Internal Pressure & Free Volume of Potassium Chloride Solutions in Water- Dimethylformamide Mixtures

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Synthesis, Characterization and Activity of Sulphate-modified V2O5/SnO2 Catalysts

Sulphate-modified V2O5/SnO2 catalysts were prepared by a simple impregnation method and characterized using different physicochemical techniques, such as EDX, BET-SA, XRD, FT-IR, TGA and 51V NMR spectroscopy. A simple, effective and environmentally friendly method for the gas phase conversion of cyclohexanone oxime to ε-caprolactam by these modified catalysts is presented. The optimal protocol allows ε-caprolactam to be synthesized in excellent yields. NH3-TPD and cumene conversion reactions were used to determine the acid structural properties of the catalysts. Definite correlation was observed between the concentration of medium strength acid sites or Brønsted sites and the ε-caprolactam selectivity. Time-on-stream studies showed fast decline in the activity of the catalyst resulting fromthe basic nature of the reactant and product molecules.Keywords: Sulphated V2O5/SnO2 catalysts, acidity, vapour phase Beckmann rearrangemen

Novel genetic algorithm for scheduling of appliances

YesThe introduction of smart metering has brought more detailed information on the actual load profile of a house. With the ability to measure, comes the desire to control the load profile. Furthermore, advances in renewable energy have made the consumer to become supplier, known as Prosumer, who therefore also becomes interested in the detail of his load, and also his energy production. With the lowering cost of smart plugs and other automation units, it has become possible to schedule electrical appliances. This makes it possible to adjust the load profiles of houses. However, without a market in the demand side, the use of load profile modification techniques are unlikely to be adapted by consumers on the long term. In this research, we will be presenting work on scheduling of energy appliances to modify the load profiles within a market environment. The paper will review the literature on algorithms used in scheduling of appliances in residential areas. Whilst many algorithms presented in the literature show that scheduling of appliances is feasible, many issues arise with respect to user interaction, and hence adaptation. Furthermore, the criteria used to evaluate the algorithms is often related only to reducing energy consumption, and hence CO2. Whilst this a key factor, it may not necessarily meet the demands of the consumer. In this paper we will be presenting work on a novel genetic algorithm that will optimize the load profile while taking into account user participation indices. A novel measure of the comfort of the customer, derived from the standard deviation of the load profile, is proposed in order to encourage the customer to participate more actively in demand response programs. Different scenarios will also be tested.This work was supported by the British Council and the UK Department of Business Innovation and Skills under GII funding for the SITARA project

Investigating the impact of discomfort in load scheduling using genetic algorithm

YesEnergy consumers oftentimes suffer some element of discomfort associated with the implementation of demand response programs as they aim to follow a suggested energy consumption profile generated from scheduling algorithms for the purpose of optimizing grid performance. This is because people naturally do not like to be told what to do or when to use their appliances. Although advances in renewable energy have made the consumer to also become energy supplier, who can actively cash in at times of the day when energy cost is high to either sell excess energy generated or consume it internally if required, thereby nullifying the adverse effect of this discomfort. But a majority of consumers still rely wholly on the supply from the grid. This impact on users' comfort who are active participants in demand response programs was investigated and ways to minimizing load scheduling discomfort was sought in order to encourage user participation

Investigation of an optimized energy resource allocation algorithm for a community based virtual power plant

YesRecently, significant advances in renewable energy generation have made it possible to consider consumers as prosumers. However, with increase in embedded generation, storage of electrical energy in batteries, flywheels and supercapacitors has become important so as to better utilize the existing grid by helping smooth the peaks and troughs of renewable electricity generation, and also of demand. This has led to the possibility of controlling the times when stored energy from these storage units is fed back to the grid. In this paper we look at how energy resource sharing is achieved if these storage units are part of a virtual power plant. In a virtual power plant, these storage units become energy resources that need to be optimally scheduled over time so as to benefit both prosumer and the grid supplier. In this paper, a smart energy resources allocation algorithm is presented for a virtual power plants using genetic algorithms. It is also proposed that the cause of battery depreciation be accounted for in the allocation of discharge rates. The algorithm was tested under various pricing scenarios, depreciation cost, as well as constraint. The results are presented and discussed. Conclusions were drawn, and suggestion for further work was made.Mr. Oghenovo Okpako is grateful for the support of the Niger Delta Development Commission of Nigeria for supporting the work. The work has been also supported by the British Council and the UK Department of Business innovations and Skills under the GII funding of the SITARA project

Evaluation of community virtual power plant under various pricing schemes

YesTechnological advancement on the electricity grid has focused on maximizing its use. This has led to the introduction of energy storage. Energy storage could be used to provide both peak and off-peak services to the grid. Recent work on the use of small units of energy storage like battery has proposed the vehicle to grid system. It is propose in this work to have energy storage device embedded inside the house of the energy consumer. In such a system, consumers with battery energy storage can be aggregated in to a community virtual power plant. In this paper, an optimized energy resource allocation algorithm is presented for a virtual power plant using genetic algorithm. The results show that it is critical to have a pricing scheme that help achieve goals for grid, virtual power plant, and consumers.Mr. Oghenovo Okpako is grateful to the Niger Delta Development Commission of Nigeria for funding the work. The work has been also supported by the British Council and the UK Department of Business innovations and Skills under the GII funding of the SITARA project

Electric Field Assisted Self-Healing of Open Circuits with Conductive Particle-Insulating Fluid Dispersions: Optimizing Dispersion Concentration

Abstract: Open circuit faults in electronic systems are a common failure mechanism, particularly in large area electronic systems such as display and image sensor arrays, flexible electronics and wearable electronics. To address this problem several methods to self heal open faults in real time have been investigated. One approach of interest to this work is the electric field assisted self-healing (eFASH) of open faults. eFASH uses a low concentration dispersion of conductive particles in an insulating fluid that is packaged over the interconnect. The electric field appearing in the open fault in a current carrying interconnect polarizes the conductive particles and chains them up to create a heal. This work studies the impact of dispersion concentration on the heal time, heal impedance and cross-talk when eFASH is used for self-healing. Theoretical predictions are supported by experimental evidence and an optimum dispersion concentration for effective self-healing is identified

Quantitative CT: Associations between Emphysema, Airway Wall Thickness and Body Composition in COPD

The objective of the present study was to determine the association between CT phenotypes—emphysema by low attenuation area and bronchitis by airway wall thickness—and body composition parameters in a large cohort of subjects with and without COPD. In 452 COPD subjects and 459 subjects without COPD, CT scans were performed to determine emphysema (%LAA), airway wall thickness (AWT-Pi10), and lung mass. Muscle wasting based on FFMI was assessed by bioelectrical impedance. In both the men and women with COPD, FFMI was negatively associated with %LAA. FMI was positively associated with AWT-Pi10 in both subjects with and without COPD. Among the subjects with muscle wasting, the percentage emphysema was high, but the predictive value was moderate. In conclusion, the present study strengthens the hypothesis that the subgroup of COPD cases with muscle wasting have emphysema. Airway wall thickness is positively associated with fat mass index in both subjects with and without COPD