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The monitoring of a waste separation process in the nuclear power industry is considered. Recent advances in gamma ray emission and electrical impedance tomography mean that it is now feasible to unite these two modalities into a novel dual-modality monitoring method. This paper considers a simple model problem for the identification of a boundary between two distinct waste streams in a semi-continuous rotation separator. The simplicity of the problem affords the opportunity to demonstrate the general feasibility of the approach whilst avoiding unnecessary complications.

Effects of nicotine, administered by continuous infusion via osmotic minipumps, were studied on the operant self-administration of alcohol by rats, using a variable interval (15 s) schedule, and measuring the acquisition, maintenance, extinction and reinstatement of responding for alcohol. Doses of nicotine of 0.25, 1.25 and 7.5 mg/kg/24 h had no significant effects on the maintenance of responding for alcohol, but 5 mg/kg/24 h nicotine resulted in a significant increase in responding on the lever delivering the reward when water was substituted for the alcohol, indicating delayed extinction of responding. During infusion of 2.5 mg/kg/24 h nicotine, responding was significantly greater over the "sucrose-fading" training sessions, during acquisition of responding, when mixtures of alcohol and sucrose were provided as reward. When minipumps infusing 2.5 mg/kg/24 h nicotine were implanted after the alcohol responding had been acquired, the responding for alcohol increase during the first week of nicotine infusion, but corresponding nicotine infusion doses of 0.25, 1.25 and 7.5 had no significant effects. The results indicate that nicotine can increase operant responding for alcohol and this is crucially dependent on the dose of nicotine and the time of testing. The results have implications for the frequently encountered dependence on the combination of alcohol and nicotine.

There is a widely believed myth that replacing the use of fossil fuels largely by renewable forms of energy is, with a possible exception of nuclear power, critically dependent on the development of appropriate new technologies. Accordingly, it is held that decarbonizing straight away is particularly difficult and expensive. There was a time when this idea had an element of reality, but this is no longer the case. Unfortunately, belief in this myth is shared by those in positions of influence. This paper serves to document that this presumed reality no longer holds, although the misconception may have been based on fact in the past. Whilst the survey of the available technology offered concentrates on electricity supply, it also documents that manufacture of synthetic fuels via hydrogen obtained by electrolysis of water and CO2 integrates smoothly with electricity grid stabilization as well as reducing the CO2 content of the atmosphere. The likely price and cost development in the energy market is also reviewed. In addition the role of CCS, in practice mainly capture from the air and industrial processes other than power generation is reviewed against the background of the cost effective generation of electricity by harvesting renewable forms of energy.

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

Abstract Bioenergy crops are one of the renewable energy options available to decarbonise the energy sector in Scotland and help to achieve the overall planned target of 80% reduction in greenhouse gas (GHG) emissions by 2050. A process-based model for poplar and willow developed for simulating the effect of different environmental and management options on growth and biomass yield was used to estimate the GHG abatement potential (GHG-AP) under different crop management options in Scotland. The model results of annual wood yield did not show a strong relation with any of the environmental factors except that of initial soil organic carbon (SOC) content. Increasing plant density and decreasing harvest frequency increased GHG-AP. Application of N-fertilizers at a rate of 50–100 kg N ha−1 resulted in the buildup of carbon in soils with less than 180 Mg C ha−1. However, in soils with greater SOC contents, annual emissions resulting from N fertilizer application were greater than the carbon saving through marginal increases in wood yield and SOC changes. The best management scenario in terms of economic and environmental objectives depends on identifying an optimum plant density based on the site specific conditions with a fertilizer application of 20–100 kg ha−1 y−1 and a five year harvest interval. Even under the best economic scenarios, SRC willow and poplar have a GHG-AP ranging from 9.9 to 11.6 and 8.8–10.0 Mg CO2 eq. ha−1 y−1, respectively. Under the best environmental scenarios this range increases to 10.5- 13.2 and 9–11.1 Mg CO2 eq. ha−1 y−1 for willow and poplar, respectively.

There is a growing market for food producers to supply their goods direct to customers, which has largely been brought about by the growing popularity of web-based ordering. However, the geographical distance between supplier and consumer can be large and this presents problems if temperature-sensitive foods are to be transported. Here we describe a numerical model for the thermal experience of foodstuff packaged in expanded polystyrene boxes in the presence of a gel refrigerant. The model is validated by comparing predicted temperatures with those measured in the laboratory, and the model is then used to investigate the effect that the quantity and placement of gel refrigerant and the ambient temperature have on the period for which maximum food temperature can be maintained below 5°C and 8°C. Results indicate that it is possible to maintain chilled food below these temperatures for up to 24 h, though this period is dependent upon ambient temperature, the manner in which the refrigerant is wrapped around the food and the position of a food item inside the box.

Differences between the expected and the actual performance of buildings have been attributed partly to the influence of occupants; post-occupancy evaluations have thus gained more importance in the last years. There are a variety of methods to evaluate the performance of buildings. However, many of them are expensive, time consuming, intrusive or require expert knowledge, and thus, it has been difficult to embed them in practice. This investigation seeks to categorise the methods according to their purpose and usability to provide feedback. This study aims at linking different types of evaluation methods to different purposes for monitoring buildings. The objective is providing with the information needed to set up and carry out the monitoring of a building in-use. This overview was based on the experience of the authors in a number of non-domestic and domestic case studies, and on literature review. The study shows that the selection of the evaluation methods should be made according to the objective of the evaluation, the depth and nature of the study, the audience of the feedback, and the resources available for the evaluation. The relationship between all these factors have been studied and represented visually for a better comprehension of the methods.

Abstract Novel tariff structures have been introduced by power utilities over the past two decades in order to induce load shifts to achieve the aims of load management strategies. As a result of the availability of reliable communications between suppliers and consumers, new ‘smart’ metering equipment and the re-organization of the UK power supply industry, the potential for dynamic- or spot-price based tariffs has been recognized. In order to ensure the successful introduction of such a pricing scheme, it is important to make use of accurate consumer process models as well as spot-price behavioural models to provide the means by which a consumer would optimize his production schedules in the light of rapidly changing tariffs. This paper builds up consumer models from fundamental process characteristics and applies optimizing techniques incorporating spot-price models in order to develop algorithms which would minimize production costs and contribute towards the philosophy of joint supplier/consumer optimality through spot-pricing.