An energy-efficient route to the rapid synthesis of organically-modified SBA-15 via ultrasonic template removal

A low energy route for the removal of Pluronic P123 surfactant template during the synthesis of SBA-15 mesoporous silicas is explored. The conventional reflux of the hybrid inorganic-organic intermediate formed during co-condensation routes to Pr-SOH-SBA-15 is slow, utilises large solvent volumes, and requires 24 h to remove ∼90% of the organic template. In contrast, room temperature ultrasonication in a small methanol volume achieves the same degree of template extraction in only 5 min, with a 99.9% energy saving and 90% solvent reduction, without compromising the textural, acidic or catalytic properties of the resultant Pr-SOH-SBA-15. © 2014 The Royal Society of Chemistry.

Host and network optimizations for performance enhancement and energy efficiency in data center networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.

Energy-efficient self -organization of wireless acoustic sensor networks for ground target tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.

Effective energy conservation and management in the building sector : the answer to the energy predicament

Modern civilization has developed principally through man's harnessing of forces. For centuries man had to rely on wind, water and animal force as principal sources of power. The advent of the industrial revolution, electrification and the development of new technologies led to the application of wood, coal, gas, petroleum, and uranium to fuel new industries, produce goods and means of transportation, and generate the electrical energy which has become such an integral part of our lives. The geometric growth in energy consumption, coupled with the world's unrestricted growth in population, has caused a disproportionate use of these limited natural resources. The resulting energy predicament could have serious consequences within the next half century unless we commit ourselves to the philosophy of effective energy conservation and management. National legislation, along with the initiative of private industry and growing interest in the private sector has played a major role in stimulating the adoption of energy-conserving laws, technologies, measures, and practices. It is a matter of serious concern in the United States, where ninety-five percent of the commercial and industrial facilities which will be standing in the year 2000 - many in need of retrofit - are currently in place. To conserve energy, it is crucial to first understand how a facility consumes energy, how its users' needs are met, and how all internal and external elements interrelate. To this purpose, the major thrust of this report will be to emphasize the need to develop an energy conservation plan that incorporates energy auditing and surveying techniques. Numerous energy-saving measures and practices will be presented ranging from simple no-cost opportunities to capital intensive investments.

Energy-Efficient Self-Organization of Wireless Acoustic Sensor Networks for Ground Target Tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.

Performance assessment of earth pipe cooling system for low energy buildings in a subtropical climate

Energy consumption in heating and cooling around the world has been a major contributor to global warming. Hence, many studies have been aimed at finding new techniques to save and control energy through energy efficient measures. Most of this energy is used in residential, agricultural and commercial buildings. It is therefore important to adopt energy efficiency measures in these buildings through new technologies and novel building designs. These new building designs can be developed by employing various passive cooling systems. Earth pipe cooling is one of these which can assist to save energy without using any customary mechanical units. This paper investigates the earth pipe cooling performance in a hot humid subtropical climate of Rockhampton, Australia. A thermal model is developed using ANSYS Fluent for measuring its performance. Impacts of air velocity, air temperature, relative humidity and soil temperature on room cooling performance are also assessed. A temperature reduction of around 2 °C was found for the system. This temperature reduction contributed to an energy saving of a maximum of 866.54 kW (8.82%) per year for a 27.23 m3 room.

Adaptive energy-efficient qos-aware scheduling algorithm for TCP/IP mobile cloud

The expected pervasive use of mobile cloud computing and the growing number of Internet data centers have brought forth many concerns, such as, energy costs and energy saving management of both data centers and mobile connections. Therefore, the need for adaptive and distributed resource allocation schedulers for minimizing the communication-plus-computing energy consumption has become increasingly important. In this paper, we propose and test an efficient dynamic resource provisioning scheduler that jointly minimizes computation and communication energy consumption, while guaranteeing user Quality of Service (QoS) constraints. We evaluate the performance of the proposed dynamic resource provisioning algorithm with respect to the execution time, goodput and bandwidth usage and compare the performance of the proposed scheduler against the exiting approaches. The attained experimental results show that the proposed dynamic resource provisioning algorithm achieves much higher energy-saving than the traditional schemes.

Energy consumption prediction based on time-series models for CPU-intensive activities in the cloud

Due to the increasing energy consumption in cloud data centers, energy saving has become a vital objective in designing the underlying cloud infrastructures. A precise energy consumption model is the foundation of many energy-saving strategies. This paper focuses on exploring the energy consumption of virtual machines running various CPU-intensive activities in the cloud server using two types of models: traditional time-series models, such as ARMA and ES, and time-series segmentation models, such as sliding windows model and bottom-up model. We have built a cloud environment using OpenStack, and conducted extensive experiments to analyze and compare the prediction accuracy of these strategies. The results indicate that the performance of ES model is better than the ARMA model in predicting the energy consumption of known activities. When predicting the energy consumption of unknown activities, sliding windows segmentation model and bottom-up segmentation model can all have satisfactory performance but the former is slightly better than the later.

An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method.

Host and Network Optimizations for Performance Enhancement and Energy Efficiency in Data Center Networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.

Sensible use of primary energy in organic greenhouse production.

This document addresses the direct and indirect use of energy in European organic greenhouse horticulture (OGH) with the aim of reviewing available means for making it more environmental friendly and identifying knowledge gaps that should be addressed to attain this aim. The first observation is that there is no common regulation for energy use in OGH, which is not unexpected, since the need for climatisation is not uniformly distributed in the EU (and outside). Accordingly, the EU directive on organic agriculture does not set limitations on the use of energy, but rather promotes the responsible use of energy and of natural resources. The restrictions and rules of most private standards are slightly more stringent. Some standards have specific restrictions on the amount and sources of energy and/or on the seasonal use of energy for heating. Some standards also address processes that may affect (in)direct energy use, such as cultivation methods, mulching, lighting and growing media or substrates. However, most private standards have no or little restrictions or regulations on energy use. Accordingly, it should not surprise that very little quantitative information is available about energy use in OGH. In the present document we have filled the gaps with data with estimates drawn on energy use in conventional greenhouses. With respect to ongoing research, whereas many of the present research results about energy use and saving in conventional greenhouses are relevant (and also applied) in OGH, little research is devoted to address the energy use that is peculiar to OGH, particularly energy use for humidity control. In short, there are still a lot of knowledge gaps to improve quality and to lower energy use in organic greenhouses. The purpose of this document is a summary of present relevant knowledge about energy use and energy saving and of the perspective for improvement. In particular, the goal is to make an overview on the methods and technologies which can be used to reduce the energy use in OGH. We start from the assumption that methods and technologies that are used for reducing direct and indirect energy in conventional greenhouses can also be applied in organic greenhouses. Research on reducing energy use in conventional greenhouses is also more widely available because the area of conventional greenhouse horticulture is much larger than the area of OGH. When implementing these methods and techniques we should take into account the specific characteristics of organic agriculture like soil-based cultivation, use of organic fertilizers and the limited use of crop protection products. This document is organised as follows: first we report the results of a survey about energy use and relevant standards in the countries participating to the COST action (chapter 1); then we review the energy use for climatisation: heating (chapter 2) and humidity (chapter 3). In chapter 4 we review the available design and management means that would either reduce energy use and/or increase energy use efficiency by increasing productivity of OGH. In chapter 5 we present a short summary of existing information on indirect energy use, that is the energy required to manufacture production means (greenhouse structure and cover, fertilisers, equipment etc.) and for crop protection, particularly steaming, and briefly discuss possible savings. Finally (chapter 6) we review briefly the potential for application of renewable energy sources in OGH.

Optimum utilisation of rolling stocks for iron ore mining industries

In this paper, a generic and flexible optimisation methodology is developed to represent, model, solve and analyse the iron ore supply chain system by integrating of iron ore shipment, stockpiles and railing within a whole system. As a result, an integrated train-stockpile-ship timetable is created and optimised for improving efficiency of overall supply chain system. The proposed methodology provides better decision making on how to significantly improve rolling stock utilisation with the best cost-effectiveness ratio. Based on extensive computational experiments and analysis, insightful and quantitative advices are suggested for iron ore mine industry practitioners. The proposed methodology contributes to the sustainability of the environment by reducing pollution due to better utilisation of transportation resources and fuel.

Organisational obstacles to reducing carbon emissions in Hong Kong

An emerging theme for a nation transiting into a sustainable future is the provision of a low carbon (dioxide) environment. Carbon emission reduction is therefore important for the industry and community as a whole. Buildings contribute immensely to total greenhouse gas emissions, so pragmatic actions need to be taken to cut the amount of carbon emitted by the construction industry. These typically involve strategies such as energy-saving features in the design, construction and operation of building projects. However, a variety of characteristics of the markets and stakeholders involved are suppressing their development. This paper reports on a series of interviews with a variety of Hong Kong construction project participants aimed at identifying the drivers of, and obstacles to, the construction industry's attempts to reduce carbon emissions. The results confirm the main actions currently undertaken are energy efficiency enhancement, green procurement, research and development activities, waste/water management and other technical measures such as the provision of thermal insulation. The majority of the drivers are economical in nature, suggesting that financial aids, and particularly government incentives, are likely to be useful motivators. Also suggested is the increased promotion of the benefits of environmental sustainability to the wider community, in order to alert the general public to the need for reducing the amount of carbon originating from building usage.

Rapid microwave-assisted synthesis of Mn3O4–graphene nanocomposite and its lithium storage properties

A nanocomposite of Mn3O4 wrapped in graphene sheets (GSs) was successfully synthesized via a facile, effective, energy-saving, and scalable microwave hydrothermal technique. The morphology and microstructures of the fabricated GS–Mn3O4 nanocomposite were characterized using various techniques. The results indicate that the particle size of the Mn3O4 particles in the nanocomposite markedly decreased to nearly 20 nm, significantly smaller than that for the bare Mn3O4. Electrochemical measurements demonstrated a high specific capacity of more than 900 mA h g−1 at 40 mA g−1, and excellent cycling stability with no capacity decay can be observed up to 50 cycles. All of these properties are also interpreted by experimental studies and theoretical calculations.

A hierarchical decomposition approach for coordinated dispatch of plug-in electric vehicles

Plug-in electric vehicles (PEVs) are increasingly popular in the global trend of energy saving and environmental protection. However, the uncoordinated charging of numerous PEVs can produce significant negative impacts on the secure and economic operation of the power system concerned. In this context, a hierarchical decomposition approach is presented to coordinate the charging/discharging behaviors of PEVs. The major objective of the upper-level model is to minimize the total cost of system operation by jointly dispatching generators and electric vehicle aggregators (EVAs). On the other hand, the lower-level model aims at strictly following the dispatching instructions from the upper-level decision-maker by designing appropriate charging/discharging strategies for each individual PEV in a specified dispatching period. Two highly efficient commercial solvers, namely AMPL/IPOPT and AMPL/CPLEX, respectively, are used to solve the developed hierarchical decomposition model. Finally, a modified IEEE 118-bus testing system including 6 EVAs is employed to demonstrate the performance of the developed model and method.