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Abstract Flow behavior of particles in a two-dimensional spouted bed with a draft tube is studied using a continuous kinetic-friction stresses model. The kinetic stress of particles is predicted from kinetic theory of granular flow, while the friction stress is computed from a model proposed by Johnson et al. (1990) . A stitching function is used to smooth from the rapid shearing viscous regime to the slow shearing plastic regime. The distributions of concentration and velocities of particles are predicted in the spouted bed with a draft tube. Simulated results compare with the vertical velocity of particles ( Zhao et al., 2008 ) measured and in the spout bed with draft plates and solid circulation rate ( Ishikura et al., 2003 ) measured in the spouted bed with a draft tube. The impact of the friction stress of particles on the spout, annulus, fountain and entrancement regions is analyzed in gas–solid spouted bed with a draft tube. Numerical results show that the gas flow rate through the annulus increases with the increase of the entrainment zone. The solids circulation rate decreases with the decrease of inlet gas velocity and the height of the entrainment zone. The effect of spouting gas velocity on distributions of concentration, velocity and particle circulation is discussed.

Abstract In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the e -insensitive loss function, admits of the residual within the boundary values of e -tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.

Short term load forecasting plays an increasingly important role in Smart Grid. Short term load forecasting is also an important part of enterprise power system management. Providing accurate load time series data for a certain period of time in the future can enable enterprises to ensure the smooth operation of production while making a reasonable power plan, reducing power consumption and basic electricity charges, thus reducing the production cost of enterprises. In addition, lower electricity consumption means lower carbon dioxide emissions, which has far-reaching implications for sustainable development strategies. This paper presents a short-term load forecasting method based on time series. The model divides the time series data into four parts: trend item, period item, holiday item and error item. In the experiment part, this paper provides a set of preprocessing method flow. Aiming at the problem that the sampling rate of the current smart grid data is not constant, a data smoothing algorithm is proposed.

Abstract The central role of occupants for achieving energy savings in residential buildings is increasingly recognised. Simulation programmes able to take into account occupant behaviour are considered to be powerful tools for bridging the gap between the predicted and the actual energy consumption for new buildings. Nevertheless, the majority of residential buildings that will constitute the housing stock in 2050 have already been built today, therefore occupant behaviour and building simulation tools need to be fully exploited for supporting the renovation of existing housing stock. The aim of this paper is to explore the role of occupant behaviour modelling in supporting decision-makers dealing with the design of renovation strategies for residential buildings. An Italian multi-family public housing building is assumed as case study to estimate the influence of three dimensions linked with occupant behaviour – management of the thermostat, management of the heating system, variation of building characteristics – on energy heating consumption. The results show that, while the occupant behaviour influences the heating loads up to 1/3 in case of high level of building retrofit, the less the building is renovated, the higher is the behavioural impact in absolute terms of energy reduction. Therefore, in order to be effective, renovation strategies are required to design appropriate informative instruments at an early stage to support behaviour changes towards responsible energy consumption.

Abstract The combination of biomass gasification with fuel cells, especially high temperature Solid Oxide Fuel Cells (SOFCs) promises sustainable and highly efficient (decentralized and modular) energy conversion systems. This review encompasses the components of biomass integrated gasification–SOFC technology including biomass characteristics, the thermochemical conversion in gasifiers and the factors affecting the gasification process, the cleaning technologies for raw producer gas and its conditioning and finally the integration of gasifier with SOFCs. The influence of impurities present in biomass producer gas such as particulates, tar, H 2 S, HCl and alkali compounds based on recent experimental studies and their tolerance limits towards SOFCs are presented. Even though analysis based on the probable tolerance limits of impurities towards SOFCs and a comprehensive overview of the cleaning technologies for producer gas impurities indicate that producer gas cleaning at various temperatures using current technologies to meet SOFC requirements is possible, more experimental studies are still needed to acquire the detailed information on the tolerance limits of impurities for SOFCs. The recent theoretical modeling and experimental studies of biomass integrated gasification–SOFC systems are also presented.

Abstract This paper investigates the performance of a variable ratio gearbox (VRG) used in a small fixed-speed wind turbine with active blades. The major components of the VRG-enabled drivetrain are an automatic-manual gearbox and squirrel cage induction generator that connects directly to the grid. The simplicity of this system may be appealing for applications when cost and reliability are of concern. It is an alternative to variable speed systems, which necessitate a modified generator and power conditioning equipment. During partial load operation the VRG provides a discrete set of rotor speeds. This allows the controller to track the wind speed and to achieve a greater efficiency. This study suggests three VRG ratios are sufficient to improve performance when used with active blades. A case study is presented where the performance is simulated using three different wind data sets. The study suggests that the VRG can improve production between 7 and 8.5% in low wind areas. The design procedure also illustrates a technique for finding the lowest and highest gear ratios needed for VRG design. These ratios allow the system to achieve the lowest cut-in and rated speeds. The approach also has useful implications for the design of a continuously variable transmission.

In order to improve the accuracy of the battery state of charge (SOC) estimation, in this paper we take a lithium-ion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate. Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.

Consumer interest in food products, including fresh vegetables, with health promoting properties is rising. In fresh vegetables, these properties include vitamins, minerals, dietary fiber, and secondary compounds, which collectively impart a large portion of the dietary, nutritional or health value associated with vegetable intake. Many, including farmers, aim to increase the health-promoting properties of fresh vegetables on the whole but they face at least three obstacles. First, describing crop composition in terms of its nutrition-based impact on human health is complex and there are few, if any, accepted processes and associated metrics for assessing and managing vegetable composition on-farm, at the origin of supply. Second, data suggest that primary and secondary metabolism can be 'in conflict' when establishing the abundance versus composition of a crop. Third, fresh vegetable farmers are rarely compensated for the phytochemical composition of their product. The development and implementation of a fresh vegetable 'nutritional yield' index could be instrumental in overcoming these obstacles. Nutritional yield is a function of crop biomass and tissue levels of health-related metabolites, including bioavailable antioxidant potential. Data from a multi-factor study of leaf lettuce primary and secondary metabolism and the literature suggest that antioxidant yield is sensitive to genetic and environmental production factors, and that changes in crop production and valuation will be required for fresh vegetable production systems to become more focused and purposeful instruments of public health.