Bibliography: leaves 281-330.

Bibliography: leaves 281-330. / xvi, 330 leaves ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Environmental Studies, 2003

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.

Submitted in partial fulfilment of the requirements for the Degree of Masters of Technology: Chemical Engineering, M.L. Sultan Technikon, 1999. / An environmental problem facing the textile industry is the coloured effluent from the dyeing of cellulosic fibres with reactive dyes. Reactive dye loss during dyeing operations is about 10 to 40 %, indicating the need to learn more about the fate of these dyes. Increasing environmental regulations are driving technical innovation to manage this problem. Good analytical techniques for the separation and detection of reactive dyes and their derivatives are necessary for monitoring dye-house effluent, as well as in the optimisation of dye synthesis, purification, formulation and application. / M

羅杜芳. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 210-215) / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in Chinese and English. / Luo Dufang.

褚涓. / "2010年9月". / "2010 nian 9 yue". / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 99-106). / Abstract in Chinese and English. / Chu Juan. / 論文摘要 --- p.i / Abstract --- p.ii / Chapter 第一章 --- 導論 --- p.1 / Chapter 第一節 --- 研究背景與研究問題 --- p.1 / Chapter 第二節 --- 理論文獻綜述 --- p.4 / “碎片化的權威主義´ح與“多元化´ح --- p.5 / 中國環保NGO的政治闡釋 --- p.11 / Chapter 第三節 --- 研究設計 --- p.16 / 研究對象 --- p.16 / 研究意義 --- p.17 / 資料收集 --- p.20 / 文章結構 --- p.20 / Chapter 第二章 --- 水壩政治的制度環境變遷 --- p.21 / Chapter 第一節 --- 水電體制改革：行動者與利益 --- p.23 / 水電管理與投資體制改革 --- p.23 / 水電的規劃與決策 --- p.26 / Chapter 第二節 --- 官僚政治：環保總局分享決策權 --- p.29 / 環保總局的“升遷´ح之路 --- p.29 / 環評制衡水電開發 --- p.30 / Chapter 第三節 --- 公民社會政治：環保NGO結緣江河開發 --- p.33 / 都江堰“保衛戰´ح --- p.34 / 漫灣電站社會影響評估 --- p.36 / Chapter 第三章 --- “保留一條原始生態江´ؤ´ؤ怒江！ ´ح --- p.38 / Chapter 第一節 --- 怒江水電開發政策議題的開端 --- p.38 / 怒江水電項目的由來 --- p.38 / 環保總局發力 --- p.40 / Chapter 第二節 --- 環保NGO的初期動員 --- p.44 / 掀起媒體“風暴´ح --- p.44 / 遊說官員 --- p.47 / 尋求國際社會的支援 --- p.49 / NGO之間的聯盟 --- p.S1 / 深入怒江考察和調研 --- p.52 / 通過兩會提案保護怒江 --- p.54 / Chapter 第三節 --- 總理批示：“慎重研究、科學決策´ح --- p.55 / 政府内部的角力 --- p.55 / 爭論聲中叫停 --- p.57 / Chapter 第四節 --- 社區行動與政府的反應 --- p.59 / 綠色流域走進社區 --- p.59 / 遭到打壓 --- p.62 / Chapter 第四章 --- 新一輪的挑戰與動員 --- p.65 / Chapter 第一節 --- 轉折：總理再次批示 --- p.65 / “傳媒戰´ح受挫與院士考察團訪問怒江 --- p.65 / 地方政府遊說中央 --- p.67 / Chapter 第二節 --- 環保NGO的第二輪動員 --- p.69 / 呼吁知情权与参与权 --- p.69 / 啟動法律工具 --- p.71 / Chapter 第三節 --- 陷入膠著 --- p.72 / 官方的表態 --- p.72 / 環保NGO的後續倡導 --- p.75 / Chapter 第四節 --- 虎跳峽、小南海電站再掀波瀾 --- p.77 / Chapter 第五章 --- 結論：決策開放化的解讀 --- p.80 / 政府的“碎片化´ح與“權威´ح --- p.82 / 利益代表：有序的政治參與官僚包辦 --- p.85 / 公眾參與的“陷阱´ح --- p.92 / 參考文獻 --- p.99 / 附件：訪談對象 --- p.107

As a result of rapid product development, the product life cycle has become shorter, and thus the amount of waste from discarded industrial products has risen dramatically. An awareness of the world???s environmental problems has stimulated researchers to explore the opportunities to reuse, recycle and remanufacture end-of-life products. Disassembly is a systematic approach to separating products into components or subassemblies in order to facilitate recovery of components or materials. However, the full disassembly of a product tends to be unproductive due to technical and cost constraints and product conditions after usage. Therefore, selective disassembly has been introduced as a more practical approach, where only a limited number of disassembly paths that lead to selected parts with recovering potential are considered. This research focuses on the development of a selective disassembly methodology by reversing an assembly sequencing approach. The methodology uses a step-by-step approach to generate a disassembly sequence diagram. This involves listing all the parts within the product, generating a liaison diagram to illustrate part relationships and then establishing precedence rules describing prerequisite actions for each liaison. This is followed by segregating disassembly paths that lead to the removal of selected parts or subassemblies. Then a winnowing process is applied to these paths to eliminate invalid disassembly states and transitions. The last step is to select the optimal disassembly path by using the time requirement as the main selection criterion. In order to shorten the time for carrying out the sequencing process, a javabased program that is capable of performing the first three steps has been created. The program requires three basic inputs in forms of precedence rules, and user-required part (s) and disassembly rules, prescribing which liaison (s) should be done subsequent to a particular liaison. The viability of the methodology and the program is proved through seven case studies conducted on a fishing reel, a single-hole punch, a kettle, an entire washing machine and three washing machine subassemblies. The application of the program allows the users to determine an optimal disassembly sequence in a very short time and with only basic product information as the input.