The objective of the paper is to develop a mathematical model to compute the chemical compositions of the organic portion of municipal solid waste (MSW) using proximate analysis data. The validation of the developed models were done using test of hypothesis and field samples. In the study, MSW samples were collected from 23 different locations throughout the year and chemical compositions of the organic matter were determined such as carbon, hydrogen, oxygen, nitrogen and sulfur using elemental analyser. Proximate analysis such as combustible volatile matter (VM) and ash content were also measured for all the sites. The range of variation in the chemical compositions and proximate analysis were observed as 5·2% ≤ carbon ≤ 21·2%, 0·64% ≤ hydrogen ≤ 2·7%, 17·2% ≤ oxygen ≤ 67·6%, 0·3% ≤ nitrogen ≤ 1·1%, 0·1% ≤ sulfur ≤ 0·37%, 9·3% ≤ VM ≤ 35·5%, 18·6% ≤ ash ≤ 72·1% on dry basis. Various statistical analyses such as standard deviation, mean, P value and F value were also carried out in the study.
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1 April 2016
Research Article|
July 15 2014
Modeling chemical compositions of municipal solid waste
Sumedha Chakma, PhD;
Sumedha Chakma, PhD
Assistant Professor
Civil Engineering Department, Indian Institute of Technology Delhi, New Delhi, India
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Rakesh Chandra Vaishya, PhD;
Rakesh Chandra Vaishya, PhD
Professor
Civil Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Allahabad, Uttar Pradesh, India
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Alok Kumar Yadav, MTech
Alok Kumar Yadav, MTech
Engineer-I
Environmental Cell, Jaypee Bela Cement Plant (Unit of Jaiprakash Associates Ltd.), Jaypeepuram, Rewa, Madhya Pradesh, India
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Publisher: Emerald Publishing
Received:
August 11 2013
Accepted:
June 10 2014
Online ISSN: 2051-803X
ICE Publishing: All rights reserved
2016
Environmental Geotechnics (2016) 3 (2): 65–77.
Article history
Received:
August 11 2013
Accepted:
June 10 2014
Citation
Chakma S, Vaishya RC, Yadav AK (2016), "Modeling chemical compositions of municipal solid waste". Environmental Geotechnics, Vol. 3 No. 2 pp. 65–77, doi: https://doi.org/10.1680/envgeo.13.00082
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