Multiple regression models for the lower heating value of municipal solid waste in Taiwan

A multiple regression analysis was used to develop two predictive models of lower heating value (LHV) for municipal solid waste (MSW), using 180 samples gathered from cities and counties in Taiwan during 2001–2002. These models are referred to as the original proposed model (OPM) and the simplified...

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Bibliographic Details
Published in:Journal of environmental management 2007-12, Vol.85 (4), p.891-899
Main Authors: Chang, Y.F., Lin, C.J., Chyan, J.M., Chen, I.M., Chang, J.E.
Format: Article
Language:English
Subjects:
Animal, plant and microbial ecology
Applied ecology
Biological and medical sciences
Cities
Conservation, protection and management of environment and wildlife
Environmental management
Fundamental and applied biological sciences. Psychology
General aspects
Geography
Heating
Incineration
Lower heating value
Models, Theoretical
Multiple regression analysis
Municipal solid waste
Physical combustible component
Regression Analysis
Statistical models
Taiwan
Waste disposal
Waste management
Waste Management - methods
Waste Management - statistics & numerical data
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Summary:A multiple regression analysis was used to develop two predictive models of lower heating value (LHV) for municipal solid waste (MSW), using 180 samples gathered from cities and counties in Taiwan during 2001–2002. These models are referred to as the original proposed model (OPM) and the simplified model (SM). The coefficients of multiple determinations for the OPM and SM were 0.983 and 0.975, respectively. To verify the feasibility of the models, a demonstration program based on sampling of MSW in Kaohsiung City was conducted. As a result, the OPM showed superior precision in terms of relative percentage deviation (RPD) and mean absolute percentage error (MAPE), when compared to the conventional models based on the proximate analysis, physical composition and ultimate analysis. The SM was derived by neglecting the three minor physical components used in the OPM. The resulting SM was less precise when compared to the OPM, but it was still acceptable, with a precision level better than the conventional models. It was concluded that the predictability of empirical models could be improved significantly through selection of the appropriate physical components and multiple regression analysis.
ISSN:0301-4797
1095-8630
DOI:10.1016/j.jenvman.2006.10.025