IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i18p11369-d911706.html
   My bibliography  Save this article

Determination of Energy Parameters and Their Variability between Varieties of Fodder and Turf Grasses

Author

Listed:
  • Łukasz Sobol

    (Department of Applied Bioeconomy, Wroclaw University of Environmental and Life Sciences, Chełmońskiego St. 37a, 51-630 Wroclaw, Poland)

  • Karol Wolski

    (Department of Agroecology and Plant Production, Wroclaw University of Environmental and Life Sciences, Grunwaldzki 24A, 50-363 Wroclaw, Poland)

  • Adam Radkowski

    (Department of Agroecology and Plant Production, University of Agriculture in Krakow, Mickiewicza 21, 31-120 Krakow, Poland)

  • Elżbieta Piwowarczyk

    (Malopolska Plant Breeding, Plant Breeding Station in Polanowice, Polanowice 122, 32-090 Slomniki, Poland)

  • Maciej Jurkowski

    (Malopolska Plant Breeding, Plant Breeding Station in Nieznanice, Klonowa 1, 42-270 Klomnice, Poland)

  • Henryk Bujak

    (Department of Genetics, Wroclaw University of Environmental and Life Sciences, Grunwaldzki 24A, 50-363 Wroclaw, Poland
    Research Centre for Cultivar Testing, Slupia Wielka 34, 63-022 Slupia Wielka, Poland)

  • Arkadiusz Dyjakon

    (Department of Applied Bioeconomy, Wroclaw University of Environmental and Life Sciences, Chełmońskiego St. 37a, 51-630 Wroclaw, Poland)

Abstract

Due to the need to diversify energy sources and transform the energy system and its decarbonization, new paths for obtaining raw materials are being sought. One of the potential options is to increase the use of grasses’ share in bioenergy production, which has a significant area potential. However, the diversified chemical composition of grasses and their anatomical heterogeneity mean that, between the various cultivars and species, the parameters determining their energetic usefulness may differ significantly, hence the key is to know the appropriate parameters at the variety level of a given species in order to effectively carry out the combustion process. In this experiment, a total of 23 varieties of seven grass species (Kentucky bluegrass ( Poa pratensis L.), Red Fescue ( Festuca rubra L.), Perennial Ryegrass ( Lolium perenne L.), Meadow Fescue ( Festuca pratensis Huds.), Timothy ( Phleum pratense L.), Common Bent ( Agrostis capillaris L.), Sheep Fescue ( Festuca ovina L.), which had not yet been evaluated in terms of energy utilization, were tested. Proximate analysis showed the average ash content was in the range of 5.73–8.31%, the content of volatile matter in the range of 70.99–82.29% and the content of fixed carbon in the range of 5.96–17.19%. Higher heating value and lower heating value of grasses ranged from 16,548–18,616 kJ∙kg −1 , 15,428–17,453 kJ∙kg −1 , respectively. The Sheep Fescue turned out to be the most useful species for combustion. It has been shown that there may be statistically significant differences in the parameters determining their combustion suitability between the various varieties of a given species of grass. Therefore the major finding of this work shows that it is necessary to need to know theparameters of a given variety is necessary to optimize the combustion process and maintain the full energy efficiency of the system (especially lower heating value).

Suggested Citation

  • Łukasz Sobol & Karol Wolski & Adam Radkowski & Elżbieta Piwowarczyk & Maciej Jurkowski & Henryk Bujak & Arkadiusz Dyjakon, 2022. "Determination of Energy Parameters and Their Variability between Varieties of Fodder and Turf Grasses," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11369-:d:911706
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/18/11369/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/18/11369/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Birka Wicke & Ingeborg Kluts & Jan Peter Lesschen, 2020. "Bioenergy Potential and Greenhouse Gas Emissions from Intensifying European Temporary Grasslands," Land, MDPI, vol. 9(11), pages 1-18, November.
    2. Jan Hari Arti Khalsa & Frank Döhling & Florian Berger, 2016. "Foliage and Grass as Fuel Pellets–Small Scale Combustion of Washed and Mechanically Leached Biomass," Energies, MDPI, vol. 9(5), pages 1-16, May.
    3. Xing, Jiangkuan & Luo, Kun & Wang, Haiou & Gao, Zhengwei & Fan, Jianren, 2019. "A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches," Energy, Elsevier, vol. 188(C).
    4. Bogusława Waliszewska & Mieczysław Grzelak & Eliza Gaweł & Agnieszka Spek-Dźwigała & Agnieszka Sieradzka & Wojciech Czekała, 2021. "Chemical Characteristics of Selected Grass Species from Polish Meadows and Their Potential Utilization for Energy Generation Purposes," Energies, MDPI, vol. 14(6), pages 1-14, March.
    5. Kumar, Sanjoy & Ghosh, Prosenjit, 2018. "Sustainable bio-energy potential of perennial energy grass from reclaimed coalmine spoil (marginal sites) of India," Renewable Energy, Elsevier, vol. 123(C), pages 475-485.
    6. Cho, Min-Hwan & Mun, Tae-Young & Choi, Young-Kon & Kim, Joo-Sik, 2014. "Two-stage air gasification of mixed plastic waste: Olivine as the bed material and effects of various additives and a nickel-plated distributor on the tar removal," Energy, Elsevier, vol. 70(C), pages 128-134.
    7. Isah Y. Mohammed & Yousif A. Abakr & Feroz K. Kazi & Suzana Yusup & Ibraheem Alshareef & Soh A. Chin, 2015. "Comprehensive Characterization of Napier Grass as a Feedstock for Thermochemical Conversion," Energies, MDPI, vol. 8(5), pages 1-15, April.
    8. M. Shahabuddin & Tanvir Alam, 2022. "Gasification of Solid Fuels (Coal, Biomass and MSW): Overview, Challenges and Mitigation Strategies," Energies, MDPI, vol. 15(12), pages 1-20, June.
    9. Ashfaq Ahmed & Muhammad S. Abu Bakar & Abdul Razzaq & Syarif Hidayat & Farrukh Jamil & Muhammad Nadeem Amin & Rahayu S. Sukri & Noor S. Shah & Young-Kwon Park, 2021. "Characterization and Thermal Behavior Study of Biomass from Invasive Acacia mangium Species in Brunei Preceding Thermochemical Conversion," Sustainability, MDPI, vol. 13(9), pages 1-13, May.
    10. Hosseinpour, Soleiman & Aghbashlo, Mortaza & Tabatabaei, Meisam & Mehrpooya, Mehdi, 2017. "Estimation of biomass higher heating value (HHV) based on the proximate analysis by using iterative neural network-adapted partial least squares (INNPLS)," Energy, Elsevier, vol. 138(C), pages 473-479.
    11. Krystian Butlewski, 2022. "Concept for Biomass and Organic Waste Refinery Plants Based on the Locally Available Organic Materials in Rural Areas of Poland," Energies, MDPI, vol. 15(9), pages 1-19, May.
    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. Łukasz Sobol & Dominika Sabat & Arkadiusz Dyjakon, 2023. "Assessment of Bark Properties from Various Tree Species in Terms of Its Hydrophobicity and Energy Suitability," Energies, MDPI, vol. 16(18), pages 1-21, September.
    2. Łukasz Sobol & Jacek Łyczko & Arkadiusz Dyjakon & Ryszard Sroczyński, 2023. "Relationship between Odor Adsorption Ability and Physical–Hydraulic Properties of Torrefied Biomass: Initial Study," Energies, MDPI, vol. 16(4), pages 1-18, February.

    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. Rahaman, Touhidur & Biswas, Subhadeep & Ghorai, Shubhankar & Bera, Sudeshna & Dey, Sonali & Guha, Suman & Maity, Debabrata & De, Sukanta & Ganguly, Jhuma & Das, Malay, 2023. "Integrated application of morphological, anatomical, biochemical and physico-chemical methods to identify superior, lignocellulosic grass feedstocks for bioenergy purposes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    2. Huang, Jijiang & Veksha, Andrei & Chan, Wei Ping & Giannis, Apostolos & Lisak, Grzegorz, 2022. "Chemical recycling of plastic waste for sustainable material management: A prospective review on catalysts and processes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    3. Frank Hensgen & Michael Wachendorf, 2018. "Aqueous Leaching Prior to Dewatering Improves the Quality of Solid Fuels from Grasslands," Energies, MDPI, vol. 11(4), pages 1-13, April.
    4. Kim, Jun Young & Kim, Dongjae & Li, Zezhong John & Dariva, Claudio & Cao, Yankai & Ellis, Naoko, 2023. "Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach," Energy, Elsevier, vol. 263(PC).
    5. Kotchakarn Nantasaksiri & Patcharawat Charoen-Amornkitt & Takashi Machimura, 2021. "Land Potential Assessment of Napier Grass Plantation for Power Generation in Thailand Using SWAT Model. Model Validation and Parameter Calibration," Energies, MDPI, vol. 14(5), pages 1-15, March.
    6. David Antonio Buentello-Montoya & Miguel Ángel Armenta-Gutiérrez & Victor Manuel Maytorena-Soria, 2023. "Parametric Modelling Study to Determine the Feasibility of the Co-Gasification of Macroalgae and Plastics for the Production of Hydrogen-Rich Syngas," Energies, MDPI, vol. 16(19), pages 1-18, September.
    7. Cho, Min-Hwan & Choi, Young-Kon & Kim, Joo-Sik, 2015. "Air gasification of PVC (polyvinyl chloride)-containing plastic waste in a two-stage gasifier using Ca-based additives and Ni-loaded activated carbon for the production of clean and hydrogen-rich prod," Energy, Elsevier, vol. 87(C), pages 586-593.
    8. Santa Margarida Santos & Ana Carolina Assis & Leandro Gomes & Catarina Nobre & Paulo Brito, 2022. "Waste Gasification Technologies: A Brief Overview," Waste, MDPI, vol. 1(1), pages 1-26, December.
    9. Bello, Yusuf H. & Ahmed, Mahmoud A. & Ookawara, Shinichi & Elwardany, Ahmed E., 2022. "Numerical and experimental investigation on air distributor design of fluidized bed reactor of sawdust pyrolysis," Energy, Elsevier, vol. 239(PC).
    10. Frank Hensgen & Michael Wachendorf, 2016. "The Effect of the Invasive Plant Species Lupinus polyphyllus Lindl. on Energy Recovery Parameters of Semi-Natural Grassland Biomass," Sustainability, MDPI, vol. 8(10), pages 1-14, October.
    11. Bogusława Waliszewska & Mieczysław Grzelak & Eliza Gaweł & Agnieszka Spek-Dźwigała & Agnieszka Sieradzka & Wojciech Czekała, 2021. "Chemical Characteristics of Selected Grass Species from Polish Meadows and Their Potential Utilization for Energy Generation Purposes," Energies, MDPI, vol. 14(6), pages 1-14, March.
    12. M. Shahabuddin & Tanvir Alam, 2022. "Gasification of Solid Fuels (Coal, Biomass and MSW): Overview, Challenges and Mitigation Strategies," Energies, MDPI, vol. 15(12), pages 1-20, June.
    13. Chen, Xiaoling & Zhang, Yongxing & Xu, Baoshen & Li, Yifan, 2022. "A simple model for estimation of higher heating value of oily sludge," Energy, Elsevier, vol. 239(PA).
    14. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    15. Chen, Dengyu & Cen, Kehui & Cao, Xiaobing & Chen, Fan & Zhang, Jie & Zhou, Jianbin, 2021. "Insight into a new phenolic-leaching pretreatment on bamboo pyrolysis: Release characteristics of pyrolytic volatiles, upgradation of three phase products, migration of elements, and energy yield," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    16. Ascher, Simon & Watson, Ian & You, Siming, 2022. "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    17. Yang, Shiliang & Dong, Ruihan & Du, Yanxiang & Wang, Shuai & Wang, Hua, 2021. "Numerical study of the biomass pyrolysis process in a spouted bed reactor through computational fluid dynamics," Energy, Elsevier, vol. 214(C).
    18. Anna Matveeva & Aleksey Bychkov, 2022. "How to Train an Artificial Neural Network to Predict Higher Heating Values of Biofuel," Energies, MDPI, vol. 15(19), pages 1-13, September.
    19. Xing, Jiangkuan & Wang, Haiou & Luo, Kun & Wang, Shuai & Bai, Yun & Fan, Jianren, 2019. "Predictive single-step kinetic model of biomass devolatilization for CFD applications: A comparison study of empirical correlations (EC), artificial neural networks (ANN) and random forest (RF)," Renewable Energy, Elsevier, vol. 136(C), pages 104-114.
    20. Lisandra Rocha-Meneses & Oghenetejiri Frances Otor & Nemailla Bonturi & Kaja Orupõld & Timo Kikas, 2019. "Bioenergy Yields from Sequential Bioethanol and Biomethane Production: An Optimized Process Flow," Sustainability, MDPI, vol. 12(1), pages 1-19, December.

    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:jsusta:v:14:y:2022:i:18:p:11369-:d:911706. 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.