IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2022i1p46-d1009721.html
   My bibliography  Save this article

Determination of the Self-Ignition Behavior of the Accumulation of Sludge Dust and Sludge Pellets from the Sewage Sludge Thermal Drying Station

Author

Listed:
  • Adriana Dowbysz

    (Department of Chemistry, Biology and Biotechnology, Bialystok University of Technology, Wiejska 45A Street, 15-351 Bialystok, Poland)

  • Bożena Kukfisz

    (Faculty of Security Engineering and Civil Protection, The Main School of Fire Service, Slowackiego Street 52/54, 01-629 Warsaw, Poland)

  • Mariola Samsonowicz

    (Department of Chemistry, Biology and Biotechnology, Bialystok University of Technology, Wiejska 45A Street, 15-351 Bialystok, Poland)

  • Jan Stefan Bihałowicz

    (Institute of Safety Engineering, The Main School of Fire Service, Slowackiego Street 52/54, 01-629 Warsaw, Poland)

Abstract

Sewage sludge may pose a fire risk. The safe storage of biomass waste is a challenge due to self-heating processes. This study aims to assess the propensity to spontaneously combust of sewage sludge in order to determine safe storage and transport conditions. The evaluation of spontaneous ignition hazard was assessed according to EN 15188, by the determination of the self-ignition temperature. Certain parameters assumed to affect the inclination of sewage sludge to self-ignite, including the moisture content, bulk density, elemental composition, and particle size, were discussed. The results showed the risk of self-ignition during the storage and transport of sludge dust and pellets. The usage of the smallest basket volume resulted in the highest self-ignition temperatures, which were 186 °C and 160 °C for sludge pellets and dust, respectively. The comparison of the two forms of thermally dry sludge showed, that despite sludge pellets being easier to store and handle issues, the more favorable conditions for the management in terms of fire risk is sludge dust. Its temperatures for safe storage are slightly higher. The results highlighted that future research should focus on the hazards of silo fires and explosions in terms of silo fire prevention and management.

Suggested Citation

  • Adriana Dowbysz & Bożena Kukfisz & Mariola Samsonowicz & Jan Stefan Bihałowicz, 2022. "Determination of the Self-Ignition Behavior of the Accumulation of Sludge Dust and Sludge Pellets from the Sewage Sludge Thermal Drying Station," Energies, MDPI, vol. 16(1), pages 1-15, December.
  • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:46-:d:1009721
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/46/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/1/46/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gabriele Di Giacomo & Pietro Romano, 2022. "Evolution and Prospects in Managing Sewage Sludge Resulting from Municipal Wastewater Purification," Energies, MDPI, vol. 15(15), pages 1-33, August.
    2. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    3. Dinko Đurđević & Saša Žiković & Paolo Blecich, 2022. "Sustainable Sewage Sludge Management Technologies Selection Based on Techno-Economic-Environmental Criteria: Case Study of Croatia," Energies, MDPI, vol. 15(11), pages 1-23, May.
    4. Adriana Dowbysz & Mariola Samsonowicz & Bożena Kukfisz, 2022. "Recent Advances in Bio-Based Additive Flame Retardants for Thermosetting Resins," IJERPH, MDPI, vol. 19(8), pages 1-26, April.
    5. Dorota Siuta & Bożena Kukfisz & Aneta Kuczyńska & Piotr Tomasz Mitkowski, 2022. "Methodology for the Determination of a Process Safety Culture Index and Safety Culture Maturity Level in Industries," IJERPH, MDPI, vol. 19(5), pages 1-18, February.
    Full references (including those not matched with items on IDEAS)

    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. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    2. Jessica M. Vanslambrouck & Sean B. Wilson & Ker Sin Tan & Ella Groenewegen & Rajeev Rudraraju & Jessica Neil & Kynan T. Lawlor & Sophia Mah & Michelle Scurr & Sara E. Howden & Kanta Subbarao & Melissa, 2022. "Enhanced metanephric specification to functional proximal tubule enables toxicity screening and infectious disease modelling in kidney organoids," Nature Communications, Nature, vol. 13(1), pages 1-23, December.
    3. Lauren L. Porter & Allen K. Kim & Swechha Rimal & Loren L. Looger & Ananya Majumdar & Brett D. Mensh & Mary R. Starich & Marie-Paule Strub, 2022. "Many dissimilar NusG protein domains switch between α-helix and β-sheet folds," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Matthew Rosenblatt & Link Tejavibulya & Rongtao Jiang & Stephanie Noble & Dustin Scheinost, 2024. "Data leakage inflates prediction performance in connectome-based machine learning models," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. Sayedali Shetab Boushehri & Katharina Essig & Nikolaos-Kosmas Chlis & Sylvia Herter & Marina Bacac & Fabian J. Theis & Elke Glasmacher & Carsten Marr & Fabian Schmich, 2023. "Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Khaled Akkad & David He, 2023. "A dynamic mode decomposition based deep learning technique for prognostics," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2207-2224, June.
    7. Romain Fournier & Zoi Tsangalidou & David Reich & Pier Francesco Palamara, 2023. "Haplotype-based inference of recent effective population size in modern and ancient DNA samples," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    8. Laura Portell & Sergi Morera & Helena Ramalhinho, 2022. "Door-to-Door Transportation Services for Reduced Mobility Population: A Descriptive Analytics of the City of Barcelona," IJERPH, MDPI, vol. 19(8), pages 1-20, April.
    9. Caroline Haimerl & Douglas A. Ruff & Marlene R. Cohen & Cristina Savin & Eero P. Simoncelli, 2023. "Targeted V1 comodulation supports task-adaptive sensory decisions," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    10. Matthias Wagener & Andriette Bekker & Mohammad Arashi, 2021. "Mastering the Body and Tail Shape of a Distribution," Mathematics, MDPI, vol. 9(21), pages 1-22, October.
    11. Gallo Cassarino, Tiziano & Barrett, Mark, 2022. "Meeting UK heat demands in zero emission renewable energy systems using storage and interconnectors," Applied Energy, Elsevier, vol. 306(PB).
    12. Maren Schnieder, 2023. "Ebike Sharing vs. Bike Sharing: Demand Prediction Using Deep Neural Networks and Random Forests," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    13. Dinko Đurđević & Saša Žiković & Tomislav Čop, 2022. "Socio-Economic, Technical and Environmental Indicators for Sustainable Sewage Sludge Management and LEAP Analysis of Emissions Reduction," Energies, MDPI, vol. 15(16), pages 1-15, August.
    14. Gabriele Orlando & Daniele Raimondi & Ramon Duran-Romaña & Yves Moreau & Joost Schymkowitz & Frederic Rousseau, 2022. "PyUUL provides an interface between biological structures and deep learning algorithms," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    15. Hazal Colak Oz & Çiçek Güven & Gonzalo Nápoles, 2023. "School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach," Journal of Computational Social Science, Springer, vol. 6(1), pages 245-287, April.
    16. Vincent Wagner & Nicole Erika Radde, 2021. "SiCaSMA: An Alternative Stochastic Description via Concatenation of Markov Processes for a Class of Catalytic Systems," Mathematics, MDPI, vol. 9(10), pages 1-13, May.
    17. L. Mathur & B. Szalai & N. H. Du & R. Utharala & M. Ballinger & J. J. M. Landry & M. Ryckelynck & V. Benes & J. Saez-Rodriguez & C. A. Merten, 2022. "Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    18. Samuel G. Fadel & Sebastian Mair & Ricardo da Silva Torres & Ulf Brefeld, 2023. "Contextual movement models based on normalizing flows," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 51-72, March.
    19. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    20. Wout Bittremieux & Nicole E. Avalon & Sydney P. Thomas & Sarvar A. Kakhkhorov & Alexander A. Aksenov & Paulo Wender P. Gomes & Christine M. Aceves & Andrés Mauricio Caraballo-Rodríguez & Julia M. Gaug, 2023. "Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics," Nature Communications, Nature, vol. 14(1), pages 1-15, 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:jeners:v:16:y:2022:i:1:p:46-:d:1009721. 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.