Review of higher heating value of municipal solid waste based on analysis and smart modelling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU141561" target="_blank" >RIV/00216305:26210/21:PU141561 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1364032121008686?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1364032121008686?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rser.2021.111591" target="_blank" >10.1016/j.rser.2021.111591</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Review of higher heating value of municipal solid waste based on analysis and smart modelling

Popis výsledku v původním jazyce

Energy recovery from 252 kinds of solid waste originating from various geographical areas under thermal waste-to-energy operation is investigated. A fast, economical, and comparative methodology is presented for evaluating the heating values resulted from burning municipal solid waste (MSW) based on prior knowledge, specialist experience, and data-mining methods. Development of models for estimating higher heating values (HHVs) of 252 MSW samples based on the ultimate analysis is conducted by simultaneously utilising five nonlinear models including Radial Basis Function (RBF) neural network in conjunction with Genetic Algorithm (GA), namely GA-RBF, genetic programming (GP), multivariate nonlinear regression (MNR), particle swarm optimisation adaptive neuro-fuzzy inference system (PSO-ANFIS) and committee machine intelligent system (CMIS) models to increase the accuracy of each model. Eight different equations based on MNR are developed to estimate energy recovery capacity from different MSW groups (e.g., textiles, plastics, papers, rubbers, mixtures, woods, sewage sludge and other waste). A detailed investigation is conducted to analyse the accuracy of the models. It is indicated that the CMIS model has the best performance comparing the results obtained from different models. The R2 values of the test dataset for GA-RBF are 0.888, for GP 0.979, for MNR 0.978, for PSO-ANFIS 0.965, and for CMIS 0.985. The developed models with an acceptable accuracy would be followed by a better estimation of HHV and providing reliable heating value for an automatic combustion control system. The results obtained from this study are beneficial to design and optimise sustainable thermal waste-to-energy (WTF) processes to accelerate city transition into a circular economy. © 2021

Název v anglickém jazyce

Review of higher heating value of municipal solid waste based on analysis and smart modelling

Popis výsledku anglicky

Energy recovery from 252 kinds of solid waste originating from various geographical areas under thermal waste-to-energy operation is investigated. A fast, economical, and comparative methodology is presented for evaluating the heating values resulted from burning municipal solid waste (MSW) based on prior knowledge, specialist experience, and data-mining methods. Development of models for estimating higher heating values (HHVs) of 252 MSW samples based on the ultimate analysis is conducted by simultaneously utilising five nonlinear models including Radial Basis Function (RBF) neural network in conjunction with Genetic Algorithm (GA), namely GA-RBF, genetic programming (GP), multivariate nonlinear regression (MNR), particle swarm optimisation adaptive neuro-fuzzy inference system (PSO-ANFIS) and committee machine intelligent system (CMIS) models to increase the accuracy of each model. Eight different equations based on MNR are developed to estimate energy recovery capacity from different MSW groups (e.g., textiles, plastics, papers, rubbers, mixtures, woods, sewage sludge and other waste). A detailed investigation is conducted to analyse the accuracy of the models. It is indicated that the CMIS model has the best performance comparing the results obtained from different models. The R2 values of the test dataset for GA-RBF are 0.888, for GP 0.979, for MNR 0.978, for PSO-ANFIS 0.965, and for CMIS 0.985. The developed models with an acceptable accuracy would be followed by a better estimation of HHV and providing reliable heating value for an automatic combustion control system. The results obtained from this study are beneficial to design and optimise sustainable thermal waste-to-energy (WTF) processes to accelerate city transition into a circular economy. © 2021

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

<a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

ISSN

1364-0321

e-ISSN

—

Svazek periodika

neuveden

Číslo periodika v rámci svazku

151

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

111591-111591

Kód UT WoS článku

000708470200008

EID výsledku v databázi Scopus

2-s2.0-85113487292