engineering and Agrophysics, University of Agriculture in Krakow, Balicka 120, 31-120 Kraków, Poland)

  • Artur Knapczyk

    (Private Researcher, Kurow 26, 34-233 Kurow, Poland)

  • Renata Francik

    (Institute of Health, State Higher Vocational School, Staszica 1, 33-300 Nowy Sącz, Poland)

Abstract

The biomass is regarded as a part of renewable energy sources (RES), which can satisfy energy demands. Biomass obtained from plantations is characterized by low bulk density, which increases transport and storage costs. Briquetting is a technology that relies on pressing biomass with the aim of obtaining a denser product (briquettes). In the production of solid biofuels, the technological as well as material variables significantly influence the densification process, and as a result influence the end quality of briquette. This process progresses differently for different materials. Therefore, the optimal selection of process’ parameters is very difficult. It is necessary to use a decision support tool—decision support system (DSS). The purpose of the work was to develop a decision support system that would indicate the optimal parameters for conducting the process of producing Miscanthus and willow briquettes (pre-comminution, milling and briquetting), briquette parameters (durability and specific density) and total energy consumption based on process simulation. Artificial neural networks (ANNs) were used to describe the relationship between individual parameters of the briquette production process. DSS has the form of a web application and is opened from a web browser (it is possible to open it on various types of devices). The modular design allows the modification and expansion the application in the future.

Suggested Citation

  • Sławomir Francik & Adrian Knapczyk & Artur Knapczyk & Renata Francik, 2020. "Decision Support System for the Production of Miscanthus and Willow Briquettes," Energies, MDPI, vol. 13(6), pages 1-24, March.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:6:p:1364-:d:332808
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    as
    1. Mario Martín-Gamboa & Luis C. Dias & Paula Quinteiro & Fausto Freire & Luís Arroja & Ana Cláudia Dias, 2019. "Multi-Criteria and Life Cycle Assessment of Wood-Based Bioenergy Alternatives for Residential Heating: A Sustainability Analysis," Energies, MDPI, vol. 12(22), pages 1-17, November.
    2. Iddio, E. & Wang, L. & Thomas, Y. & McMorrow, G. & Denzer, A., 2020. "Energy efficient operation and modeling for greenhouses: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    3. Hubert Byliński & Andrzej Sobecki & Jacek Gębicki, 2019. "The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process," Sustainability, MDPI, vol. 11(16), pages 1-12, August.
    4. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    5. Reynolds, Jonathan & Ahmad, Muhammad Waseem & Rezgui, Yacine & Hippolyte, Jean-Laurent, 2019. "Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 699-713.
    6. Konstantinos Ioannou & Georgios Tsantopoulos & Garyfallos Arabatzis & Zacharoula Andreopoulou & Eleni Zafeiriou, 2018. "A Spatial Decision Support System Framework for the Evaluation of Biomass Energy Production Locations: Case Study in the Regional Unit of Drama, Greece," Sustainability, MDPI, vol. 10(2), pages 1-22, February.
    7. Georgiana Moiceanu & Gigel Paraschiv & Gheorghe Voicu & Mirela Dinca & Olivia Negoita & Mihai Chitoiu & Paula Tudor, 2019. "Energy Consumption at Size Reduction of Lignocellulose Biomass for Bioenergy," Sustainability, MDPI, vol. 11(9), pages 1-12, April.
    8. Arturas Kaklauskas & Gintautas Dzemyda & Laura Tupenaite & Ihar Voitau & Olga Kurasova & Jurga Naimaviciene & Yauheni Rassokha & Loreta Kanapeckiene, 2018. "Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment," Energies, MDPI, vol. 11(8), pages 1-20, August.
    9. Jie Xu & Shiyan Chang & Zhenhong Yuan & Yang Jiang & Shuna Liu & Weizhen Li & Longlong Ma, 2015. "Regionalized Techno-Economic Assessment and Policy Analysis for Biomass Molded Fuel in China," Energies, MDPI, vol. 8(12), pages 1-18, December.
    10. Aleksandra Besser & Jan K. Kazak & Małgorzata Świąder & Szymon Szewrański, 2019. "A Customized Decision Support System for Renewable Energy Application by Housing Association," Sustainability, MDPI, vol. 11(16), pages 1-16, August.
    11. Peter Križan & Miloš Matú & Ľubomír Šooš & Juraj Beniak, 2015. "Behavior of Beech Sawdust during Densification into a Solid Biofuel," Energies, MDPI, vol. 8(7), pages 1-17, June.
    12. Yuewei Liu & Shenghui Zhang & Xuejun Chen & Jianzhou Wang, 2018. "Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting," Sustainability, MDPI, vol. 10(12), pages 1-30, December.
    13. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    14. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    15. Iulia Stamatescu & Nicoleta Arghira & Ioana Făgărăşan & Grigore Stamatescu & Sergiu Stelian Iliescu & Vasile Calofir, 2017. "Decision Support System for a Low Voltage Renewable Energy System," Energies, MDPI, vol. 10(1), pages 1-15, January.
    16. Veronika Chaloupková & Tatiana Ivanova & Ondřej Ekrt & Abraham Kabutey & David Herák, 2018. "Determination of Particle Size and Distribution through Image-Based Macroscopic Analysis of the Structure of Biomass Briquettes," Energies, MDPI, vol. 11(2), pages 1-13, February.
    17. Anna Brunerová & Hynek Roubík & Milan Brožek & David Herák & Vladimír Šleger & Jana Mazancová, 2017. "Potential of Tropical Fruit Waste Biomass for Production of Bio-Briquette Fuel: Using Indonesia as an Example," Energies, MDPI, vol. 10(12), pages 1-22, December.
    18. Arán Carrión, J. & Espín Estrella, A. & Aznar Dols, F. & Zamorano Toro, M. & Rodríguez, M. & Ramos Ridao, A., 2008. "Environmental decision-support systems for evaluating the carrying capacity of land areas: Optimal site selection for grid-connected photovoltaic power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(9), pages 2358-2380, December.
    19. Jianguo Zhou & Xiaolei Xu & Xuejing Huo & Yushuo Li, 2019. "Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
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