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dc.contributor.advisorMujtaba, Iqbal M.
dc.contributor.advisorKara-Zaitri, Chakib
dc.contributor.authorAl-Obaidi, Mudhar A.A.R.
dc.date.accessioned2019-10-25T08:32:28Z
dc.date.available2019-10-25T08:32:28Z
dc.identifier.urihttp://hdl.handle.net/10454/17345
dc.description.abstractReverse Osmosis (RO) is a membrane-based separation process applied in several industrial and food processing applications. In this research, performance of RO process is investigated in respect of two applications (a) wastewater treatment (b) concentration fruit juices using model-based techniques. For this purpose, a number of models (both 1 and 2-dimensional steady state and dynamic) for spiral wound RO process are developed based on Solution-Diffusion model and Irreversible Thermodynamic model. The models are validated against actual experimental data reported in the literature before being used in further simulation and optimisation studies for both wastewater treatment and fruit juice concentration. Wastewater effluents of many industrial applications contain a variety of micro-pollutants and highly-toxic compounds, which are released into a variety of water resources. Such pollutants not only disrupt the biological ecosystem, but they also pose a real threat to the water supply for human consumption and to the aquatic ecosystems. The earlier chapters of the thesis evaluate the performance of RO process in terms of removal efficiency of toxic compounds such as chlorophenol, N-nitrosamine, etc. from wastewater. The effect of several operating parameters such as feed pressure, concentration, flow rate and temperature, on the performance of RO process are evaluated. Also, suitability of a number of different RO configurations for efficient removal of toxic compounds are evaluated. For example, (a) two-stage/two-pass RO design synthesis of RO network for the removal of chlorophenol (b) multistage multi-pass RO process with and without energy recovery option for the removal of N-nitrosamine are investigated. The dynamic response of the RO process for step changes in the operating parameters is investigated for the removal of phenolic compounds. Finally, in the context of wastewater treatment, a case study with multi compounds contaminants is suggested where a multi-objective optimisation problem has achieved the optimum rejection of all the compounds and recovery rate. In respect of food processing, RO has been considered as a prominent process in fruit juice concentration due to its ability to effectively retain the flavour, sensory, aroma and nutritional characteristics and concentrate the juice. This research elucidates one example of apple juice concentration process and focuses on highlighting successful modelling and optimisation methodology. This in turn provides an efficient method of RO process for concentrating apple juice by improving the reliability and efficiency of the underlying separation and concentration process.en_US
dc.description.sponsorshipMinistry of High Education and Scientific Research of Iraqen_US
dc.language.isoenen_US
dc.publisherUniversity of Bradforden_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectWastewateren_US
dc.subjectSpiral wound moduleen_US
dc.subjectDistributed modellingen_US
dc.subjectOptimisationen_US
dc.subjectParameter estimationen_US
dc.subjectEnergy consumptionen_US
dc.subjectPhenolic and N-nitrosamineen_US
dc.subjectApple juice concentrationen_US
dc.subjectSimulationen_US
dc.titleModelling, Simulation, and Optimisation of Reverse Osmosis Process with Application in Wastewater Treatment and Food Processingen_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentFaculty of Engineering and Informaticsen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2018
refterms.dateFOA2019-10-25T08:32:28Z


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