Modelling secondary waste composition using optimization and machine learning techniques: Case of the Czech Republic

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Modelling secondary waste composition using optimization and machine learning techniques: Case of the Czech Republic

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Modelling secondary waste composition using optimization and machine learning techniques: Case of the Czech Republic

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/GA25-15751S" target="_blank" >GA25-15751S: Nové metody operačního výzkumu pro managerské rozhodování v odpadovém hospodářství</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Waste Management

ISSN

0956-053X

e-ISSN

1879-2456

Svazek periodika

205

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

—

Kód UT WoS článku

001573610100001

EID výsledku v databázi Scopus

2-s2.0-105010847277