IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3278-d1064709.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3278/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/4/3278/
    Download Restriction: no
    ---><---

    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. 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.
    4. 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.
    5. 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.
    6. 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.
    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. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. Putna, Ondřej & Janošťák, František & Šomplák, Radovan & Pavlas, Martin, 2018. "Demand modelling in district heating systems within the conceptual design of a waste-to-energy plant," Energy, Elsevier, vol. 163(C), pages 1125-1139.
    3. Wesley Marcos Almeida & Claudimar Pereira Veiga, 2023. "Does demand forecasting matter to retailing?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 219-232, June.
    4. Tetiana Zatonatska & Olena Liashenko & Yana Fareniuk & Oleksandr Dluhopolskyi & Artur Dmowski & Marzena Cichorzewska, 2022. "The Migration Influence on the Forecasting of Health Care Budget Expenditures in the Direction of Sustainability: Case of Ukraine," Sustainability, MDPI, vol. 14(21), pages 1-17, November.
    5. Alroomi, Azzam & Karamatzanis, Georgios & Nikolopoulos, Konstantinos & Tilba, Anna & Xiao, Shujun, 2022. "Fathoming empirical forecasting competitions’ winners," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1519-1525.
    6. Ramadan, Mohamad & Murr, Rabih & Khaled, Mahmoud & Olabi, Abdul Ghani, 2018. "Mixed numerical - Experimental approach to enhance the heat pump performance by drain water heat recovery," Energy, Elsevier, vol. 149(C), pages 1010-1021.
    7. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
    8. Hatzenbühler, Jonas & Jenelius, Erik & Gidófalvi, Gyözö & Cats, Oded, 2023. "Modular vehicle routing for combined passenger and freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    9. Hossein Nami & Amjad Anvari-Moghaddam & Ahmad Arabkoohsar & Amir Reza Razmi, 2020. "4E Analyses of a Hybrid Waste-Driven CHP–ORC Plant with Flue Gas Condensation," Sustainability, MDPI, vol. 12(22), pages 1-21, November.
    10. Tomić, Tihomir & Schneider, Daniel Rolph, 2018. "The role of energy from waste in circular economy and closing the loop concept – Energy analysis approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 268-287.
    11. Sun, Chenhao & Zhou, Zhuoyu & Zeng, Xiangjun & Li, Zewen & Wang, Yuanyuan & Deng, Feng, 2022. "A multi-model-integration-based prediction methodology for the spatiotemporal distribution of vulnerabilities in integrated energy systems under the multi-type, imbalanced, and dependent input data sc," Applied Energy, Elsevier, vol. 320(C).
    12. Guo, Su & Zheng, Kun & He, Yi & Kurban, Aynur, 2023. "The artificial intelligence-assisted short-term optimal scheduling of a cascade hydro-photovoltaic complementary system with hybrid time steps," Renewable Energy, Elsevier, vol. 202(C), pages 1169-1189.
    13. Patterson, S.R. & Kozan, E. & Hyland, P., 2017. "Energy efficient scheduling of open-pit coal mine trucks," European Journal of Operational Research, Elsevier, vol. 262(2), pages 759-770.
    14. Jonathan Berrisch & Florian Ziel, 2022. "Distributional modeling and forecasting of natural gas prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1065-1086, September.
    15. Zajac, Sandra & Huber, Sandra, 2021. "Objectives and methods in multi-objective routing problems: a survey and classification scheme," European Journal of Operational Research, Elsevier, vol. 290(1), pages 1-25.
    16. Imen Ben Mohamed & Walid Klibi & Olivier Labarthe & Jean-Christophe Deschamps & Mohamed Zied Babai, 2017. "Modelling and solution approaches for the interconnected city logistics," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2664-2684, May.
    17. Yu, Yang & Wang, Sihan & Wang, Junwei & Huang, Min, 2019. "A branch-and-price algorithm for the heterogeneous fleet green vehicle routing problem with time windows," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 511-527.
    18. Emmanuel Senyo Fianu, 2022. "Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach," Forecasting, MDPI, vol. 4(2), pages 1-27, June.
    19. Hrabec, Dušan & Šomplák, Radovan & Nevrlý, Vlastimír & Viktorin, Adam & Pluháček, Michal & Popela, Pavel, 2020. "Sustainable waste-to-energy facility location: Influence of demand on energy sales," Energy, Elsevier, vol. 207(C).
    20. Ted Gifford & Tracy Opicka & Ashesh Sinha & Daniel Vanden Brink & Andy Gifford & Robert Randall, 2018. "Dispatch Optimization in Bulk Tanker Transport Operations," Interfaces, INFORMS, vol. 48(5), pages 403-421, October.

    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:15:y:2023:i:4:p:3278-:d:1064709. 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.