Modeling energy content of municipal solid waste based on proximate analysis: R-k class estimator approach
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 energ...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Cogent Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2022.2046243 |
| _version_ | 1797765195821481984 |
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| author | Rotimi Adedayo Ibikunle Adewale Folaranmi Lukman Isaac Femi Titiladunayo Abdul-Rahaman Haadi |
| author_facet | Rotimi Adedayo Ibikunle Adewale Folaranmi Lukman Isaac Femi Titiladunayo Abdul-Rahaman Haadi |
| author_sort | Rotimi Adedayo Ibikunle |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T20:07:44Z |
| format | Article |
| id | doaj.art-539ce4729a4b40c0a60aa9fb2859179f |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T20:07:44Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-539ce4729a4b40c0a60aa9fb2859179f2023-08-02T01:58:43ZengTaylor & Francis GroupCogent Engineering2331-19162022-12-019110.1080/23311916.2022.2046243Modeling energy content of municipal solid waste based on proximate analysis: R-k class estimator approachRotimi Adedayo Ibikunle0Adewale Folaranmi Lukman1Isaac Femi Titiladunayo2Abdul-Rahaman Haadi3Department of Mechanical Engineering, Landmark University, Omu-Aran, NigeriaDepartment of Epidemiology and Biostatistics, University of Medical Sciences, Ondo, NigeriaDepartment of Mechanical Engineering, Federal University of Technology, Akure, NigeriaDepartment of Statistical Sciences, Tamale Technical University, GhanaIn 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.https://www.tandfonline.com/doi/10.1080/23311916.2022.2046243Municipal solid wasteproximate analysisheating valuesmodelingprincipal component regression and r-k class estimators |
| spellingShingle | Rotimi Adedayo Ibikunle Adewale Folaranmi Lukman Isaac Femi Titiladunayo Abdul-Rahaman Haadi Modeling energy content of municipal solid waste based on proximate analysis: R-k class estimator approach Cogent Engineering Municipal solid waste proximate analysis heating values modeling principal component regression and r-k class estimators |
| title | Modeling energy content of municipal solid waste based on proximate analysis: R-k class estimator approach |
| title_full | Modeling energy content of municipal solid waste based on proximate analysis: R-k class estimator approach |
| title_fullStr | Modeling energy content of municipal solid waste based on proximate analysis: R-k class estimator approach |
| title_full_unstemmed | Modeling energy content of municipal solid waste based on proximate analysis: R-k class estimator approach |
| title_short | Modeling energy content of municipal solid waste based on proximate analysis: R-k class estimator approach |
| title_sort | modeling energy content of municipal solid waste based on proximate analysis r k class estimator approach |
| topic | Municipal solid waste proximate analysis heating values modeling principal component regression and r-k class estimators |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2022.2046243 |
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