A novel encapsulation of 16 polycyclic aromatic hydrocarbons in petroleum sludge with palm oil fuel ash binder; an optimization study and sensitivity analysis using machine learning application
Palm oil fuel ash (POFA) has limited use as a fertilizer, while contribute effectively to the environmental contamination and health risks. Petroleum sludge poses a serious effect on the ecological environment and human health. The present work aimed to present a novel encapsulation process with POF...
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2023
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2-s2.0-85160340140 Roslee N.F.; Kamil N.A.F.M.; Alias S.; Senthil Kumar P.; Alkhadher S.; Muthusamy G.; Al-Gheethi A. A novel encapsulation of 16 polycyclic aromatic hydrocarbons in petroleum sludge with palm oil fuel ash binder; an optimization study and sensitivity analysis using machine learning application 2023 Chemosphere 334 10.1016/j.chemosphere.2023.139037 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160340140&doi=10.1016%2fj.chemosphere.2023.139037&partnerID=40&md5=1feac9b949d26bffd482e055eac6f87d Palm oil fuel ash (POFA) has limited use as a fertilizer, while contribute effectively to the environmental contamination and health risks. Petroleum sludge poses a serious effect on the ecological environment and human health. The present work aimed to present a novel encapsulation process with POFA binder for treating petroleum sludge. Among 16 polycyclic aromatic hydrocarbons, four compounds were selected for the optimization of encapsulation process due to their high risk as carcinogenic substrates. Percentage PS (10–50%) and curing days (7–28 days) factors were used in the optimization process. The leaching test of PAHs was assessed using a GC-MS. The best operating parameters to minimize PAHs leaching from solidified cubes with OPC and10% POFA were recorded with 10% PS and after 28 days, at which PAH leaching was 4.255 and 0.388 ppm with R2 is 0.90%. Sensitivity analysis of the actual and predicted results for both the control and the test (OPC and 10% POFA) revealed that the actual results of the 10% POFA experiments have a high consistency with the predicted data (R2 0.9881) while R2 in the cement experiments was 0.8009. These differences were explained based on the responses of PAH leaching toward percentage of PS and days of cure. In the OPC encapsulation process, the main role was belonged to PS% (94.22%), while with 10% POFA, PS% contributed by 32.36 and cure day contributed by 66.91%. © 2023 Elsevier Ltd Elsevier Ltd 456535 English Article |
author |
Roslee N.F.; Kamil N.A.F.M.; Alias S.; Senthil Kumar P.; Alkhadher S.; Muthusamy G.; Al-Gheethi A. |
spellingShingle |
Roslee N.F.; Kamil N.A.F.M.; Alias S.; Senthil Kumar P.; Alkhadher S.; Muthusamy G.; Al-Gheethi A. A novel encapsulation of 16 polycyclic aromatic hydrocarbons in petroleum sludge with palm oil fuel ash binder; an optimization study and sensitivity analysis using machine learning application |
author_facet |
Roslee N.F.; Kamil N.A.F.M.; Alias S.; Senthil Kumar P.; Alkhadher S.; Muthusamy G.; Al-Gheethi A. |
author_sort |
Roslee N.F.; Kamil N.A.F.M.; Alias S.; Senthil Kumar P.; Alkhadher S.; Muthusamy G.; Al-Gheethi A. |
title |
A novel encapsulation of 16 polycyclic aromatic hydrocarbons in petroleum sludge with palm oil fuel ash binder; an optimization study and sensitivity analysis using machine learning application |
title_short |
A novel encapsulation of 16 polycyclic aromatic hydrocarbons in petroleum sludge with palm oil fuel ash binder; an optimization study and sensitivity analysis using machine learning application |
title_full |
A novel encapsulation of 16 polycyclic aromatic hydrocarbons in petroleum sludge with palm oil fuel ash binder; an optimization study and sensitivity analysis using machine learning application |
title_fullStr |
A novel encapsulation of 16 polycyclic aromatic hydrocarbons in petroleum sludge with palm oil fuel ash binder; an optimization study and sensitivity analysis using machine learning application |
title_full_unstemmed |
A novel encapsulation of 16 polycyclic aromatic hydrocarbons in petroleum sludge with palm oil fuel ash binder; an optimization study and sensitivity analysis using machine learning application |
title_sort |
A novel encapsulation of 16 polycyclic aromatic hydrocarbons in petroleum sludge with palm oil fuel ash binder; an optimization study and sensitivity analysis using machine learning application |
publishDate |
2023 |
container_title |
Chemosphere |
container_volume |
334 |
container_issue |
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doi_str_mv |
10.1016/j.chemosphere.2023.139037 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160340140&doi=10.1016%2fj.chemosphere.2023.139037&partnerID=40&md5=1feac9b949d26bffd482e055eac6f87d |
description |
Palm oil fuel ash (POFA) has limited use as a fertilizer, while contribute effectively to the environmental contamination and health risks. Petroleum sludge poses a serious effect on the ecological environment and human health. The present work aimed to present a novel encapsulation process with POFA binder for treating petroleum sludge. Among 16 polycyclic aromatic hydrocarbons, four compounds were selected for the optimization of encapsulation process due to their high risk as carcinogenic substrates. Percentage PS (10–50%) and curing days (7–28 days) factors were used in the optimization process. The leaching test of PAHs was assessed using a GC-MS. The best operating parameters to minimize PAHs leaching from solidified cubes with OPC and10% POFA were recorded with 10% PS and after 28 days, at which PAH leaching was 4.255 and 0.388 ppm with R2 is 0.90%. Sensitivity analysis of the actual and predicted results for both the control and the test (OPC and 10% POFA) revealed that the actual results of the 10% POFA experiments have a high consistency with the predicted data (R2 0.9881) while R2 in the cement experiments was 0.8009. These differences were explained based on the responses of PAH leaching toward percentage of PS and days of cure. In the OPC encapsulation process, the main role was belonged to PS% (94.22%), while with 10% POFA, PS% contributed by 32.36 and cure day contributed by 66.91%. © 2023 Elsevier Ltd |
publisher |
Elsevier Ltd |
issn |
456535 |
language |
English |
format |
Article |
accesstype |
|
record_format |
scopus |
collection |
Scopus |
_version_ |
1825722581011922944 |