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Abstract The Biofuels Directive sets reference values for the quantity of biofuels and other renewable fuels to be placed on the transport market. Biogas from agricultural crops can be used to meet this directive. This paper investigates biogas production for three crop rotations: wheat, barley and sugar beet; wheat, wheat and sugar beet; wheat only. A technical and economic analysis for each crop rotation was carried out. It was found that wheat produces significantly more biogas than either barley or sugar beet, when examined on a weight basis. However sugar beet produces more biogas and subsequently more energy when examined on an area basis. When producing biofuels, land is the limiting factor to the quantity of energy that may be produced. Thus if optimising land then a crop rotation of wheat, wheat and sugar beet should be utilised, as this scenario produced the greatest quantity of energy. This scenario has a production cost of €0.90/mN3, therefore, this scenario is competitive with petrol when the price of petrol is at least €1.09/l (VAT is charged at 21%). If optimising the production costs then a crop rotation of wheat only should be utilised when the cost of grain is less than €132/ton. This scenario has the least production cost at €0.83/mN3, therefore, this scenario is competitive with petrol when the price of petrol is at least €1.00/l. But as this scenario produces the least quantity of biogas, it also produces the least quantity of energy. In comparing with other works by the authors it is shown that a biomethane system produces more energy from the same crops at a cheaper cost than an ethanol system.

Abstract The South Korean government has been actively promoting an educational-facility improvement program as part of its energy-saving efforts. This research seeks to develop a decision support model for selecting the facility expected to be effective in generating energy savings and making the facility improvement program more effective. In this research, project characteristics and electric-energy consumption data for the year 2009 were collected from 6282 elementary schools located in seven metropolitan cities in South Korea. In this research, the following were carried out: (i) a group of educational facilities was established based on electric-energy consumption, using a decision tree; (ii) a number of similar projects were retrieved from the same group of facilities, using case-based reasoning; and (iii) the accuracy of prediction was improved, using the combination of genetic algorithms, the artificial neural network, and multiple regression analysis. The results of this research can be useful for the following purposes: (i) preliminary research on the systematic and continuous management of educational facilities’ electric-energy consumption; (ii) basic research on electric-energy consumption prediction based on the project characteristics; and (iii) practical research for selecting an optimum facility that can more effectively apply an educational-facility improvement program as a decision support model.

Abstract A high heat storage capacity form-stable composite phase change material (CPCM) with enhanced flame retardancy that integrated modified glass fibers with form-stable PCM was proposed. The modified glass fibers were wrapped by a composite flame retardant coating. The thermal and flame retardant properties of the CPCM were measured and compared to other CPCM samples. The results of vertical burning test indicated that the glass fibers improved the mechanical properties of the CPCM and prevented it from fracturing during the burning process. The modified glass fibers could further improve the flame retardancy of CPCM, and V-0 burning rating was achieved while the content of paraffin was maintained at 70 wt%, which means the proportion of flame retardants could be reduced. TGA results showed that the modified glass fibers could enhance the thermal stability and retard the degradation process of the CPCM, and the char residue was increased to 15.3%. Thermal cycling results indicated that the CPCM has good thermal reliability. The results of cone calorimeter test indicated that the peak heat release rate (PHRR) of flame retardant form-stable CPCM dropped by 58.8%, and the combustion rate could be greatly slowed down due to the protection of carbon layers formed by modified glass fibers. In addition, the thermal conductivity of CPCMs were greatly enhanced and the CPCM has good thermal reliability.

Thermal generation is a vital component of mature and reliable electricity markets. As the share of renewable electricity in such markets grows, so too do the challenges associated with its variability. Proposed solutions to these challenges typically focus on alternatives to primary generation, such as energy storage, demand side management, or increased interconnection. Less attention is given to the demands placed on conventional thermal generation or its potential for increased flexibility. However, for the foreseeable future, conventional plants will have to operate alongside new renewables and have an essential role in accommodating increasing supply-side variability.\ud \ud This paper explores the role that conventional generation has to play in managing variability through the sub-system case study of Northern Ireland, identifying the significance of specific plant characteristics for reliable system operation. Particular attention is given to the challenges of wind ramping and the need to avoid excessive wind curtailment. Potential for conflict is identified with the role for conventional plant in addressing these two challenges. Market specific strategies for using the existing fleet of generation to reduce the impact of renewable resource variability are proposed, and wider lessons from the approach taken are identified.

Abstract In recent years, solar steam generation has attracted many attentions due to its potential applications in desalination, etc. In the present work, a bi-layer solar steam generation system is prepared by daubing carbon particles on the sintered sawdust film, which possesses an advantage of adjustable porosities compared to widely used wood. Then, the influence of the porosity on the evaporation performance is explored. The experimental result indicates that: the porosity could significantly affect the water transportation in the film, and the water diffusivity increases almost linearly with the increase of the porosity. The evaporation efficiency increases with the increasing porosity, until the porosity reaches about 0.52 then decrease slowly. The positive effect of the increased water diffusivity and the negative effect of the increased thermal conductivity of the bottom film layer determine that the porosity of 0.52 is optimal for improving the evaporation efficiency. Under a solar light power of 1 kW·m−2, the optimal porosity gives an evaporation efficiency of 77.64%, which is comparable to the best performance of bi-layer systems reported in previous works. The conduction of heat through the bottom layer to the bulk water and the convection heat loss on the top surface contribute 83% to the total heat losses in the system, suggesting that the energy losses of these two modes should be further reduced in the future applications. Considering the accessible materials, easy preparation, low cost and high efficiency, we conclude that the 0.52-porosity system is suitable for being used as an efficient solar steam generation device.

Abstract To enhance prediction accuracy and reduce computation complexity, a decomposition–ensemble methodology with data-characteristic-driven reconstruction is proposed for crude oil price forecasting, based on two promising principles of “divide and conquer” and “data-characteristic-driven modeling”. Actually, this proposed model improves the existing decomposition–ensemble techniques in the “divide and conquer” framework, by formulating and incorporating a data-characteristic-driven reconstruction method based on the “data-characteristic-driven modeling”. Four main steps are involved in the proposed methodology, i.e., data decomposition for simplifying the complex data, component reconstruction based on the “data-characteristic-driven modeling” for capturing inner factors and reducing computational cost, individual prediction for each reconstructed component via a certain artificial intelligence (AI) tool, and ensemble prediction for final output. In the proposed data-characteristic-driven reconstruction, all decomposed modes are thoroughly analyzed to explore the hidden data characteristics, and are accordingly reconstructed into some meaningful components. For illustration and verification, the West Texas Intermediate (WTI) and Brent crude oil spot prices are used as the sample data, and the empirical results indicate that the proposed model statistically outperforms all considered benchmark models (including popular AI single models, typical decomposition–ensemble models without reconstruction, and similar decomposition–ensemble models with other existing reconstruction methods), since it has higher prediction accuracy and less computational time.

Abstract Because of the increased need for virtual analysis during the vehicle manufacturing processes, more stringent optimization methods are required for the simulation field. Owing to the use of big data from engine testing, 1D analysis can provide more powerful approaches in the conceptual design phase for car makers. In this study, system-level optimization of dual-loop EGR was performed at both the engine and vehicle level, whereas our previous research had been performed at the engine level. Depending on the virtually developed engine and vehicle models and control scheme from our previous works, improved driving capabilities could be observed under light-duty diesel vehicle systems under the world-harmonized light-vehicles test procedure (WLTP). The numerical model was extended through two steps. The first step includes model conversion from the base engine model with an HP EGR system to a virtual engine model with a dual-loop EGR system. The second step represents mode extension from the virtual engine model with a dual-loop EGR system to a vehicle model with a dual-loop EGR system. Optimizing the dominant parameters and using design of experiment (DoE)-based multi-objective Pareto optimization methods in each step, fuel economy could be improved by approximately, 1.5% and the deNOX rate was approximately 5% that of the conventional NEDC. It is implied that the dual-loop EGR system and gear strategy could improve vehicle performance under difficult driving conditions.

Abstract Algae have been considered as a promising biodiesel feedstock. One of the major factors affecting large-scale algae technology application is poor wintering cultivation performance. In this study, an integrated approach is investigated combining freshwater microalgae Chlorella zofingiensis wintering cultivation in pilot-scale photobioreactors with artificial wastewater treatment. Mixotrophic culture with the addition of acetic acid (pH-regulation group) and autotrophic culture (control group) were designed, and the characteristics of algal growth, lipid and biodiesel production, and nitrogen and phosphate removal were examined. The results showed that, by using acetic acid three times per day to regulate pH at between 6.8 and 7.2, the total nitrogen (TN) and total phosphate (TP) removal could be increased from 45.2% to 73.5% and from 92.2% to 100%, respectively. Higher biomass productivity of 66.94 mg L−1 day−1 with specific growth rate of 0.260 day−1 was achieved in the pH-regulation group. The lipid content was much higher when using acetic acid to regulate pH, and the relative lipid productivity reached 37.48 mg L−1 day−1. The biodiesel yield in the pH-regulated group was 19.44% of dry weight, with 16–18 carbons as the most abundant composition for fatty acid methyl esters. The findings of the study prove that pH adjustment using acetic acid is efficient in cultivating C. zofingiensis in wastewater in winter for biodiesel production and nutrient reduction.