Eco-efficiency improvements in manufacturing is a controversial subject for researchers, practitioners as well as policy makers. The widely accepted definition of "doing more with less" is not accurate enough to guide the design of improvements that can deliver products in a sustainable way. The outcome of these challenges is evident through significant environmental performance variations across various levels of manufacturing operations. The study is driven by the complexity of manufacturing systems and sought to offer design support for practitioners that aim to improve eco-efficiency. A maturity model has been developed in this work that simulates the influence of manufacturing practices on eco-efficiency. The model takes the form of a maturity grid (PMGE) that overlooks practices at process, management systems and top-management levels and incorporates 15 dimensions of performance overall. Evidence shows that practices tend to evolve from reactive to proactive as manufacturing systems mature and embrace eco- efficiency as a systemic property. It was also found that mature companies achieve improvements in energy and resources by relying on existing internal capabilities. Tools to facilitate research and intervene with practitioners in real-life problems were developed and tested. The researcher combined research findings and tools into a maturity-based method (PMGEM) for eco-efficiency improvements. The method intends to help practitioners plan and design eco- efficiency improvements aligned to existing internal capabilities and adopt a more proactive behaviour to environmental challenges. PMGEM was ultimately applied in two case studies with ultimate goal to help practitioners resolve real-life challenges. The applications were positively commented and encourage further work in this field. The researcher envisages that methods such as PMGEM are deeply needed in manufacturing to support practitioners approach complex concepts such as eco-efficiency. Simplification and decomposition techniques with a clear intended use can facilitate the implementation of ambitious improvement strategies for sustainable development.

Sensor nodes are deployed over sensing fields for the purpose of monitoring certain phenomena of interest. The sensor nodes perform specific measurements, process the sensed data, and send the data to a base station over a wireless channel. The base station collects data from the sensor nodes, analyses this data, and reports it to the users. Wireless sensor networks are different from traditional networks, because of the following constraints. Typically, a large number of sensor nodes need to be randomly deployed and, in most cases, they are deployed in unreachable environments; however, the sensor nodes may fail, and they are subject to power constraints. Energy is one of the most important design constraints of wireless sensor networks. Energy consumption, in a sensor node, occurs due to many factors, such as: sensing the environment, transmitting and receiving data, processing data, and communication overheads. Since the sensor nodes behave as router nodes for data propagation, of the other sensor nodes to the base station, network connectivity decreases gradually. This may result in disconnected sub networks of sensor nodes. In order to prolong the network's lifetime, energy efficient protocols should be designed for the characteristics of the wireless sensor network. Sensor nodes in different regions of the sensing field can collaborate to aggregate the data that they gathered. Data aggregation is defined as the process of aggregating the data from sensor nodes to reduce redundant transmissions. It reduces a large amount of the data traffic on the network, it requires less energy, and it avoids information overheads by not sending all of the unprocessed data throughout the sensor network. Grouping sensor nodes into clusters is useful because it reduces the energy consumption. The clustering technique can be used to perform data aggregation. The clustering procedure involves the selection of cluster heads in each of the cluster, in order to coordinate the member nodes. The cluster head is responsible for: gathering the sensed data from its cluster's nodes, aggregating the data, and then sending the aggregated data to the base station. An adaptive clustering protocol was introduced to select the heads in the wireless sensor network. The proposed clustering protocol will dynamically change the cluster heads to obtain the best possible performance, based on the remaining energy level of sensor nodes and the average energy of clusters. The OMNET simulator will be used to present the design and implementation of the adaptive clustering protocol and then to evaluate it. This research has conducted extensive simulation experiments, in order to fully study and analyse the proposed energy efficient clustering protocol. It is necessary for all of the sensor nodes to remain alive for as long as possible, since network quality decreases as soon as a set of sensor nodes die. The goal of the energy efficient clustering protocol is to increase the lifetime and stability period of the sensor network. This research also introduces a new bidirectional data gathering protocol. This protocol aims to form a bidirectional ring structure among the sensor nodes, within the cluster, in order to reduce the overall energy consumption and enhance the network's lifetime. A bidirectional data gathering protocol uses a source node to transmit data to the base station, via one or more multiple intermediate cluster heads. It sends data through energy efficient paths to ensure the total energy, needed to route the data, is kept to a minimum. Performance results reveal that the proposed protocol is better in terms of: its network lifetime, energy dissipation, and communication overheads.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21108号 / 工博第4472号 / 新制||工||1695(附属図書館) / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 篠原 真毅, 教授 山川 宏, 教授 木本 恒暢 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

This thesis reports on the research undertaken to minimise energy consumption within the production phase of a product lifecycle through modelling, monitoring and improved control of energy use within manufacturing facilities. The principle objective of this research is to develop a framework which integrates energy data at plant and process levels within a manufacturing system so as to establish how much energy is required to manufacture a unit product. The research contributions are divided into four major parts. The first reviews relevant literature in energy trends, related governmental policies, and energy tools and software. The second introduces an Embodied Product Energy framework which categorises energy consumption within a production facility into direct and indirect energy required to manufacture a product. The third describes the design and implementation of a simulation model based on this framework to support manufacturing and design decisions for improved energy efficiency through the use of what-if scenario planning. The final part outlines the utilisation of this energy simulation model to support a Design for Energy Minimisation methodology which incorporates energy considerations within the design process. The applicability of the research concepts have been demonstrated via two case studies. The detailed analysis of energy consumption from a product viewpoint provides greater insight into inefficiencies of processes and associated supporting activities, thereby highlighting opportunities for optimisation of energy consumption via operational or design improvements. Although the research domain for this thesis is limited to the production phase, the flexibility offered by the energy modelling framework and associated simulation tool allow for their employment other product lifecycle phases. In summary, the research has concluded that investment in green sources of power generation alone is insufficient to deal with the rapid rise in energy demand, and has highlighted the paramount importance of energy rationalisation and optimisation within the manufacturing industry.