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Comprehensive Review on Waste Generation Modeling

Author

Listed:
  • Radovan Šomplák

    (Institute of Process Engineering, Faculty of Mechanical Engineering, Brno University of Technology, Technická 2, 616 69 Brno, Czech Republic)

  • Veronika Smejkalová

    (Institute of Process Engineering, Faculty of Mechanical Engineering, Brno University of Technology, Technická 2, 616 69 Brno, Czech Republic)

  • Martin Rosecký

    (Czech Math, a.s., Šumavská 416/15, 602 00 Brno, Czech Republic)

  • Lenka Szásziová

    (Institute of Process Engineering, Faculty of Mechanical Engineering, Brno University of Technology, Technická 2, 616 69 Brno, Czech Republic)

  • Vlastimír Nevrlý

    (Institute of Process Engineering, Faculty of Mechanical Engineering, Brno University of Technology, Technická 2, 616 69 Brno, Czech Republic)

  • Dušan Hrabec

    (Institute of Mathematics, Faculty of Applied Informatics, Tomas Bata University in Zlín, Nad Stráněmi 4511, 760 05 Zlín, Czech Republic)

  • Martin Pavlas

    (Institute of Process Engineering, Faculty of Mechanical Engineering, Brno University of Technology, Technická 2, 616 69 Brno, Czech Republic)

Abstract

Strategic plans for waste management require information on the current and future waste generation as a primary data source. Over the years, various approaches and methods for waste generation modeling have been presented and applied. This review provides a summary of the tasks that require information on waste generation that are most frequently handled in waste management. It is hypothesized that there is not currently a modeling approach universally suitable for forecasting any fraction of waste. It is also hypothesized that most models do not allow for modeling different scenarios of future development. Almost 360 publications were examined in detail, and all of the tracked attributes are included in the supplementary. A general step-by-step guide to waste generation forecasting, comprising data preparation, pre-processing, processing, and post-processing, was proposed. The problems that occurred in the individual steps were specified, and the authors’ recommendations for their solution were provided. A forecasting approach based on a short time series is presented, due to insufficient options of approaches for this problem. An approach is presented for creating projections of waste generation depending on the expected system changes. Researchers and stakeholders can use this document as a supporting material when deciding on a suitable approach to waste generation modeling or waste management plans.

Suggested Citation

  • Radovan Šomplák & Veronika Smejkalová & Martin Rosecký & Lenka Szásziová & Vlastimír Nevrlý & Dušan Hrabec & Martin Pavlas, 2023. "Comprehensive Review on Waste Generation Modeling," Sustainability, MDPI, vol. 15(4), pages 1-29, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3278-:d:1064709
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    References listed on IDEAS

    as
    1. Ilbahar, Esra & Kahraman, Cengiz & Cebi, Selcuk, 2021. "Location selection for waste-to-energy plants by using fuzzy linear programming," Energy, Elsevier, vol. 234(C).
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Jiang, P. & Liu, X., 2016. "Hidden Markov model for municipal waste generation forecasting under uncertainties," European Journal of Operational Research, Elsevier, vol. 250(2), pages 639-651.
    4. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "Thirty years of heterogeneous vehicle routing," European Journal of Operational Research, Elsevier, vol. 249(1), pages 1-21.
    5. Yinsheng Yang & Gang Yuan & Jiaxiang Cai & Silin Wei, 2021. "Forecasting of Disassembly Waste Generation under Uncertainties Using Digital Twinning-Based Hidden Markov Model," Sustainability, MDPI, vol. 13(10), pages 1-15, May.
    6. Tomić, Tihomir & Dominković, Dominik Franjo & Pfeifer, Antun & Schneider, Daniel Rolph & Pedersen, Allan Schrøder & Duić, Neven, 2017. "Waste to energy plant operation under the influence of market and legislation conditioned changes," Energy, Elsevier, vol. 137(C), pages 1119-1129.
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    1. Smejkalová, Veronika & Jirásek, Petr & Burcin, Boris & Kučera, Tomáš & Pluskal, Jaroslav & Nevrlý, Vlastimír & Šomplák, Radovan, 2025. "Demographic development in waste production forecasts for energy recovery planning," Energy, Elsevier, vol. 323(C).

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