Modeling energy content of municipal solid waste based on proximate analysis: R-k class estimator approach. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Ibikunle, Rotimi Adedayo; Lukman, Adewale Folaranmi; Titiladunayo, Isaac Femi; Haadi, Abdul-Rahaman
• Editors: Pham, D T
• Is Part Of: Cogent engineering. Volume 9:Issue 1(2022)
• Publication Date: 2022-12-31
• Subjects: Municipal solid waste -- proximate analysis -- heating values -- modeling -- principal component regression and r-k class estimators; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2022.2046243
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: In the Ilorin metropolis, there are power challenges. Energy supplied by Power Holding Company of Nigeria is insufficient for the social, technological, and industrial requirements of the metropolis. Moreover, the huge municipal solid waste (MSW) produced daily that supposed to be converted to energy is only constituting a nuisance. In waste to energy (WTE) procedures, the heating value (HV) of the MSW generated is pertinent in the selection or design of an appropriate waste to energy (WTE) technology required for waste conversion. The HV determination using ultimate analysis is tedious, expensive, and requires specialized equipment. A proximate analysis method that is less tedious and cheaper was adopted to obtain the dependent variables for the modeling of the HV. The high heating value (HHV) of MSW components was determined using a bomb calorimeter, and proximate analysis was used to determine the typical values for fixed carbon (FC), volatile matter (VM), Ash, and moisture (MC) to be 32%, 37%, 13%, and 5% correspondingly. The typical HV was estimated to be 24 MJ/kg. The heating value obtained from the bomb calorimeter was modeled against the dependent variables from proximate analysis. The conventional ordinary least squares (OLS) estimator is popularly used to estimate the model parameters. However, the performance of the estimator suffers a setback when the predictor variables are correlated. Alternatively, the ridge estimator (RE) and the principal component regression estimator (PCE) can be adopted. In this study, we combined PCE and RE to form the r-k class estimator for effective modeling. The estimators' performances are assessed using the mean squares error (MSE) criterion. The estimator with the smallest MSE is generally preferred. The result, the MSE of OLSE, ridge, PCE, and r-k are 581.84, 2.56, 523.69, and 0.239, respectively. The r-k class estimator outperforms other estimators considered in this study and is employed for the modeling. With a unit increase in the volatile matter and fixed carbon, heating values increased by about 21% and 36%, respectively. Also, the heating values decrease by about 0.2% and 40%, respectively, with a unit increase in Ash and Moisture.