Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Bioremediation is a soil clean-up technique which exploits the metabolic capacity of microorganisms to degrade soil contaminants. A model was developed to simulate the ex situ bioremediation of a diesel-contaminated soil in a bio-slurry reactor inoculated with a diesel-degrading bacterial strain. Mass transfer processes involving desorption of diesel from soil to water and volatilization of diesel from water, and biodegradation by the bacterial inoculant were included in the model by using Weibull sigmoid kinetics and logistic/Monod kinetics respectively. Model parameters were estimated in batch-based abiotic and biodegradation experiments. Sensitivity analysis revealed the importance of maintaining a high bacterial density in the system for maximum bioremediation efficiency. The model was validated using a pilot bioreactor monitored for 528 h, which removed almost 90% of the diesel present in the system. The results revealed the capacity of the model to predict the bioremediation efficiency under different scenarios by adapting the input parameters to each system.
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Source |
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http://dx.doi.org/10.1016/j.jenvman.2019.06.034 | DOI Listing |
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