Organic Rankine Cycle Control using Neural Networks and Variable Structure Control

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atmire.migration.oldid5044
dc.contributor.advisorPieper, Jeff
dc.contributor.advisorMacnab, Chris
dc.contributor.authorBadkoubeh Hezaveh, Babak
dc.contributor.committeememberGoldsmith, Peter
dc.contributor.committeememberSun, Qiao
dc.contributor.committeememberTrifkovic, Milana
dc.date.accessioned2016-10-04T19:33:13Z
dc.date.available2016-10-04T19:33:13Z
dc.date.issued2016
dc.date.submitted2016en
dc.description.abstractThis research work proposes an adaptive optimal Lyapunov control for a Trans-critical Organic Rankine Cycle (TORC) to address disturbance rejection problem. TORC is a thermodynamic cycle for converting low temperature heat into electrical power where the system operates with an organic fluid with low boiling temperature at supercritical or trans-critical conditions. This thermodynamic cycle is similar to the traditional steam Rankine cycle, but it is capable of recovering low grade heat into useful power. The control-oriented challenge of utilizing this system at nominal efficiency is that low grade heat resources have a fluctuating nature which does not allow the heat recovery system operates at nominal operating conditions. In this regards, a control-oriented nonlinear model of TORC system is developed, and an Adaptive Variable Structure Control (AVSC) using Cerebellar Model Articulation Controller (CMAC) neural networks is proposed to maintain the system at nominal operating conditions while rejecting the disturbances. The variable structure control is a well known robust control strategy suitable for this application where CMAC neural network method integrates into the control such that the final control is not only robust but also adaptive to the disturbances.en_US
dc.identifier.citationBadkoubeh Hezaveh, B. (2016). Organic Rankine Cycle Control using Neural Networks and Variable Structure Control (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/27225en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/27225
dc.identifier.urihttp://hdl.handle.net/11023/3402
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectEngineering--Mechanical
dc.subject.classificationOrganic Rankine Cycle Controlen_US
dc.subject.classificationAdaptive Variable Structure Controlen_US
dc.subject.classificationCMACen_US
dc.subject.classificationAdaptive Sliding Mode Controlen_US
dc.subject.classificationAdaptive Robust Optimal Controlen_US
dc.subject.classificationTrans-critical Organic Rankine Cycleen_US
dc.subject.classificationNeural Networksen_US
dc.titleOrganic Rankine Cycle Control using Neural Networks and Variable Structure Control
dc.typemaster thesis
thesis.degree.disciplineMechanical and Manufacturing Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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