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

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

Článek WoS

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

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

Energy recovery; Higher heating value; Municipal solid waste; Regression; Smart modelling; Ultimate analysis

Energy recovery; Higher heating value; Municipal solid waste; Regression; Smart modelling; Ultimate analysis

Dashti, A., Noushabadi, A.S., Asadi, J., Raji, M., Chofreh, A.G., Klemeš, J.J., Mohammadi, A.H.

2022

01.11.2021

Elsevier, Ltd.

PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND

1364-0321

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

151

Spojené státy americké

111591

111591

12

https://www.sciencedirect.com/science/article/pii/S1364032121008686?via%3Dihub