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
Cement ball mills in the finishing stage of the cement industries consume the highest energy in the cement manufacturing stage. Therefore, suitable controllers that result in good productivity and product quality with reduced energy consumption are required for the cement ball mill grinding process to increase the profit margins. In this study, generalised predictive controllers (GPC)have been designed for the cement ball mill grinding operation using the model obtained from the step response data taken from the industrially recognized simulator. The servo and regulatory responses are analysed with and without constraints by implementing the designed GPC under the closed loop. The error metrics for GPC and conventional controllers are also analysed. The designed GPC for the cement ball mill grinding process outperforms the traditional controller in error metrics.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1038/s41598-024-82708-w | DOI Listing |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11682061 | PMC |
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