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The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. The commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

The hydrate-based gas separation for capturing CO2 from flue gas has the characteristics of low energy consumption, simple operation and convenience for the subsequent CO2 storage and utilization. In order to reduce the total cumulative deviation of multi-stage hydration reaction, it is of great importance to establish an accurate thermodynamic model. Based on the vdW–P + CPA model, therefore, we refitted the parameters of the thermodynamic model considering the association between CO2 and H2O. Firstly, the energy parameter α0.5 of H2O and CO2 are developed as the cubic function and the linear function of [1-(T/Tc)0.5], respectively. Then, the calculation parameters of Langmuir absorption coefficient of vdW–P model is refitted based on the temperature dependent binary interaction parameters kij. The following research results are obtained. First, when the novel fitted thermodynamic model is used to predict the density of saturated fluid, the average absolute deviation (AAD) of H2O drops from 1.84% to 0.08% and that of CO2 drops from 4.06% to 2.09%. Second, when it is used to predict the phase equilibrium pressure of the hydrate generated from pure CO2 and pure N2, the AAD is 0.86% and 0.82%, respectively. Third, when it is used to calculate the phase equilibrium condition of hydrate generated from flue gas with different compositions, the AAD is decreased from 15.16% to 5.02%. In conclusion, this novel fitted thermodynamic model is of higher accuracy and it, to some extent, can decrease the total accumulative deviation of multi-stage hydration reaction. The research results provide reference for the actual application of the hydrate-based gas separation for capturing CO2 from flue gas. Keywords: Hydrate-based gas separation, Flue gas, CO2 capture, CO2 thermodynamic model, Multi-stage hydration reaction, Energy parameter, Langmuir absorption coefficient, Prediction

Abstract The promise of redox flow batteries (RFBs) utilizing soluble redox couples, such as all vanadium ions as well as iron and chromium ions, is becoming increasingly recognized for large-scale energy storage of renewables such as wind and solar, owing to their unique advantages including scalability, intrinsic safety, and long cycle life. An ongoing question associated with these two RFBs is determining whether the vanadium redox flow battery (VRFB) or iron-chromium redox flow battery (ICRFB) is more suitable and competitive for large-scale energy storage. To address this concern, a comparative study has been conducted for the two types of battery based on their charge–discharge performance, cycle performance, and capital cost. It is found that: i) the two batteries have similar energy efficiencies at high current densities; ii) the ICRFB exhibits a higher capacity decay rate than does the VRFB; and iii) the ICRFB is much less expensive in capital costs when operated at high power densities or at large capacities.

Uncertainty analysis is useful in determining whether the results of life cycle assessment are sufficiently reliable and valid when making optimal decisions. However, only a few studies have measured carbon emissions by considering the inherent uncertainty during building construction phase that may result in the misinterpretation of critical parameters. To address such weakness, a multi-method-based uncertainty analysis framework was developed in view of the basic characteristics of the construction practice. This framework integrated the deterministic and probabilistic approaches to facilitate the uncertainty assessment in quantifying carbon emissions and to provide insights into the sensitive construction activities from the uncertainty perspective. The developed framework was examined through a mix-use project in Guangzhou China. Results showed that the uncertainties in the measurement method and geographic representativeness are the major uncertainty sources for the building construction phase. The total greenhouse gas emission for the target building was 8791.5 tonnes of carbon dioxide equivalent with a 9.8% coefficient of variation, which was in line with the result calculated by the deterministic method and with the result extrapolated based on the data collected from China. The results of the scenario analysis showed that the proportion of 1% in contribution analysis and the coefficient of variation of 18% in uncertainty analysis can be regarded as the baseline for determining the critical input parameters. This study lends a useful tool for monitoring the uncertainty of LCA studies in the construction practice. In addition, this framework can facilitate to avoid the misinterpretation of the final results during the decision-making process. Although this study focuses on Chinese construction industry, it also provides good references for measuring uncertainty of greenhouse gas emissions of construction industries around the world.

Increasing demand for fuels and chemicals, driven by factors including over-population, the threat of global warming and the scarcity of fossil resources, strains our resource system and necessitates the development of sustainable and innovative strategies for the chemical industry. Our society is currently experiencing constraints imposed by our resource system, which drives industry to increase its overall efficiency by improving existing processes or finding new uses for waste. Food supply chain waste emerged as a resource with a significant potential to be employed as a raw material for the production of fuels and chemicals given the abundant volumes globally generated, its contained diversity of functionalised chemical components and the opportunity to be utilised for higher value applications. The present manuscript is aimed to provide a general overview of the current and most innovative uses of food supply chain waste, providing a range of worldwide case-studies from around the globe. These studies will focus on examples illustrating the use of citrus peel, waste cooking oil and cashew shell nut liquid in countries such as China, the UK, Tanzania, Spain, Greece or Morocco. This work emphasises 2nd generation food waste valorisation and re-use strategies for the production of higher value and marketable products rather than conventional food waste processing (incineration for energy recovery, feed or composting) while highlighting issues linked to the use of food waste as a sustainable raw material. The influence of food regulations on food supply chain waste valorisation will also be addressed as well as our society's behavior towards food supply chain waste. “There was no ways of dealing with it that have not been known for thousands of years. These ways are essentially four: dumping it, burning it, converting it into something that can be used again, and minimizing the volume of material goods – future garbage – that is produced in the first place.” William Rathje on waste (1945–2012) – Director of the Tucson Garbage project.

Abstract Explosion risk analysis (ERA) is an effective method to investigate potential accidents in hydrogen production facilities. The ERA suffers from significant hydrogen dispersion-explosion scenario-related parametric uncertainty. To better understand the uncertainty in ERA results, thousands of Computational Fluid Dynamics (CFD) scenarios need to be computed. Such a large number of CFD simulations are computationally expensive. This study presents a stochastic procedure by integrating a Bayesian Regularization Artificial Neural Network (BRANN) methodology with ERA to effectively manage the uncertainty as well as reducing the stimulation intensity in hydrogen explosion risk study. This BRANN method randomly generates thousands of non-simulation data presenting the relevant hydrogen dispersion and explosion physics. The generated data is used to develop scenario-based probability models, which are then used to estimate the exceedance frequency of maximum overpressure. The performance of the proposed approach is verified by analyzing the parametric sensitivity on the exceedance frequency curve and comparing the results against the traditional ERA approach.

Abstract The present study is aimed to investigate and compare effects of biodiesel-ethanol (BE) and biodiesel-n-butanol (BBu) blends on combustion, performance and emissions of a direct-injection diesel engine. Experiments were conducted on BE5 (5% ethanol and 95% biodiesel, v/v), BE10, BE15, BBu5, BBu10 and BBu15, at five engine loads and at 1800 rpm. In regard to combustion characteristics, effects on maximum heat release rate, maximum in-cylinder pressure, start of combustion, combustion duration and coefficients of variations (COVs) of IMEP and maximum increase rate of in-cylinder pressure were investigated. In regard to engine performance, effects on BSFC and BTE were investigated. The blended fuels have adverse effects on engine performance especially at low load, with the BE blends having more adverse effects than the BBu blends. Moreover, on average of the five engine loads, the BBu and BE blends increase CO emission by 13.7% and 22.8% and HC emission by 5.6% and 29.2%, respectively; but reduce NOx emission by 6.5% and 28.0%, particle mass concentration by 20.7% and 20.6% and particle number concentration by 22% and 21%, respectively. Overall, the BE blends are more effective in reducing particulate and NOx emissions but the BBu blends would lead to less increase in CO and HC emissions.

Abstract Renewable energy utilisation, latent energy storage, optimal system design, and robust system operation are critical elements for carbon-free buildings and communities. Machine learning methods are effective to assist the energy-efficient renewable systems during multi-criteria design and multi-level uncertainty-based operation periods. However, the current literature provides little knowledge on this topic. In this study, a state-of-the-art-review on phase change materials for cooling applications is presented, in terms of smart ventilations, intelligent PCMs charging/discharging, deterministic parametrical analysis, stochastic uncertainty-based performance prediction and optimisation. Furthermore, technical effectiveness of machine learning methods in single and multi-objective optimisations has been presented, through hybrid PCMs integrated renewable systems. Multivariables involved in the review include thermo-physical, geometrical and operating parameters of PCMs. Multi-criteria employed in the review include heat transfer rate, cooling energy storage density, heat storage and release efficiency, and indoor thermal comfort. The literature review presents technical challenges, such as tradeoff solutions between computational accuracy and efficiency, generic methods for effective selection amongst multi-diversified optimal solutions along the Pareto front, the general methodology for multi-level uncertainty quantification, smart controllers with accurate predictions under high-level parameters’ uncertainty and stochastic occupants’ behaviors. The future outlook and recommendations of machine learning methods in PCMs integrated cooling systems have also been presented as avenues for upcoming research.