Design of Organic Rankine Cycle (ORC) Systems Using Knowledge-Based Approaches

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dc.contributor.advisor Yu, W en
dc.contributor.advisor Young, B en Dong, Shoulong en 2020-04-08T19:49:33Z en 2020 en
dc.identifier.uri en
dc.description.abstract The continual increase in global energy consumption has resulted in a series of consequences, such as energy shortage, fuel price growth as well as severe environmental issues. The organic Rankine cycle (ORC) has attracted growing interest and has been considered to be a promising technology for converting low-grade heat sources into electricity, such as industrial waste heat recovery, geothermal power, biomass and solar thermal energy. Although a few suppliers dominate the ORC international market, New Zealand holds considerable potential in this niche because of its strong history of geothermal developments. Therefore, an industry-led research and development initiative, AGGAT, was proposed and championed by the Heavy Engineering Research Association (HERA). This program was devoted to providing a readily available platform for its membership to facilitate relevant research and development of low enthalpy geothermal and waste heat recovery field. As an essential part of the AGGAT program, my work focused on exploring new design methodologies to facilitate the design of ORC systems, especially knowledge-based approaches. Based on comprehensive investigations and gradually deepening into the ORC design field, some tangible contributions have been achieved. Firstly, a detailed literature review was conducted to examine state-of-the-art ORC design methodologies with a particular emphasis on computer-aided design (CAD) methods and tools. It covered four technical aspects, namely, computer-aided molecular design (CAMD), modeling and simulation tools, optimization-based approaches, and knowledge-based approaches. These techniques could significantly facilitate the development of ORC systems. On top of that, new research trends were pointed out in order to pursue a more efficient and intelligent ORC design. Secondly, a new online Expert Design Tool (EDT) was proposed and implemented to perform process design and performance assessment for ORC systems at the early conceptual stage of design. Being a critical deliverable of the AGGAT program, the EDT was well structured and developed due to software integration of seven kinds of software or database. It combined all initial design considerations into one online platform and could produce comprehensive information related to ORC process design and analysis. Ten modules were developed on the basis of the expertise and judgment of experts in the AGGAT program. A preliminary EDT II version was deployed online. The EDT was validated against a real ORC plant, and the results proved that the online EDT was easy to use and could generate reliable information related to ORC design to support designers or researchers decision-making process. Thirdly, a new case-based reasoning (CBR) approach was proposed to make the best use of existing ORC plant designs. The complete workflow was formulated into essential processes including problem analysis, case representation, case base establishment, similarity measure, attribute weighting, and basic CBR steps. In addition, in order to further improve the performance of standalone CBR-based approach, a new hybrid intelligent approach combining rule-based reasoning (RBR) and CBR was proposed. It was hypothesized and proved that these two intelligent knowledge-based approaches could achieve improved design schemes with noticeable increases in ORC performance. These approaches were proved to be feasible and effective in making the best use of the expertise of designers and existing ORC plant designs systematically, to support decision-making and facilitate the development of ORC systems. It is worth noting that the significant contribution of this thesis was to apply artificial intelligence (AI) technologies to the ORC design field, i.e., the CBR-based approach and the hybrid intelligent approach combing RBR and CBR. Such AI techniques have the potential to transform the ORC design domain. en
dc.publisher ResearchSpace@Auckland en
dc.relation.ispartof PhD Thesis - University of Auckland en
dc.relation.isreferencedby UoA en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
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dc.title Design of Organic Rankine Cycle (ORC) Systems Using Knowledge-Based Approaches en
dc.type Thesis en Chemical and Materials Engineering en The University of Auckland en Doctoral en PhD en
dc.rights.holder Copyright: The author en
dc.rights.accessrights en
pubs.elements-id 797678 en
pubs.record-created-at-source-date 2020-04-09 en

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