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Optimization of Process Parameters of Rhamnolipid Treatment of Oily Sludge Based on Response Surface Methodology
[Image: see text] Oily sludge is a hazardous waste. If not handled properly, it can not only pollute the environment but also endanger human health. This study is the first to use a response surface method to optimize the main parameters of rhamnolipid-based recovery of oil from oily sludge. Using r...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7675929/ https://www.ncbi.nlm.nih.gov/pubmed/33225164 http://dx.doi.org/10.1021/acsomega.0c04108 |
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author | Liu, Chong Xu, Qi Hu, Xuefei Zhang, Shengnan Zhang, PengYan You, Yongjun |
author_facet | Liu, Chong Xu, Qi Hu, Xuefei Zhang, Shengnan Zhang, PengYan You, Yongjun |
author_sort | Liu, Chong |
collection | PubMed |
description | [Image: see text] Oily sludge is a hazardous waste. If not handled properly, it can not only pollute the environment but also endanger human health. This study is the first to use a response surface method to optimize the main parameters of rhamnolipid-based recovery of oil from oily sludge. Using rhamnolipids as the cleaning agent and the oil recovery fraction as the evaluation index, the factors affecting the cleaning efficiency of oily sludge were optimized. The aforementioned sludge was obtained from the Tarim Oilfield. A single-factor experiment was conducted to determine the optimal range of the dosage, liquid–solid ratio, pH value, and time. The Box–Behnken response surface method was used to investigate the influence of each variable on the residual oil fraction of the oily sludge, and the dosage, pH value, and time were found to have a significant impact. The model optimization results show that the best process conditions for rhamnolipid-based recovery of oil are as follows: rhamnolipid dosage = 167.785 mg/L; liquid–solid ratio = 4.589:1; pH = 9.618; time = 1.627 h. Under optimal conditions, the model-predicted oil recovery fraction and the actual oil recovery fraction were 85.15 and 82.56%, respectively; the relative error between the predicted and the actual values was 2.59%. These results indicate that the model results are reliable. The solid residue after the cleaning was also analyzed to gain an in-depth understanding of the cleaning process. This study determined the feasibility of a rhamnolipid-based solution for the treatment of oily sludge and oil-contaminated soil. |
format | Online Article Text |
id | pubmed-7675929 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-76759292020-11-20 Optimization of Process Parameters of Rhamnolipid Treatment of Oily Sludge Based on Response Surface Methodology Liu, Chong Xu, Qi Hu, Xuefei Zhang, Shengnan Zhang, PengYan You, Yongjun ACS Omega [Image: see text] Oily sludge is a hazardous waste. If not handled properly, it can not only pollute the environment but also endanger human health. This study is the first to use a response surface method to optimize the main parameters of rhamnolipid-based recovery of oil from oily sludge. Using rhamnolipids as the cleaning agent and the oil recovery fraction as the evaluation index, the factors affecting the cleaning efficiency of oily sludge were optimized. The aforementioned sludge was obtained from the Tarim Oilfield. A single-factor experiment was conducted to determine the optimal range of the dosage, liquid–solid ratio, pH value, and time. The Box–Behnken response surface method was used to investigate the influence of each variable on the residual oil fraction of the oily sludge, and the dosage, pH value, and time were found to have a significant impact. The model optimization results show that the best process conditions for rhamnolipid-based recovery of oil are as follows: rhamnolipid dosage = 167.785 mg/L; liquid–solid ratio = 4.589:1; pH = 9.618; time = 1.627 h. Under optimal conditions, the model-predicted oil recovery fraction and the actual oil recovery fraction were 85.15 and 82.56%, respectively; the relative error between the predicted and the actual values was 2.59%. These results indicate that the model results are reliable. The solid residue after the cleaning was also analyzed to gain an in-depth understanding of the cleaning process. This study determined the feasibility of a rhamnolipid-based solution for the treatment of oily sludge and oil-contaminated soil. American Chemical Society 2020-11-06 /pmc/articles/PMC7675929/ /pubmed/33225164 http://dx.doi.org/10.1021/acsomega.0c04108 Text en © 2020 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes. |
spellingShingle | Liu, Chong Xu, Qi Hu, Xuefei Zhang, Shengnan Zhang, PengYan You, Yongjun Optimization of Process Parameters of Rhamnolipid Treatment of Oily Sludge Based on Response Surface Methodology |
title | Optimization of Process Parameters
of Rhamnolipid Treatment of Oily Sludge Based on Response Surface
Methodology |
title_full | Optimization of Process Parameters
of Rhamnolipid Treatment of Oily Sludge Based on Response Surface
Methodology |
title_fullStr | Optimization of Process Parameters
of Rhamnolipid Treatment of Oily Sludge Based on Response Surface
Methodology |
title_full_unstemmed | Optimization of Process Parameters
of Rhamnolipid Treatment of Oily Sludge Based on Response Surface
Methodology |
title_short | Optimization of Process Parameters
of Rhamnolipid Treatment of Oily Sludge Based on Response Surface
Methodology |
title_sort | optimization of process parameters
of rhamnolipid treatment of oily sludge based on response surface
methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7675929/ https://www.ncbi.nlm.nih.gov/pubmed/33225164 http://dx.doi.org/10.1021/acsomega.0c04108 |
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