Aggregate Energy Research Articles

Numerical simulation of airflow temperature field and cooling in high temperature coal seam working face

With the continuous extension of coal mining depth, the risk of heat exhaustion from high temperatures gets worse. In this paper, we first identified the main heat sources of the high-temperature coal seam working face. The effects of air supply volume on the temperature evolution of working face were then investigated by numerical simulation. The air supply volume to effectively cool the working face was calculated using a new method, sigma heat difference. A mechanical cooling method, an air cooler in the intake airway, was proposed, and the accuracy of the sigma heat difference method was confirmed through numerical simulation and field measurements. The results show that: (1) The aggregate thermal energy emitted by the heat sources located in the mining working face is 688.2177 kW, with mechanical equipment heat dissipation accounting for the main part; (2) The numerical simulation shows that the temperature of the airflow of the roadway steadily drops as the airflow velocity from the inlet airway increases. Yet extremely high inlet airflow velocities lead to narrower change ranges of the air temperature of the working face; (3) The air supply volume required to effectively cool the working face is determined by the sigma heat difference method to be 1972.2 m3/min, which provides a basis for numerical simulation of inlet air velocity for cooling; (4) The Field measurement and numerical modelling demonstrate that the sigma heat difference method is reasonable. The research conclusions provide theoretical guidance for dealing with heat hazards in coal mining faces.

Validation of the Energy Demand Load Profile Estimator “PROFet” for Trondheim Non-residential Buildings

Accurate long-term forecasts of aggregate energy load profiles are crucial for effective energy system planning at regional and national scales. This study aims to validate PROFet, a flexible load profile modeling tool. PROFet forecasts weather-dependent heating and electrical load profiles at an hourly resolution for both residential and non-residential buildings connected to district heating systems. Given that the tool’s accuracy for residential buildings has been demonstrated in previous studies, further validation is needed for non-residential buildings. To achieve this, our study evaluates PROFet’s extrapolation performance using an out-of-sample test dataset from Trondheim municipality. The results show that PROFet accurately forecasts the hourly heating load during the space-heating season but is less accurate during periods when domestic hot water needs dominate. The tool exhibits nearly identical performance across the three cases, except for efficient kindergartens, where the estimation accuracy is lower.

Examining the consumption of oil on total factor productivity and CO2 emissions: an analysis of highly oil-consuming countries

PurposeThis study aims to examine the impact of oil consumption on carbon dioxide (CO2) emissions and total factor productivity (TFP) in highly oil-consuming countries of the world from 1995 to 2019.Design/methodology/approachFor this purpose, fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) are applied.FindingsFMOLS and DOLS models reveal that oil consumption, human capital, population, trade openness and nonrenewable energy have a significant positive effect on CO2 emissions. While information and communication technology (ICT), as proxied by mobile and natural resources, has a significant negative effect on CO2 emissions. In the case of TFP, oil consumption, ICT and natural resources have a significant positive effect on the TFP. On the other hand, trade openness, population, human capital and nonrenewable energy have a significant negative effect on TFP. The results of this study can help to provide policy recommendations to reduce CO2 emissions in studied highly oil-consuming countries of the world.Originality/valueDue to the threat to sustainable development, climate change has become a major topic for debate around the world. The influence of oil consumption on CO2 emission and TFP is less known in the available literature. Another significance of this study is that many researchers considered aggregate energy consumption to study this relationship, but the authors have studied the effect of energy consumption, particularly from oil in the top oil-consuming countries, which is a significant shortcoming of the present research.

Research on energy consumption and economic growth in Henan Province (China) based on sustainable development

Henan Province is a major economic province of China with significant aggregate energy consump-tion. In the context of studying the problems of factors and elements of sustainable economic development, the author sets the task of researching the relationship between energy consumption and economic growth. Using data from 2001-2021 and Stata 15 software, it can be studied that the gross regional product (GRP) of Henan province has relatively low energy-intensive nature, since for each percentage point of increase in energy consumption, GRP increases by about 4.12 percentage points. However, electricity production is based on an expensive and limited resource, and environmental pollution occurs. Therefore, the challenge for managing the sustainable development of Henan Province is to increase its GRP while reducing energy consumption. In the process of sustainable development of Henan Province, the author recommends stepping up research and development, developing new energy-saving technologies; increase the share of clean energy use, optimize the structure of energy consumption, increase public awareness of the need for energy saving.

Early stage of erythrocyte sedimentation rate test: Fracture of a high-volume-fraction gel.

Erythrocyte sedimentation rate (ESR) is a clinical parameter used as a nonspecific marker for inflammation, and recent studies have shown that it is linked to the collapse of the gel formed by red blood cells (RBCs) at physiological hematocrits (i.e. RBC volume fraction). Previous research has suggested that the observation of a slower initial dynamics is related to the formation of fractures in the gel. Moreover, RBC gels present specific properties due to the anisotropic shape and flexibility of the RBCs. Namely, the onset of the collapse is reached earlier and the settling velocity of the gel increases with increasing attraction between the RBCs, while the gel of spherical particles shows the opposite trend. Here, we report experimental observations of the gel structure during the onset of the collapse. We suggest an equation modeling this initial process as fracturing of the gel. We demonstrate that this equation provides a model for the motion of the interface between blood plasma and the RBC gel, along the whole time span. We also observe that the increase in the attraction between the RBCs modifies the density of fractures in the gel, which explains why the gel displays an earlier onset when the aggregation energy between the RBCs increases. Our work uncovers the detailed physical mechanism underlying the ESR and provides insights into the fracture dynamics of an RBC gel. These results can improve the accuracy of clinical measurements.

A Multivariate Time Series Analysis of Electrical Load Forecasting Based on a Hybrid Feature Selection Approach and Explainable Deep Learning

In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant advantages for enhancing grid reliability and informing energy planning decisions. Specifically, mid-term ELF is a key priority for power system planning and operation. Although statistical methods were primarily used because ELF is a time series problem, deep learning (DL)-based forecasting approaches are more commonly employed and successful in achieving precise predictions. However, these DL-based techniques, known as black box models, lack interpretability. When interpreting the DL model, employing explainable artificial intelligence (XAI) yields significant advantages by extracting meaningful information from the DL model outputs and the causal relationships among various factors. On the contrary, precise load forecasting necessitates employing feature engineering to identify pertinent input features and determine optimal time lags. This research study strives to accomplish a mid-term forecast of ELF study load utilizing aggregated electrical load consumption data, while considering the aforementioned critical aspects. A hybrid framework for feature selection and extraction is proposed for electric load forecasting. Technical term abbreviations are explained upon first use. The feature selection phase employs a combination of filter, Pearson correlation (PC), embedded random forest regressor (RFR) and decision tree regressor (DTR) methods to determine the correlation and significance of each feature. In the feature extraction phase, we utilized a wrapper-based technique called recursive feature elimination cross-validation (RFECV) to eliminate redundant features. Multi-step-ahead time series forecasting is conducted utilizing three distinct long-short term memory (LSTM) models: basic LSTM, bi-directional LSTM (Bi-LSTM) and attention-based LSTM models to accurately predict electrical load consumption thirty days in advance. Through numerous studies, a reduction in forecasting errors of nearly 50% has been attained. Additionally, the local interpretable model-agnostic explanations (LIME) methodology, which is an explainable artificial intelligence (XAI) technique, is utilized for explaining the mid-term ELF model. As far as the authors are aware, XAI has not yet been implemented in mid-term aggregated energy forecasting studies utilizing the ELF method. Quantitative and detailed evaluations have been conducted, with the experimental results indicating that this comprehensive approach is entirely successful in forecasting multivariate mid-term loads.

Investigating the modern renewable energy-environmental Kuznets curve (REKC) hypothesis for East Africa Community (EAC) countries

PurposeThe study aims to explore the validity of the modern renewable energy-environmental Kuznets curve (REKC) while considering the relevance of financial development in the consumption of modern renewable energy in East Africa Community (EAC). Modern renewable energy in this study includes all other forms of renewable energy except traditional use of biomass. The authors controlled for the effects of urbanization, governance, foreign direct investment (FDI) and trade openness.Design/methodology/approachPanel data of the five EAC countries of Burundi, Kenya, Rwanda, Tanzania and Uganda for the period 1996–2019 were used. The analysis relied on the use of the autoregressive distributed lag–pooled mean group (ARDL-PMG) model, and the data were sourced from the World Development Indicators (WDI), World Governance Indicators (WGI) and International Energy Agency (IEA).FindingsThe REKC hypothesis is supported for modern renewable energy consumption in the EAC region. Financial development positively and significantly affects modern renewable energy consumption, whereas urbanization, FDI and trade openness reduce modern renewable energy consumption. Governance is insignificant.Originality/valueThe concept of the REKC, although explored in other contexts such as aggregate renewable energy and in other regions, has not been used to explain the consumption of modern renewable energy in the EAC.

Resource scarcity, circular economy and the energy rebound: A macro-evolutionary input-output model

We propose an Agent-Based Stock-Flow Consistent model combined with a simplified Input-Output (IO) structure of production. In the model, heterogeneous firms interact in the energy, material, capital and consumption markets. Materials for production of consumer goods can be manufactured using non-renewable or recycled material inputs. We examine the conditions under which the circular economy emerges through market mechanisms, as well as it can be a source of the rebound effect. An important novelty of our approach is that recycling and mining sectors employ different types of capital for production. Capital goods (machinery) are produced by capital firms, which constantly engage in innovations to improve their technological features. This way we endogenize changes in technological coefficients of the Input-Output tables. We show that sectoral interdependencies along the value chain can render the energy rebound effect due to the circular economy (CE) even if energy intensity of the recycling process is lower compared to mining. This effect depends on the impact of investment in the recycling sector on the aggregate demand and energy use along the entire value chain. We assess the role of macroeconomic policies, namely mission-oriented innovation policies (MOIPs), active fiscal policies (AFPs), and environmental taxes in fostering the CE transition, while mitigating the rebound effect. We find that the combination of MOIPs and active fiscal policy is the most effective in promoting the circular economy, preserving employment and ensuring a sustainable growth path.

Do clean energy stocks and sub-sectors hedge China economic policy uncertainty: New evidence from wavelet analysis

This study aims to investigate the hedging properties of aggregate clean energy markets and their subsectors against China economic policy uncertainty from September 14, 2012, to June 16, 2022. We employed wavelet coherence with a phase difference to explore the time-frequency relationship between our chosen markets. Meanwhile, strong time-varying connectedness is observed between clean energy indices against China economic policy uncertainty, especially during market turmoil. In addition, asymmetric patterns in the interconnections of all markets are more evident between 4 and 64 days. However, fuel cells, green IT, and energy management sub-sectors are act as a strong hedge against economic policy uncertainty. In contrast, the global, US, and European aggregate clean energy markets act as strong hedge during different periods. Based on these empirical findings, this research proposes several important implications for energy investors, market regulators, and other market participants to make more informed decisions about their portfolio risk.

General Overview and Proof of Concept of a Smart Home Energy Management System Architecture

This paper proposes and implements a smart architecture for Home Energy Management Systems (HEMS) that enables interoperability among devices from different manufacturers. This is achieved through the use of standardized elements and the design of an innovative middleware. The system comprises a control unit that communicates with smart outlets using the Wireless Smart Ubiquitous Network (WI-SUN) Home Area Network (HAN) specification, while smart metering is achieved using the WI-SUN Field Area Network (FAN) specification. To manage important data, a web platform and mobile app were created. Additionally, machine learning techniques are utilized to identify energy consumption of individual appliances when only the aggregate energy consumption of the house is available. The architecture presented here supports real-time control of energy use and generation through HEMS, and new devices can be added transparently. Finally, a comparison of the proposed system with similar systems in literature highlights its many advantages in terms of functionality.

Toward environmental sustainability: data-driven analysis of energy use patterns and load profiles for urban electric vehicle fleets

The scale-up of urban electric vehicle (EV) fleets, driven by environmental benefits, is resulting in surging aggregate energy demands that may reshape a city's power supply. This paper establishes an integrated data-driven assessment model for investigating the energy use (kWh) patterns and charging load (kW) profiles of urban-scale EV fleets. To this end, urban EV operating and operational datasets are linked with climate data and vehicle specifications. Four vehicle fleet types are distinguished: private, taxi, rental, and business fleets. Statistical models regarding distribution analysis, spectrum analysis, and identical distribution tests are employed to analyze the patterns of driving distances, energy consumption, and shares of active charging EVs. The minute-level changes in charging EV numbers and aggregate charging power are examined to reflect the grid load impact. The results show that private light-duty EVs in Beijing consume an average of 9.1 kWh/day, with more charging activities on Fridays. The primary load peaks of light-duty EVs in Beijing usually occur between 11 p.m. and 1 a.m., attributable chiefly to the private fleet's midnight peak load estimated at 28 % of the total daily charging private EV count multiplied by 5.5 kW/EV. Secondary peaks occur between 8 a.m. and 10 a.m. on weekdays for private fleets, and at 4 p.m. for public fleets. Our work can be extensively used for analyses on transport emissions, urban power supply, infrastructure build-ups, and policymaking.

Residential water and energy consumption prediction at hourly resolution based on a hybrid machine learning approach

Predicting water and energy consumption at high resolution over a short-term horizon is critical for water and energy resource management. Water and energy are shown to be closely interlinked in household consumption. However, hourly predictions are often based only on historical consumption data for the resource being predicted, with activity or appliance information and household attribution as additional information. Few studies have used aggregated water and energy consumption for predictions. Within this context, the current study proposed a novel hybrid machine learning model based on the Prophet time-series model, Gated Recurrent Unit network, and self-adaptive weights, called the Prophet-GRU model, which could jointly include historical water and electricity consumption as inputs for hourly water or electricity prediction. Data on hourly water and electricity consumption in six households in Beijing during January–March 2020 were used to train and validate the Prophet-GRU model. The goodness of fit indicator (R2) and prediction accuracy (mean squared error and mean absolute error) for the water and electricity predictions were evaluated. Compared with the single input of water or electricity, with the combined input of data of these two resources, the proposed Prophet-GRU model achieved improvements of 29.2 % and 48.5 % in R2, for water and electricity consumption prediction, respectively. Our results could help better understand water-energy linkages and promote collaborative water and energy management practices.

Why Na+ has higher propensity than K+ to condense DNA in a crowded environment.

Experimentally, in the presence of the crowding agent polyethylene glycol (PEG), sodium ions compact double-stranded DNA more readily than potassium ions. Here, we have used molecular dynamics simulations and the "ion binding shells model" of DNA condensation to provide an explanation for the observed variations in condensation of short DNA duplexes in solutions containing different monovalent cations and PEG; several predictions are made. According to the model we use, externally bound ions contribute the most to the ion-induced aggregation of DNA duplexes. The simulations reveal that for two adjacent DNA duplexes, the number of externally bound Na+ ions is larger than the number of K+ ions over a wide range of chloride concentrations in the presence of PEG, providing a qualitative explanation for the higher propensity of sodium ions to compact DNA under crowded conditions. The qualitative picture is confirmed by an estimate of the corresponding free energy of DNA aggregation that is at least 0.2kBT per base pair more favorable in solution with NaCl than with KCl at the same ion concentration. The estimated attraction free energy of DNA duplexes in the presence of Na+ depends noticeably on the DNA sequence; we predict that AT-rich DNA duplexes are more readily condensed than GC-rich ones in the presence of Na+. Counter-intuitively, the addition of a small amount of a crowding agent with high affinity for the specific condensing ion may lead to the weakening of the ion-mediated DNA-DNA attraction, shifting the equilibrium away from the DNA condensed phase.

STRENGTH STUDIES ON CONCRETE USING E-PLASTIC WASTE AS COARSE AGGREGATE

<p>As an important resource for the IT sector and due to the extremely hazardous elements it contains and the low recycling rate, e-plastic waste management and recycling are growing swiftly. E-plastic waste utilization reduces aggregate costs, landfill costs and energy prices. Rapid population growth coexisted with industrialization and a dynamic shift in the volume of garbage produced. These dangerous wastes and other types of waste represent a serious threat to the ecosystem as well as human health. Therefore, the issue of efficient waste management is essential to the preservation of the environment, human health and livelihood. Concrete coarse aggregate made from plastic and recovered e-waste is being tested for feasibility. Testing was done to find out the characteristics of electronic plastic waste when used as coarse aggregate (2%, 4%, 6%, & 8%). These characteristics included specific gravity, water absorption, fineness test, compressive strength, tensile strength and flexural strength of the concrete.</p>

Disaggregating renewable energy-growth nexus: W-ARDL and W-Toda-Yamamoto approaches

The research on the effects of economic growth on renewable energy faces two significant limitations that may impede the formulation of accurate and effective renewable energy policy. The first limitation pertains to the use of an aggregate methodology, which relies solely on aggregated data for analyzing the relationship between renewable energy and economic growth. The second limitation is the absence of a systematic search for empirical models that could minimize estimation errors. In light of these constraints, this study proposes a disaggregate analysis to examine the concurrent effects of the components of economic growth on the components of aggregate renewable energy. Second, the research introduces two methodologies: wavelet-based and error-adjusted variants of the Auto-Regressive Distributed Lag Model (ARDL) and the Toda-Yamamoto Causality Test. The study presents two major findings. First, it reveals substantial differences between the effects of aggregate growth and growth components on aggregate renewable energy and its components in the ten countries with the highest carbon emissions. Second, the study demonstrates that the predictive accuracy of wavelet-based and error-adjusted ARDL and Toda-Yamamoto tests (W-ARDL and W-Toda-Yamamoto) for all estimated models is significantly higher than that of non-wavelet-based tests. The study suggests prioritizing a disaggregate approach to the renewable energy-growth nexus and utilizing wavelet-based and error-adjusted econometric techniques to improve the accuracy and efficiency of renewable energy policy.

Effect of sweet potato flour on pasting, aggregation properties and dough quality of wheat flour

Sweet potato flour (SPF), as a functional substitute for wheat flour (WF), has gained popularity among consumers in the development of nutritious food products. However, the mechanism of SPF on gluten in dough remains unclear. Effects of SPF on the pasting characteristics, gluten aggregation properties, and the quality of dough were investigated. The results showed that SPF-WF exhibited low pasting properties, and pasting temperature increased by 22.35%–26.98%. SPF-WFs were lower in peak viscosity (62.57%–88.17%), trough viscosity (80.32%–96.67%), breakdown viscosity (11.58%–66.37%), final viscosity (70.72%–96.95%), setback viscosity (57.83%–97.33%), and peak time (16.26%–29.30%). Compared with WF dough, SPF-WF dough had higher hardness and lower elasticity. Microstructure images showed that SPF changed the pore size of the gluten network and caused starch to accumulate. SPF could accelerate gluten aggregation, decrease gluten network strength, and reduce aggregation energy, possibly due to the increase of disulfide bonds from 1.12% to 12.52% (P < 0.05). Moreover, SPF mediated medium molecular weight gluten as well as monomer gluten were polymerized into large molecular weights. In summary, SPF might mediate gluten aggregation and 10% substitution of SPF showed a significant level compared to others.

An evaluation of the demand response potential of integrated dynamic window and HVAC systems

Demand response (DR) increases the flexibility and reliability of the electricity grid as use of intermittent renewable energy sources increases. HVAC and envelope DR measures present the largest aggregate energy and peak demand savings potential of all commercial building end uses because their net demand savings occur during critical peak demand periods. Controllable envelope measures include switchable electrochromic windows, operable window attachments such as outdoor louvers, roller shades, and awnings, as well as other innovative facade technologies that can modulate both solar heat gain and daylight admission over a broad solar-optical range. This study evaluated the technical potential of DR-enabled dynamic windows to reduce critical peak demand for a prototypical medium office building situated in all 16 U.S. climates. Model predictive control (MPC) algorithms were designed to minimize electricity cost in daylit perimeter office zones through control of an electrochromic window with and without HVAC thermostat setpoint control. Conventional and time-of-use rates were used to shape the degree of DR. Median annual peak demand savings with window and thermostat control across all climate zones were 24.3 kW (4.4 W/m2) per building or 15.9 W/m2 for non-north perimeter zones. Resource adequacy at the whole building level was estimated to be 13.1 to 43.4 $/kW per year over the 30-year life of the installation. Co-benefits were increased energy efficiency, and reduced electricity cost and emissions. Visual and thermal comfort requirements were met at all times. Dynamic facades controlled by MPC have substantial technical potential for DR across all U.S. climates and warrant serious consideration for inclusion in DR portfolios.

Waste Plastic Powder Coating on Acidic Aggregates: A New Hydrophobic Coating Technology to Build Moisture-Resistant Asphalt Mixtures

The surface energy of aggregate in asphalt pavements has a huge impact on its moisture susceptibility. The commercial antistripping agents widely used in asphalt may not be effective enough to enhance the moisture resistance if large quantities of acidic aggregates exist in pavements. This is also the reason why moisture damage is still an issue in many U.S. regions primarily supplied with “problematic” aggregates. In this regard, a promising solution is to use waste-plastic-coated aggregate, the surface of which is fundamentally modified and has a high affinity with asphalt because of the hydrophobic nature of plastic. However, it has not been widely promoted in the industry since there needs to be a practical and reliable method for uniform coating and avoiding the agglomeration of fine aggregate. To this end, an entirely new coating technology (thermoplastic polyethylene powder coating [TPPC]) was developed to coat aggregate using polyethylene (PE) powder. This technology utilizes a cheap but highly wettable fluidized bed to stably disperse the hydrophobic PE powder. Then, the dispersion liquid is sprayed onto the hot aggregate where the fluidized bed will evaporate fast while the PE particles stay, melt, and coat the aggregate surface. The microstructures of the coated aggregate were directly observed by scanning electron microscopy. Surface energy-based thermodynamic parameters were used to demonstrate the effect of TPPC on the moisture resistance of asphalt mixtures. This technology can be extended to other types of waste plastic if suitable fluidized beds are developed.

Optimal planning of self-healing multi-carriers energy systems considering integration of smart buildings and parking lots energy resources

This paper presents a new framework for optimal planning of electrical, heating, and cooling distributed energy resources and networks considering smart buildings' contribution scenarios in normal and external shock conditions. The main contribution of this paper is that the impacts of smart buildings' commitment scenarios on the planning of electrical, heating, and cooling systems are explored. The proposed iterative four-stage optimization framework is another contribution of this paper, which utilizes a self-healing performance index to assess the level of resiliency of the multi-carrier energy system. In the first stage, the optimal decision variables of planning are determined. Then, in the second stage, the smart buildings and parking lots contribution scenarios are explored. In the third stage, the optimal hourly scheduling of the energy system for the normal condition is performed considering the self-healing performance index. Finally, in the fourth stage, the optimization process determines the optimal scheduling of system resources and the switching status of electrical switches, heating, and cooling pipelines’ control valves. The proposed method was successfully assessed for the 123-bus IEEE test system. The proposed framework reduced the expected values of aggregated system costs and energy not supplied costs by about 49.92% and 93.64%, respectively, concerning the custom planning exercise.

One‐step Fabrication of Nitrogen‐Doped Carbon Dots‐Based Fluorescent Sensors with Red Emission for Efficient Detection of pH

AbstractCarbon dots have been widely noticed due to their excellent optical properties, such as good photostability and a wide excitation wavelength range. However, the emission peaks of most known carbon dots (CDs) are in the blue region, which hinders their extensive applications in long‐wavelength emission. In this study, red‐emitting carbon dots (RCDs) that are pH‐responsive are synthesized using citric acid and urea as precursors, and their fluorescence intensity increased gradually with increasing solution pH from 1 to 12. A new mechanism based on H aggregation and Fermi energy level transfer is proposed to explain the pH‐responsive emission of RCDs. In contrast to the narrow response range of most pH‐sensitive CDs reported so far, the pH‐responsive range of RCDs is large, especially in the alkaline range, and their pronounced red light emission is well visualized. The prepared carbon dots have the advantages of non‐toxicity, low cost, and a wide pH response range. In addition, “rose” test paper and carbon dot PVA composite film, RCDs‐PVA film, are made to demonstrate the visualization of carbon dots in pH detection. The “rose” test paper and carbon dot PVA still have good fluorescence intensity. The prepared carbon dots have good application prospects for pH detection.