Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Varying the depth of HFCW media causes differences in the redox status within the system, and hence the community structure and diversity of bacteria, affecting removal rates of different pollutants. The key functional microorganisms of CWs that remove contaminants belong to the phyla Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes. Secondary data of 111 HFCWs (1232 datasets) were analyzed to deduce the relationship between volumetric removal rate coefficients (K, K, K, and K) and depth. Equations of depth were derived in terms of rate coefficients using machine learning approach (MLR and SVR) (R = 0.85, 0.87 respectively). These equations were then used to find the optimum depth for pollutant(s) removal using Grey wolf optimization (GWO). The computed optimum depths were 1.48, 1.71, 1.91, 2.09, and 2.14 m for the removal of BOD, TKN, TN, TP, and combined nutrients, respectively, which were validated through primary data. This study would be helpful for optimal design of HFCWs.
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http://dx.doi.org/10.1016/j.biortech.2023.128898 | DOI Listing |