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dc.contributor.advisorDessureeault, Sean D.en_US
dc.contributor.authorTenorio, Victor Octavio
dc.creatorTenorio, Victor Octavioen_US
dc.date.accessioned2012-09-10T22:40:19Z
dc.date.available2012-09-10T22:40:19Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10150/242385
dc.description.abstractLarge opencast coal mines require a complex infrastructure to fulfill production demand and quality values. The distinct specifications required by each customer are achieved by blending adjustments. There is limited control in variability. With only partial information available, operation controllers blend coal by empirical approximation, trying to keep quality between acceptable ranges in order to avoid penalizations, shipment rejections or even contract suspensions. When a decision support system (DSS) centralized in a control room is used for blending control, crew operators visualize enhanced displays of the different sources of information, obtaining a holistic perspective of operations. Using a simulator to reproduce the blending sequence, crew operators can experiment with diverse what-if scenarios and develop blending strategies for an entire working shift, in which they also incorporate their own expertise and the knowledge obtained after interpreting the simulation results. The research focuses on the empirical analysis of the effectiveness of the DSS by studying the performance of crew users in different operating scenarios produced with a simulator. The development of a methodology for measuring this effectiveness and its impact in the quantification of controlling the variability of blending represents a significant contribution in the area of quality improvement for coal production. The effectiveness of the DSS for controlling the blending and load out processes has been numerically measured after experimenting diverse simulated scenarios, proving that the difference between estimated and actual quality delivered is narrower when using a DSS, in comparison with the BTU variability obtained from historical data. The strategies that produced better results in terms of control of coal quality variability, maximization of infrastructure utilization, time spent in making decisions and the minimization of risk for penalizations and rejections, were scored proportionally to the benefits obtained.
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectControl Roomen_US
dc.subjectDecision Support Systemsen_US
dc.subjectEffectivenessen_US
dc.subjectSimulatoren_US
dc.subjectMining Geological & Geophysical Engineeringen_US
dc.subjectBlendingen_US
dc.subjectCoalen_US
dc.titleMeasurement of the Effectiveness of a Decision Support System for Blending Control of Large Scale Coal Minesen_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberPoulton, Mary M.en_US
dc.contributor.committeememberMomayez, Moeen_US
dc.contributor.committeememberHead, Larryen_US
dc.contributor.committeememberDessureeault, Sean D.en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineMining Geological & Geophysical Engineeringen_US
thesis.degree.namePh.D.en_US
refterms.dateFOA2018-06-12T09:21:34Z
html.description.abstractLarge opencast coal mines require a complex infrastructure to fulfill production demand and quality values. The distinct specifications required by each customer are achieved by blending adjustments. There is limited control in variability. With only partial information available, operation controllers blend coal by empirical approximation, trying to keep quality between acceptable ranges in order to avoid penalizations, shipment rejections or even contract suspensions. When a decision support system (DSS) centralized in a control room is used for blending control, crew operators visualize enhanced displays of the different sources of information, obtaining a holistic perspective of operations. Using a simulator to reproduce the blending sequence, crew operators can experiment with diverse what-if scenarios and develop blending strategies for an entire working shift, in which they also incorporate their own expertise and the knowledge obtained after interpreting the simulation results. The research focuses on the empirical analysis of the effectiveness of the DSS by studying the performance of crew users in different operating scenarios produced with a simulator. The development of a methodology for measuring this effectiveness and its impact in the quantification of controlling the variability of blending represents a significant contribution in the area of quality improvement for coal production. The effectiveness of the DSS for controlling the blending and load out processes has been numerically measured after experimenting diverse simulated scenarios, proving that the difference between estimated and actual quality delivered is narrower when using a DSS, in comparison with the BTU variability obtained from historical data. The strategies that produced better results in terms of control of coal quality variability, maximization of infrastructure utilization, time spent in making decisions and the minimization of risk for penalizations and rejections, were scored proportionally to the benefits obtained.


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