The methodologies and indicators that have been proposed in the literature to measure energy poverty are quite diverse. Some are subjective approaches based on personal or third parties’ perceptions ...of affordable warmth at home; whereas others calculate objective indicators. Although these different proposals have already been theoretically compared, an empirical comparative analysis that measures in a real case study the practical impact of the theoretical limitations detected for the different indicators was still pending. The goal of this paper is thus to contribute to this debate by comparing critically the different approaches used to measure energy poverty in a real case (Spain in 2015), and to propose a new methodology that might be able to overcome some of the major problems that affect current methods.
•An empirical comparative analysis of energy poverty indicators.•An econometric vulnerability analysis of energy poverty in Spain.•A new proposal for a MIS-based energy poverty indicator.
This paper looks at the suitability of using exergy as an indicator for energy sustainability studies, by reviewing the relevant literature and describing and assessing the different uses that have ...been proposed for it as a global energy sustainability indicator.
Exergy is a thermodynamic property that links the first and the second thermodynamic principles as well as connects a system under study with the environment where it belongs. Since the first principle of thermodynamics measures quantity of energy and the second measures irreversibilities, i.e. quality of energy, having a single thermodynamic indicator which is able to deal with both issues at the same time means a great advance in energy sustainability studies.
Our review shows that using exergy for weak sustainability studies presents some problems, but still offers a worthy contribution to this field, more valuable than pure economic analyses. Strong sustainability assessments featuring exergy show more drawbacks and complications, but can also play a key role in a sustainability framework designed in order to obtain sustainable policies which are able to maintain homeostatic relations between the system under study and its environment, thus complementing traditional economic approaches which are mainly focused on the economic and social poles of sustainability.
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•N-doped micropores carbon is used for high performance aqueous supercapacitors.•The MACN electrode exhibits a high specific capacitance of 323 F/g.•MACN symmetric cell exhibits an ...energy density up to 9.8 Wh/kg.•N-doped surface enhances the interaction of ion adsorption by DFT calculation.
The performance of a supercapacitor (SCs) fabricated from coal-based activated carbon was studied in terms of its specific capacitance (C), life cycle and rate performance. In this work, a low cost modified nitrogen-doped coal-based activated carbon (MACN) was prepared by KOH/H2O co-activation from lignite. Experimental results and density functional theory (DFT) calculations showed that introducing nitrogen atoms into the coal-based activated carbon leads to a rearrangement of the carbon skeleton structure and changes the surface chemical environment. Leading to the MACN internal disorder increases (ID/IG is up to 0.99), structural stability improves (TGA curves shift right), and various nitrogen functional groups (N-5, N-6, N-Q) are formed on the carbon surface. In addition, the MACN possesses high specific surface area (SBET: 2129 m2/g), abundant micropores (Vmic: 0.62 cm3/g), appropriate mesopores (Vmes: 0.39 cm3/g, Vmes ratio: 38.6%), low impurity content, and highly N-doping (9.59 wt%). These characteristics of the MACN provide for a high C of 323 F/g at a current density of 0.5 A/g. The enhanced MACN is 64.8% higher than the undoped MAC. Furthermore, a high energy density of 10 Wh/kg can be achieved with a MACN-assembled symmetrical cell when the power density of 250 W/kg in 6 M KOH.
Forecasting of wind power is very important for both power grid and electricity market. Wind power forecasting based only on historical wind power data is carried out in this work. In a first ...treatment to the wind power data, Phase Space Reconstruction (PSR) is used to reconstruct the phase space of the wind dynamical system. Secondly, Principle Component Analysis (PCA) is used to minimize the influence from improper selection of the delay time and phase dimension. Finally, a prediction model, using Resource Allocating Network (RAN), is built for nonlinear mapping between the historical wind power data and the forecasting. Performance of the proposed method is compared with Persistence (PER), New-Reference (NR), and Adaptive Wavelet Neural Network (AWNN) models by using data from the US National Renewable Energy Laboratory (NREL). Analysis results indicate that the forecasting error of the proposed method is about 3% for 48 look-ahead hours, which is remarkably below the errors obtained with other forecast methods and has a probability close to 80% for 48 look-ahead hours forecasting within 12.5% error. The proposed method can also forecast wind power for turbines of different capacity and at different elevations below 10% error.
•Dynamics of wind power systems analyzed with Phase Space Reconstruction (PSR) and Principal Component Analysis (PCA).•Wind power forecasting modeling using Resource Allocating Networks (RAN).•Combined approach makes possible to forecast wind power with a long look-ahead horizon, 48 look-ahead hours.
In the present industrial revolution era, the industrial mechanical system becomes incessantly highly intelligent and composite. So, it is necessary to develop data-driven and monitoring approaches ...for achieving quick, trustable, and high-quality analysis in an automated way. Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery. The advent of deep learning (DL) methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals. This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network (IIFD-SOIR) Model. The proposed model operates on three major processes namely signal representation, feature extraction, and classification. The proposed model uses a Continuous Wavelet Transform (CWT) is for preprocessed representation of the original vibration signal. In addition, Inception with ResNet v2 based feature extraction model is applied to generate high-level features. Besides, the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish optimizer. Finally, a multilayer perceptron (MLP) is applied as a classification technique to diagnose the faults proficiently. Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset. The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6% and 99.64% on the applied gearbox dataset and bearing dataset. The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.
•Dielectric property is considered for bending and vibration of GPL/polymer beam.•Active tuning of structural performances of GPL/polymer dielectric beam is evidenced.•Dependency of behaviours of ...GPL/polymer beam on influencing factors are identified.
Nonlinear bending and forced vibration of graphene platelets (GPLs) reinforced composite (GPLRC) beam with dielectric permittivity are investigated. The tensile modulus and dielectric permittivity as required for structural analysis are obtained by using effective-medium theory (EMT) while Poisson's ratio and mass density are evaluated by rule of mixture. Based on Timoshenko beam theory, governing equations for nonlinear bending and forced vibration of the GPLRC beam are established and numerically solved through differential quadrature method (DQM). The dependency of the structural behaviours of the GPLRC beams on the attributes of GPL, applied external loading and electrical field are comprehensively studied. The analysis demonstrates that the performances of the GPLRC beam can be designed and actively tuned through adjusting several parameters. The bending and vibration behaviours of the beam are sensitive to smaller beam thickness, larger GPL aspect ratio and electrical voltage. Snap-through behaviour is observed for the bending and vibration of the composite beam within the involved AC frequency range. Reasons underlying the above observations are analysed and discussed to increasingly understand the structural behaviours of graphene reinforced composite structures with dielectric property.
•1. MC method was used solidify heavy metals in fly ash from coal-fired power plants.•2. Compared with dry milling, wet milling can weaken fly ash particle agglomeration.•3. MC solidify heavy metals ...by change unstable water/acid soluble to stable residue.
Fly ash from coal-fired power plants has become the world's largest solid waste pollutant. The mechanochemical (MC) method used as a non-thermal method shows good stability to heavy metals in soil and municipal solid waste incineration (MSWI) fly ash. It is first uesd to stabilize the heavy metals in fly ash from coal-fired power plants. In this paper, dry milling and wet milling MC methods were carried out on fly ash from a 300 MW supercritical circulating fluidized bed (CFB) boiler. The relative leaching rate (RLR) is defined to indicate the degree of leaching. Experimental results show that after mechanochemical treatment for 10 h, the RLRs of Cu, Cr, Pb, Zn, Cd, and Ni by dry milling decrease by 52.10%, 70.16%, 89.80%, 22.97%, 3.15%, and 23.49% respectively, and the RLRs of Cu, Cr, Pb, Zn, Cd and Ni by wet milling decrease by 58.11%, 70.92%, 89.64%, 23.26%, 10.59%, and 30.77% respectively. Compared with dry milling, the fly ash particle size is smaller after wet milling, indicating that the presence of water will weaken the agglomeration of fine particles. A continuous extraction experiment shows that the water-and acid-soluble fraction of the six heavy metals in fly ash can be reduced by dry or wet milling, and the residual fraction can be increased.
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•Supercritical CO2 greatly promotes the carbonation of fly ash.•Compared with dry milling, wet milling has a better effect on carbonation improvement.•Supercritical carbonation has a ...significant inhibitory effect on heavy metals from fly ash.
Accelerated carbonation of fly ash is a potential way to achieve CO2 emission reduction and heavy metal solidification. Slow conversion in the diffusion control stage is the bottleneck of the carbonation technical route. Based on the strong diffusion and permeability of supercritical CO2, and the modification of mechanical force to produce more fresh surfaces and pores, a method consisting of supercritical CO2 coupled with mechanical force was carried out to strengthen the carbonation of fly ash. Research results show that the carbonation efficiency of fly ash under supercritical CO2 is generally higher than under low-pressure conditions, and carbonation under supercritical CO2 can effectively stabilize heavy metals in fly ash. In this work, the optimal amount of carbon sequestration under low-pressure was found to be 42.3 g-CO2/kg-fly ash (g/kg), with a carbonation efficiency of 18.65%. Under the supercritical condition of 8 MPa, the maximum carbon sequestration by fly ash is 54.9 g/kg, and the carbonation efficiency is 24.20%. In experiments with mechanical ball milling modification, the order of carbonation efficiency is: wet milling modified ashes > dry milling modified ashes > raw ashes. Additionally, carbonation has an obvious inhibitory effect on the leaching of Pb, Cr and Cd from fly ash.
•Supercritical CO2 increases the mineralization efficiency of CaO powder by 2.09 times.•The diffusion depth in block CaO is 1.22 times higher at supercritical pressure.•H2O can activate the adjacent ...O atoms in CaO and increase adsorption of CO2 by CaO.
The accelerated mineralization of fly ash is a potential way to achieve CO2 emissions reduction. However, the slow reaction at the diffusion control stage is the bottleneck. Due to the strong diffusion of supercritical CO2, it has been to strengthen mineralization, but the degree of improvement in mineralization efficiency and the changes that take place in the transition from non-supercritical to supercritical CO2 are not clear. In the mineralization process, CaO in the ash reacts with CO2 to form CaCO3. In order to exclude the influence of other alkaline oxides, powdered and block CaO were used to research the mechanism, and experiments were then carried out on samples of fly ash with different CaO content. Firstly, powdered CaO was used to conduct dry and wet mineralization experiments. In dry mineralization, the mineralization efficiency of the process changes in two stages as the pressure increases, first undergoing a gentle-increase and then a rapid one. Diffusion depth experiments on block CaO show that the supercritical diffusion depth is higher than the non-supercritical diffusion depth by a factor of 1.22, indicating that supercritical CO2 can improve the degree of mineralization in diffusion stage. The presence of water could promote mineralization. In wet mineralization, the efficiency of the process changes in three stages as the pressure increases, with first a gentle increase then a more rapid one and finally an attenuated rate of increase. The supercritical mineralization efficiency at 8 MPa was 55.27%, a factor of 2.09 larger than for non-supercritical mineralization at 3 MPa (26.39%). This is because that supercritical CO2 has increased the diffusion and greatly improved solubility in water. The results of DFT calculations show that H2O can promote the adsorption of CO2 by CaO, which is one of the reasons why water promotes mineralization. Finally, wet mineralization experiments on fly ash show that at 8 MPa, the mineralization efficiencies of HB, SD, SX and YN fly ashes are 1.94, 1.30, 2.03 and 1.62 times their values at 3 MPa. Although the difference in efficiency is affected by the CaO content, supercritical CO2 can effectively improve the mineralization efficiency.
In this paper, we analyzed the mass transfer model with chemical reactions during the absorption of carbon dioxide (CO2) into phenyl glycidyl ether (PGE) solution. The mathematical model of the ...phenomenon is governed by a coupled nonlinear differential equation that corresponds to the reaction kinetics and diffusion. The system of differential equations is subjected to Dirichlet boundary conditions and a mixed set of Neumann and Dirichlet boundary conditions. Further, to calculate the concentration of CO2, PGE, and the flux in terms of reaction rate constants, we adopt the supervised learning strategy of a nonlinear autoregressive exogenous (NARX) neural network model with two activation functions (Log-sigmoid and Hyperbolic tangent). The reference data set for the possible outcomes of different scenarios based on variations in normalized parameters (α1, α2, β1, β2, k) are obtained using the MATLAB solver “pdex4”. The dataset is further interpreted by the Levenberg–Marquardt (LM) backpropagation algorithm for validation, testing, and training. The results obtained by the NARX-LM algorithm are compared with the Adomian decomposition method and residual method. The rapid convergence of solutions, smooth implementation, computational complexity, absolute errors, and statistics of the mean square error further validate the design scheme’s worth and efficiency.
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