Adaptive Neuro-Fuzzy Inference Systems (ANFIS) - Based Model Predictive Control (MPC) for Carbon Dioxide Reforming of Methane (CDRM) in a Plug Flow Tubular Reactor for Hydrogen Production

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dc.contributor.advisorIbrahim, Hussameldin
dc.contributor.advisorMehrandezh, Mehran
dc.contributor.advisorIdem, Raphael
dc.contributor.authorEssien, Ememobong Ita
dc.contributor.committeememberShirif, Ezeddin
dc.contributor.committeememberdeMontigny, David
dc.contributor.externalexaminerAzam, Shahid
dc.date.accessioned2013-10-30T18:46:56Z
dc.date.available2013-10-30T18:46:56Z
dc.date.issued2013-01
dc.descriptionA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Industrial Systems Engineering, University of Regina. xvii, 134 l.en_US
dc.description.abstractThe current sources of our energy supply are plagued with many problems, and the impact on the climate is of grave concern. To preserve and sustain our environment, a non-polluting and renewable energy source is required. Hydrogen (H2), when extracted from one of its many sources during carbon dioxide (CO2) capture, is considered a non-polluting, efficient and environmentally sustainable energy source. In this research work, the control of a pilot-scale reformer for the production of hydrogen was studied. Hydrogen was produced through the carbon dioxide reforming of methane (CDRM). This process was used to convert methane (CH4) and carbon dioxide into hydrogen. A high methane conversion was maintained by controlling the temperature in the reformer at the thermodynamically desired level. The control strategy applied to this process was the model predictive control (MPC) based on an adaptive neuro-fuzzy inference system (ANFIS) model. MPC has, among other advantages, the ability to predict the response of the system over a given prediction horizon. Experimental results showed that the ANFIS model was able to accurately replicate the response of the process to changes in temperature. Based on the ANFIS model, an MPC strategy was formulated for the process.en_US
dc.description.authorstatusStudenten
dc.description.peerreviewyesen
dc.identifier.tcnumberTC-SRU-3788
dc.identifier.thesisurlhttp://ourspace.uregina.ca/bitstream/handle/10294/3788/Essien_Ememobong_Ita_200281379_MASC_ISE_Spring2013.pdf
dc.identifier.urihttps://hdl.handle.net/10294/3788
dc.language.isoenen_US
dc.publisherFaculty of Graduate Studies and Research, University of Reginaen_US
dc.subject.lcshHydrogen as fuel--Research
dc.subject.lcshMethane--Synthesis
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshFuzzy systems
dc.subject.lcshPredictive control
dc.subject.lcshChemical reactors
dc.titleAdaptive Neuro-Fuzzy Inference Systems (ANFIS) - Based Model Predictive Control (MPC) for Carbon Dioxide Reforming of Methane (CDRM) in a Plug Flow Tubular Reactor for Hydrogen Productionen_US
dc.typeThesisen
thesis.degree.departmentFaculty of Engineering and Applied Scienceen_US
thesis.degree.disciplineEngineering - Industrial Systemsen_US
thesis.degree.grantorUniversity of Reginaen
thesis.degree.levelMaster'sen
thesis.degree.nameMaster of Applied Science (MASc)en_US
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