Modelling the energy content of municipal solid waste and determination of its physico-chemical correlation using multiple regression analysis

IBIKUNLE, R.A. and Titiladunayo, I.F. and Akinnuli, B.O. and Lukman, A. F. and Ikubanni, P.P. and Agboola, O.O. (2018) Modelling the energy content of municipal solid waste and determination of its physico-chemical correlation using multiple regression analysis. International Journal of Mechanical Engineering and Technology, 9 (11). pp. 220-232.

Text MSW paper.pdf Download (236kB)

Abstract

Municipal solid waste (MSW) is an aggregate of unwanted and discarded materials that are generated, as man interact with the environment, in his daily activities. The agglomeration of MSW in the environment is huge and its inefficient management could result in land degradation and pollution, unsightly scenes, ecological contamination and global warming. The energy content of its individual components varies from 0 46.169 kJ/kg while that of overall composition remains a function of mix; physicochemical and thermochemical characteristics of the components. Therefore, this paper presents a waste-to-energy method for MSW management and energy recovery for heating or electricity generation. A multiple regression analysis was used to develop a predictive model for the energy content of MSW, while the effect of physicochemical characteristic of the mix on the overall MSW energy values was established. Waste samples were collected from Lasojo dumpsite in Ilorin Metropolis of Kwara State, Nigeria. The wastes streams were identified and characterized to determine the properties of the various fractions that were available for energy generation (kg). Three regression models were therefore developed and fitted to variables derived from proximate analysis, ultimate analysis and the physical compositions of MSW. A diagnostic check on the models show that model 1 and 2 suffer the problem of multicollinearity. This necessitate the use of ridge regression estimator. The models developed in this study can therefore, accurately predict the energy content of municipal solid waste and correlate it with its physicochemical characteristics.

Actions (login required)

View Item