IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i6p1364-d332808.html
   My bibliography  Save this article

Decision Support System for the Production of Miscanthus and Willow Briquettes

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/6/1364/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/6/1364/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shuren Chen & Yunfei Zhao & Zhong Tang & Hantao Ding & Zhan Su & Zhao Ding, 2022. "Structural Model of Straw Briquetting Machine with Vertical Ring Die and Optimization of Briquetting Performance," Agriculture, MDPI, vol. 12(5), pages 1-15, May.
    2. Marcin Jewiarz & Marek Wróbel & Krzysztof Mudryk & Szymon Szufa, 2020. "Impact of the Drying Temperature and Grinding Technique on Biomass Grindability," Energies, MDPI, vol. 13(13), pages 1-22, July.
    3. Krzysztof Mudryk & Marcin Jewiarz & Marek Wróbel & Marcin Niemiec & Arkadiusz Dyjakon, 2021. "Evaluation of Urban Tree Leaf Biomass-Potential, Physico-Mechanical and Chemical Parameters of Raw Material and Solid Biofuel," Energies, MDPI, vol. 14(4), pages 1-14, February.
    4. Abdulilah Mohammad Mayet & Seyed Mehdi Alizadeh & Karina Shamilyevna Nurgalieva & Robert Hanus & Ehsan Nazemi & Igor M. Narozhnyy, 2022. "Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems," Energies, MDPI, vol. 15(6), pages 1-19, March.
    5. Sławomir Francik & Bogusława Łapczyńska-Kordon & Norbert Pedryc & Wojciech Szewczyk & Renata Francik & Zbigniew Ślipek, 2022. "The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus," Sustainability, MDPI, vol. 14(5), pages 1-26, March.
    6. Abdullah K. Alanazi & Seyed Mehdi Alizadeh & Karina Shamilyevna Nurgalieva & John William Grimaldo Guerrero & Hala M. Abo-Dief & Ehsan Eftekhari-Zadeh & Ehsan Nazemi & Igor M. Narozhnyy, 2021. "Optimization of X-ray Tube Voltage to Improve the Precision of Two Phase Flow Meters Used in Petroleum Industry," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    7. Hívila M. P. Marreiro & Rogério S. Peruchi & Riuzuani M. B. P. Lopes & Silvia L. F. Andersen & Sayonara A. Eliziário & Paulo Rotella Junior, 2021. "Empirical Studies on Biomass Briquette Production: A Literature Review," Energies, MDPI, vol. 14(24), pages 1-40, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sławomir Francik & Bogusława Łapczyńska-Kordon & Norbert Pedryc & Wojciech Szewczyk & Renata Francik & Zbigniew Ślipek, 2022. "The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus," Sustainability, MDPI, vol. 14(5), pages 1-26, March.
    2. Adrian Knapczyk & Sławomir Francik & Marcin Jewiarz & Agnieszka Zawiślak & Renata Francik, 2020. "Thermal Treatment of Biomass: A Bibliometric Analysis—The Torrefaction Case," Energies, MDPI, vol. 14(1), pages 1-31, December.
    3. Alexey I. Shinkevich & Tatiana V. Malysheva & Yulia V. Vertakova & Vladimir A. Plotnikov, 2021. "Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling," Mathematics, MDPI, vol. 9(4), pages 1-20, February.
    4. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    5. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    6. Hívila M. P. Marreiro & Rogério S. Peruchi & Riuzuani M. B. P. Lopes & Silvia L. F. Andersen & Sayonara A. Eliziário & Paulo Rotella Junior, 2021. "Empirical Studies on Biomass Briquette Production: A Literature Review," Energies, MDPI, vol. 14(24), pages 1-40, December.
    7. Wojciech Rzeźnik & Ilona Rzeźnik & Paulina Mielcarek-Bocheńska & Mateusz Urbański, 2023. "Air Pollutants Emission during Co-Combustion of Animal Manure and Wood Pellets in 15 kW Boiler," Energies, MDPI, vol. 16(18), pages 1-17, September.
    8. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    9. Andrzej Marczuk & Agata Blicharz-Kania & Petr A. Savinykh & Alexey Y. Isupov & Andrey V. Palichyn & Ilya I. Ivanov, 2019. "Studies of a Rotary–Centrifugal Grain Grinder Using a Multifactorial Experimental Design Method," Sustainability, MDPI, vol. 11(19), pages 1-11, September.
    10. Leonel Jorge Ribeiro Nunes & Radu Godina & João Carlos de Oliveira Matias, 2019. "Technological Innovation in Biomass Energy for the Sustainable Growth of Textile Industry," Sustainability, MDPI, vol. 11(2), pages 1-12, January.
    11. Ma, Chenshuo & Zhang, Yifei & Ma, Keni & Li, Chanyun, 2023. "Study on the relationship between service scale and investment cost of energy service stations," Energy, Elsevier, vol. 269(C).
    12. Finn, Thomas & McKenzie, Paul, 2020. "A high-resolution suitability index for solar farm location in complex landscapes," Renewable Energy, Elsevier, vol. 158(C), pages 520-533.
    13. Toledo, Olga Moraes & Oliveira Filho, Delly & Diniz, Antônia Sônia Alves Cardoso, 2010. "Distributed photovoltaic generation and energy storage systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 506-511, January.
    14. Zhijiang Li & Decai Tang & Mang Han & Brandon J. Bethel, 2018. "Comprehensive Evaluation of Regional Sustainable Development Based on Data Envelopment Analysis," Sustainability, MDPI, vol. 10(11), pages 1-18, October.
    15. Jan K. Kazak & Joanna A. Kamińska & Rafał Madej & Marta Bochenkiewicz, 2020. "Where Renewable Energy Sources Funds are Invested? Spatial Analysis of Energy Production Potential and Public Support," Energies, MDPI, vol. 13(21), pages 1-26, October.
    16. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    17. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    18. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    19. Zaiwu Gong & Xiaoqing Chen, 2017. "Analysis of Interval Data Envelopment Efficiency Model Considering Different Distribution Characteristics—Based on Environmental Performance Evaluation of the Manufacturing Industry," Sustainability, MDPI, vol. 9(12), pages 1-25, November.
    20. Vida Dabkienė & Tomas Baležentis & Dalia Štreimikienė, 2022. "Reconciling the micro‐ and macro‐perspective in agricultural energy efficiency analysis for sustainable development," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 149-164, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:6:p:1364-:d:332808. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.