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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 With promising benefits such as traffic emission reduction, traffic congestion alleviation, and parking problem solving, Electric Vehicle (EV)-sharing systems have attracted large attentions in recent years. Different from other business modes, customers in sharing economy systems are usually price sensitive. Therefore, it is possible to shift the usage of shared EVs through a well-designed Dynamic Pricing Scheme (DPS), with the objective of maximizing the system operator's total profit. In this study, we propose a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction. The proposed DPS is formulated as a complex optimization problem, which includes two Price Adjustment Level (PAL) decision variables for every origin-destination pair of stations. The two PALs are employed to affect the EV-sharing demand and travel time between each station pair, respectively. Physical and operational constraints from both EV demand and VGI service aspects are also included in the proposed model. Two case study are conducted to validate the effectiveness of the proposed method.

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.

The Houston-Dallas (I-45) corridor is the busiest route among 18 traffic corridors in Texas, USA. The expected population growth and the surge in passenger mobility may result in a significant impact on the regional environment. This study uses a life cycle framework to predict and evaluate the net changes of environmental impact associated with the potential development of a high-speed rail (HSR) System along the I-45 corridor through its life cycle. The environmental impact is estimated in terms of CO2 and greenhouse gas (GHG) emissions per vehicle/passenger-kilometers traveled (V/PKT) using life cycle assessment. The analyses are performed referring to the Ecoinvent 3.4 inventory database through the phases: material extraction and processing, infrastructure construction, vehicle manufacturing, system operation, and end of life. The environmental benefit is evaluated by comparing the potential development of the HSR system with those of the existing transportation systems. The vehicle component, especially operation and maintenance of vehicles, is the primary contributor to the total global warming potential with about 93% of the life cycle GHG emissions. For the infrastructure component, 56.76% of GHG emissions result from the material extraction and processing phase (23.75 kgCO2eq/VKT). Various life cycle emissions of HSR except PM are significantly lower than for passenger cars.

The operation of a building connects the building, its performance and its end-users. When a mismatch occurs between users' expectations and operations processes, then complaints can arise. The use of enhanced complaint handling processes is investigated to ascertain whether this can help diagnose performance problems. Two LEED Platinum-rated office buildings form the basis for a case study of the components that make up an enhanced complaint handling process. Some of the social dynamics of complaints in buildings are consequently discussed. It is found that an enhanced complaint handling approach could contribute to a form of ongoing commissioning that goes beyond primarily reactive or dismissive treatments of complaints. Le fonctionnement d'un immeuble etablit des rapports entre l'immeuble, ses performances et ses utilisateurs finaux. Lorsqu'un decalage se produit entre les attentes des utilisateurs et les processus d'exploitation, des plaintes peuvent alors s'elever. L'utilisation de processus ameliore...

We assessed the physical, chemical and toxicological characteristics of particulate emissions from four light-duty gasoline direct injection vehicles when operated over the LA92 driving cycle. Our results showed that particle mass and number emissions increased markedly during accelerations. For three of the four vehicles tested, particulate matter (PM) mass and particle number emissions were markedly higher during cold-start and the first few accelerations following the cold-start period than during the hot running and hot-start segments of the LA92 cycle. For one vehicle (which had the highest emissions overall) the hot-start and cold-start PM emissions were similar. Black carbon emissions were also much higher during the cold-start conditions, indicating severe fuel wetting leading to slow evaporation and pool burning, and subsequent soot formation. Particle number concentrations and black carbon emissions showed large reductions during the urban and hot-start phases of the test cycle. The oxidative potential of PM was quantified with both a chemical and a biological assay, and the gene expression impacts of the PM in a macrophage model with PCR (polymerase chain reaction) and ELISA (enzyme-linked immunosorbent assay) analyses. Inter- and intra-vehicle variability in oxidative potential per milligram of PM emitted was relatively low for both oxidative assays, suggesting that real-world emissions and exposure can be estimated with distance-normalized emission factors. The PCR response from signaling markers for oxidative stress (e.g., NOX1) was greater than from inflammatory, AhR (aryl hydrocarbon receptor), or MAPK (mitogen-activated protein kinase) signaling. Protein production associated with inflammation (tumor necrosis factor alpha-TNFα) and oxidative stress (HMOX-1) were quantified and displayed relatively high inter-vehicle variability, suggesting that these pathways may be activated by different PM components. Correlation of trace metal concentrations and oxidative potential suggests a role for small, insoluble particles in inducing oxidative stress.

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.