• Energy Research
• Closed Access close
• Restricted close
• Embargo close
• 11. Sustainability close
• 12. Responsible consumption close
• 2. Zero hunger close
• US close
• FR close

In this study, levulinic acid (LA) was produced from rice straw biomass in co-solvent biphasic reactor system consisting of hydrochloric acid and dichloromethane organic solvent. The modified protocol achieved a 15% wt LA yield through the synergistic effect of acid and acidic products (auto-catalysis) and the designed system allowed facile recovery of LA to the organic phase. Further purification of the resulting extractant was achieved through traditional column chromatography, which yielded a high purity LA product while recovering ∼85% wt. Upon charcoal treatment of the resultant fraction generated an industrial grade target molecule of ∼99% purity with ∼95% wt recovery. The system allows the solvent to be easily recovered, in excess of 90%, which was shown to be able to be recycled up to 5 runs without significant loss of final product concentrations. Overall, this system points to a method to significantly reduce manufacturing cost during large-scale LA preparation.

Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

Abstract Building energy performance is often inadequate given design goals. While different types of assessment methods exist, they either do not consider design goals and/or are not general enough to integrate new and innovative energy concepts. Furthermore, existing assessment methods focus mostly on the building and system level while ignoring more detailed data. With the availability and affordability of more detailed measured data, the increased number of measured data points requires a structure to organize these data. This paper presents the Energy Performance Comparison Methodology (EPCM), which enables the identification of performance problems based on a comparison of measured data and simulated data representing design goals. The EPCM is based on an interlinked building object hierarchy that structures the detailed performance data from a spatial and mechanical perspective. This research is developed and tested on multiple case studies that provide real-life context and more generality compared to single case studies.

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.

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.

Abstract For 118 million residential housing units in the U.S., there is currently a gap between the potential energy savings that can be achieved through the use of existing energy efficiency technologies, and the actual level of energy savings realized, particularly for the 37% of housing units that are considered residential rental properties. Additional quantifiable benefits are needed beyond energy savings to help further motivate residential property owners to invest in energy efficiency upgrades. This research focuses on assessing the adoption of energy efficient upgrades in U.S. residential housing and the impact on rental prices. Ten U.S. cities are chosen for analysis; these cities vary in size across multiple climate zones, and represent a diverse set of housing market conditions. Data was collected for over 159,000 rental property listings, their characteristics, and their energy efficiency measures listed in rental housing postings across each city. Following an extensive data quality control process, over thirty different types energy efficient features were identified. The level of adoption was determined for each city, ranging from 5.3% to 21.6%. Efficient lighting and appliances were among the most common, with many features doubling as energy efficient and other desirable aesthetic or comfort improvements. Then using propensity score matching and conditional mean comparison methods, the relative impact on rent charged in each city was calculated, which ranged from a 6% to 14.1% increase in rent for properties with energy efficient features, demonstrating a positive economic impact of these features, particularly for property owners. This was further subdivided into five types of energy efficiency upgrade and three housing types. Single family homes generally demanded higher premiums with energy efficient features, however there was not a consistent pattern across the types of efficient upgrades. The results of this work demonstrate that investment in energy efficient technologies has quantifiable benefits for rental property owners in the U.S. beyond just energy savings. This methodology and results can also be used in other cities and by property owners, utility companies, or others, ultimately encouraging further investment and positive economic impact in residential energy efficiency and in turn improving energy and resource conservation in the building sector.

Abstract An international workshop was held in Richland, Washington, United States of America, in October, 1980 to discuss progress on development of the acid digestion process for treating combustible nuclear waste. The workshop was attended by participants from nine member countries of the Nuclear Energy Agency of the Organization for Economic Cooperation and Development (OECD/NEA). The status of the acid digestion development programs of various countries is discussed in this paper. The acid digestion process has been developed and demonstrated on an engineering scale in several countries and appears to be especially applicable to treatment of combustible wastes containing high levels of transuranic contamination, where there is potential for recovering the transuranic radionuclides. Because the process takes place at a relatively low temperature, the plutonium contained in the residue is in a form that can be readily recovered using standard leaching techniques. The process is adaptable to a wide variety of combustible wastes, such as cellulosics, plastics, rubber materials, and ion exchange resin. While the process has been developed and demonstrated on an engineering scale primarily for transuranic contaminated wastes, the process is also adaptable to beta-gamma wastes such as reactor ion exchange resins.

Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.