Modelling secondary waste composition using optimization and machine learning techniques: Case of the Czech Republic
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F26%3A0198358" target="_blank" >RIV/00216305:26210/26:0198358 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0956053X25004301" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0956053X25004301</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.wasman.2025.115019" target="_blank" >10.1016/j.wasman.2025.115019</a>
Alternative languages
Result language
angličtina
Original language name
Modelling secondary waste composition using optimization and machine learning techniques: Case of the Czech Republic
Original language description
To support the shift toward a circular economy in waste management, it is essential to monitor progress using measurable indicators. However, the growing volume of secondary waste from pre-treatment processes highlights the need to assess its composition, as it can represent a diverse mixture and complicates the evaluation of individual waste streams. The proposed approach aims to estimate the composition of secondary waste by using a combination of machine learning and optimization techniques. The cornerstone for evaluation is data from waste management monitoring. Machine learning based on linear or Bayesian linear regression allows for the efficient processing of large datasets and the identification of key relationships in the system. The optimization model developed for a special form of data reconciliation maintains insight into the results and ensures the preservation of mass balances. In a case study in the Czech Republic, the model identified a 3 % reduction in the material recovery of municipal waste, as this waste is used for energy recovery or landfilled after transformation into secondary waste. Mixed secondary waste consists of 46 % plastic waste, with only 20 % being truly recycled. A significant portion is landfilled, which represents a potential for at least energy recovery from the waste. With refined waste management indicators and potential for recovery, the results can contribute to improvements in terms of technology and regional focus.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
<a href="/en/project/GA25-15751S" target="_blank" >GA25-15751S: New methods in operations research for managerial decisions in waste management</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Waste Management
ISSN
0956-053X
e-ISSN
1879-2456
Volume of the periodical
205
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
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UT code for WoS article
001573610100001
EID of the result in the Scopus database
2-s2.0-105010847277