Search

Discrete fracture network (DFN) is an effective means of describing the coupling of heat flow in an underground fractured rock mass. In this paper, an improved DFN is proposed by introducing the correlation function of fracture trace length and aperture, which is more consistent with the real fracture data. Next, based on the improved model, the influence of fracture roughness on the fluid flow and heat transmission was evaluated, and the relationship between the fracture aperture and the joint roughness coefficient (JRC) is established. Finally, based on the exponential function between confining pressure and aperture, the influence of confining pressure on the heat‐flow coupling process is considered in the improved model. Besides, the reliability of the model was verified by comparing with the analytical solution of the two‐dimensional single‐fracture heat‐flow coupling problem. The results show that under the same conditions, considering the correlation between the geometric parameters of the fracture, the seepage and heat transfer rates of the model increased, the outlet boundary flow reached the maximum value, and the average outlet temperature dropped rapidly. However, the fracture roughness reduces the outlet flow rate and the decline rate of average temperature. The confining pressure will lead to a decrease of about 3.5% in the outlet flow of the model, which is consistent with the seepage law in practical engineering. The model presented in this paper is an effective supplement to the two‐dimensional fracture network heat‐flow coupling model and can provide a theoretical basis and a numerical calculation tool for related underground rock mass engineering. [ABSTRACT FROM AUTHOR]

Coalbed methane (CBM) is an important resource of energy. For CBM recovery, sorption‐induced coal deformation can cause significant reservoir permeability change. Moisture content and coal pore morphology affect the gas adsorption capacity and can alter the coal deformation of the coal seam. Therefore, it is crucial to establish a coal gas moisture‐coupled model for CBM production prediction. However, there are currently insufficient data available for quantitative analyses. In this paper, a series of typical sorption‐induced strain experiments were carried out during methane adsorption and desorption on coal samples with different metamorphic degrees and moisture content. The pore morphology and adsorption capacity of coals were measured to analyze the reason for different deformation of coals with various pore structures and moisture. Results show that the adsorption capacity and deformation is corresponding to the specific surface area of micropore, which first decreases and then increases with coal ranks. The deformation and adsorption gas content of dried coals is greater than that of natural moisture coals, which means that moisture can reduce the sorption capacity of coals, resulting in the decrease of gas adsorption‐induced coal deformation. There is also residual deformation of coal samples after gas desorption caused by the residual adsorbed gas in coals. This paper quantitatively investigates the effects of pore characteristics and moisture on coal deformation and the internal mechanism. This work will provide essential information for building out a fully cross‐coupled model of coal gas‐moisture relationships for CBM production prediction. [ABSTRACT FROM AUTHOR]

The renewable energy system integrates various power generating units, heating units, energy conversion devices, and energy storage devices, which is an effective way to improve energy efficiency and realize clean energy utilization. In this paper, a renewable energy system integrating with photovoltaic electric power generation systems, wind‐driven generators, gas‐driven generators, gas‐fired boilers, electric chillers, electric and heating energy storage devices is constructed in an isolated tourist area. Considering the complex multi‐energy flows in renewable energy systems, an energy hub with power and heat is established to concentrate and distribute electric and heating energy flow for balancing energy supply and demand in a CCHP system. Introducing the constraints of renewable energy penetration index and energy supply reliability index, a collaborative optimization framework for system configuration is constructed to minimize annual capital cost, fuel cost, and maintenance cost. The simulation results show that the model and algorithm in this paper are feasible and applicable, and the optimization scheme has good economic benefits and optimal performance, which can promote the consumption of renewable energy and reduce the surplus of electric energy and heat energy. [ABSTRACT FROM AUTHOR]

This paper aims to study the stability region of hydropower plants (HPPs) at high‐voltage direct current (HVDC) sending terminal, to theoretically predict the frequency stability of isolated HPPs under small disturbances. The overall transfer function of the system is derived, and the Hurwitz stability criterion is applied to deduce the stability region. The theoretical analysis of the stability region and time‐domain simulation results of the system show good consistency, and this proves the accuracy of the model. The focus of this paper is mainly on the influence of the hydraulic‐mechanical aspects. Improperly tuned governor parameters and unfavourable design of the surge tank could lead to the frequency instability of HPPs, which appears as divergent oscillations of the frequency of units. A key suggestion of this paper is that the control strategy should consider the factors from not only the electrical side but also hydraulic‐mechanical aspects, for enhancing the stability of the HVDC sending terminal. Besides, the influence mechanism and parameter tuning of frequency limitation controller (FLC) are investigated and the results demonstrate its capability of quick compensation for power disturbances at the HVDC receiving terminal. [ABSTRACT FROM AUTHOR]

Measurement data based on current and voltage of photovoltaic (PV) systems and the establishment of more accurate and stable solar system models are of typical significance for the design, control, evaluation and optimization of PV systems. Accurate and stable parameter evaluation for PV systems needs to be based on more efficient optimization techniques to achieve efficient energy conversion from solar energy. Therefore, this paper proposes a novel and efficient optimization technique enhanced colony predation algorithm to solve the complex PV parameter identification problem named ECPA. By fusing extremal optimization strategy and Nelder–Mead simplex method enables ECPA to further develop in the neighborhood of potential optimal solutions while improving the position of inferior agent candidates, and finally has the ability to search globally beyond the local optimum. To verify the optimization efficiency of ECPA, the first part verifies the efficiency of ECPA in solving complex high‐dimensional and multimodal problems by conducting competitive comparison experiments at the IEEE CEC 2020 benchmark case. In the second part, ECPA is compared with nine similar published state‐of‐the‐art algorithms, and competitive tests for PV parameter identification under single diode model, double diode model, triple diode model and PV module model (PV) are conducted. Finally, we focused on three different commercial PV models (thin film ST40, monocrystalline SM55, and multicrystalline KC200GT) to test the accuracy of ECPA in evaluating PV parameters. The test results show that ECPA is able to maintain a high level of accuracy and stability when dealing with commercial PV models in complex environments. The experimental results demonstrate that ECPA outperforms other algorithms in terms of data fitting, stability, convergence speed and convergence accuracy. All the competitive experimental results show that ECPA can be a novel technique with the best performance for identifying the parameters to be determined in solar PV systems. [ABSTRACT FROM AUTHOR]

To further improve the energy‐saving potential and robustness of the energy management strategy (EMS) for plug‐in hybrid electric vehicles (PHEVs) in a real‐time application, this paper proposes a modified model‐free‐adaptive‐control‐based (MFAC‐based) EMS to overcome the disadvantages in our previous MFAC‐based EMS. First, the influence of external disturbance on MFAC‐based EMS is discussed, and the results show that both the vehicle velocity and load change have a significant impact on its performance. Second, a modified MFAC‐based real‐time EMS is designed based on history driving data obtained from a repeated route in which a state‐of‐charge (SOC)‐constraint‐based reference SOC planning method is firstly proposed to simultaneously consider the vehicle velocity and changing load. Then, global SOC constraints are incorporated in Pontryagin's minimum principle (PMP) to enhance the adaptive capability of the proposed method. Finally, the optimal solution of PMP (i.e., optimal constant) is deployed as a benchmark, and the performance of the modified MFAC‐based EMS (namely MFAC‐II and MFAC‐III) is in contrast to the previous one (MFAC for short) under various real‐world driving cycles. The results demonstrate that the MFAC‐III has a remarkable improvement in both economic performance and robustness. Particularly, the energy‐saving effectiveness is close to the global optimum one in some driving conditions. [ABSTRACT FROM AUTHOR]

The carbon dioxide (CO2) injection in oil reservoirs is a promising enhanced oil recovery method to reduce carbon emissions into the atmosphere. The CO2 injection simulation and optimization require a large computation time, especially in real large‐scale oil reservoirs. This paper integrates experimental design and machine learning to construct a reduced‐physics surrogate model alternative to the complex reservoir simulator. This surrogate model was used to evaluate and optimize the CO2‐assisted gravity drainage (CO2‐GAGD) process, applied to a clastic reservoir in the Rumaila oil field. In the GAGD process optimization, five operational decision parameters controlling the production and injection activities were manipulated to attain optimal future oil production. Hundreds of simulation runs were created by Latin Hypercube Sampling to build a proxy‐based optimization. The optimal scenario increased the cumulative oil production by 416 MMSTB at the end of 10‐year prediction period. Finally, four machine learning (ML) algorithms were built as proxy surrogates alternative to the complex compositional reservoir simulations: Quadratic Equation (QM), FUzzy logic‐GEnetic algorithm (FUzzy‐GEnetic), Multivariate Additive Regression Splines (MARS), and Generalized Boosted Modeling (GBM). To show how robust using the ML approaches for the fast CO2‐GAGD process optimization, the random subsampling cross‐validation was adopted to conclude the optimum proxy metamodel that provides the lowest mismatch between the proxy‐ and simulator‐based cumulative oil production. The GBM algorithm achieved the highest adjusted‐R2 (0.9973) and lowest root mean square prediction error (3.704×106) $(3.704\times 1{0}^{6})$ to produce the most accurate metamodel. The resulting accurate GBM proxy metamodel can be used for fast optimization of the CO2‐GAGD process. Specifically, GBM‐metamodel should lead to achieving higher optimal reservoir flow responses, especially with running a large number of simulation runs. Consequently, this proposed trained GBM‐ML proxy metamodel could be used for gas injection optimization and uncertainty assessment with far‐less computational effort than with the conventional approaches, which use a complex flow simulator. [ABSTRACT FROM AUTHOR]

The accuracy of lithium‐ion battery state estimation is critical to the safety of unmanned aerial vehicles (UAVs). In this paper, aiming at the high‐fidelity modeling of the UAV lithium‐ion battery, a splice‐electrochemical polarization model (S‐EPM) for UAV lithium‐ion battery is constructed by combining the traditional electrochemical model with the equivalent circuit model, which greatly improved the accuracy of the battery modeling. In addition, a novel prior generalized inverse least‐squares algorithm is proposed. Also, based on this algorithm, the full‐parameter identification and multicondition error analysis of the S‐EPM are realized based on this algorithm. Finally, a targeted complex discharge rate test and a full‐function charge–discharge test were designed to further verify the applicability of the S‐EPM to complex conditions. The experimental results show that the voltage error of the model under each working condition is 5.50 and 3.0 mV, and the maximum percentage error ratio is 0.20% and 0.07%. This experiment can provide a theoretical basis for the combination of the electrochemical model and equivalent circuit model and the accurate estimation of internal state variables of lithium‐ion batteries. [ABSTRACT FROM AUTHOR]

Wind power generation prediction plays an important role in the safety and economic operation of the power system. There are many parameters recorded in wind farm data, such as wind power, wind speed, wind direction, and so on. Traditional wind power prediction modeling methods lack the mining of these parameter data and fail to make good use of some potential physical information. To address this challenge, this paper proposes a multiview neural network learning framework to predict wind power. One is the data attribute view of wind power, which uses the historical data feature of the wind power itself to learn the future wind power feature. The other is the physical attribute view of wind power, which uses the physical attribute features associated with the wind power definition to learn the future features. Then all the learned features are jointly fused to predict the future wind power values. In addition, an uncertain factor is proposed and computed, which is inspired by the wind power formula and usually associated with internal and external environment perturbation. All time series features are input into the gated recurrent unit neural network to form a hybrid neural network framework for wind power prediction. Experimental results under the measured condition and the standard condition of wind farms demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In northern Shaanxi province, the coal seam deposit conditions are generally characterized by large thickness and shallow depth of burial, the phenomenon of coal pillar sloughing is serious in the process of working face retrieval, which brings great potential danger to mine safety production. In this paper, by monitoring the stress and damage of the coal pillar in the field section, the stress changes and damage degree of the working face in different mine stages are determined. A new mechanical model of coal pillar in a thick coal seam is constructed and the stress changes and structural breakage law of each section are obtained, the stress transfers mechanism of overburden structure and the stress damage to coal pillar during the mining process of upper and lower section comprehensive mining working face is analyzed. The stress, microscopic damage, and the degree of sloughing in the coal pillar were significantly reduced by monitoring through the top plate hydraulic cracking pressure relief control program. The results of the study show that the extent of sloughing coal pillar and internal damage is closely related to the length of key block B of the masonry beam formed by the overlying rock layer and the position of the break line, by changing the orientation of the overlying masonry beam rock layer and the position and length of the tendency to break line, the sloughing coal pillar is effectively controlled and its stress and damage are reduced. We have achieved good results in control of the sloughing in the Hanjiawan coal mine and similar mines in northern Shaanxi. [ABSTRACT FROM AUTHOR]

In this paper, a novel energy storage technology of a gravity‐enhanced compressed air energy storage system is proposed for the first time, aiming to support the rapid growth of solar and wind capacity. With air storage formed by the shaft well, gravity piston, and seal membrane, the proposed system could achieve constant operating pressure, high storage efficiency, and large storage capacity. Electricity charged into this system is simultaneously transformed and stored in the thermal, pressure potential, and gravitational potential forms. The operating properties of the proposed system are simulated and analyzed with the commercial software Thermoflex V29. Results show that both the storage pressure and the well radius have essential influences on the performance of the newly proposed system. The theoretical energy storage efficiency of the proposed system is in a scope of 63.58%–65.50% with a storage pressure range of 40–100 bar. Economic analysis based on the time‐of‐use price policy in China suggests that the optimum levelized cost of storage (LCOS) happens at the storage pressure of 70 bar and a well radius of 8 m. Besides, considering different plant cost factors that reflect the total investment of a power plant, the optimum LCOS is 1.09–1.26 CNY/kWh. Moreover, the specific exergy cost of the discharged electricity is assessed to be 0.51 CNY/kWh or so, which is significantly more profitable compared with the given electricity price of 1.15 CNY/kWh at off‐peak. [ABSTRACT FROM AUTHOR]

The growth in the use of unmanned aerial vehicles (UAVs) has created an increasing demand for energy‐efficient and green power systems. In this paper, we have evaluated energy management strategies (EMSs) and system optimization design methodologies for fuel cell/battery‐powered hybrid UAVs (HUAVs). EMSs aimed at the optimization of flight endurance and fuel cell durability were proposed based on fuzzy logic, dynamic programming, equivalent consumption minimization, and Pontryagin's minimum principle (PMP). System optimization design methodologies, including static design and synergistic sizing optimization design, were also devised. The synergistic sizing optimization was based on multiobjective optimization, while optimization of the EMS used a non‐dominated sorting genetic algorithm. The effectiveness of the proposed EMSs and optimization design were then validated by simulation. Results showed that the proposed EMSs have both long flight time and good fuel cell durability, with the improved PMP prolonging the fight endurance by 4.64% and reducing the mean current of the fuel cell by 16.1% compared with fuzzy logic. Substantial improvements were obtained by using sizing optimization, and parameter sensitivity was addressed. The findings of this study can aid in the future development of fuel cell‐powered UAVs. [ABSTRACT FROM AUTHOR]

Gravel packing is one of the main sand control methods for deep‐water high‐temperature and high‐pressure (HT/HP) reservoirs. According to the specific characteristics of deep‐water HT/HP and multichannel, considering the influence of slurry flowing through long‐distance multichannel and the slurry temperature alternation along the flow path, based on α–β wave packing theory, a coupling model of gravel packing in deep‐water HT/HP horizontal wells is established. The packing simulation algorithm and calculation process are designed to realize the gravel packing temperature pressure simulation and multistage refined friction calculation of deep‐water HT/HP horizontal wells. The influence of each packing parameter on the packing process is fully studied. Furthermore, the application simulation of the A10H well of the South China Sea deepwater HP well is carried out. The simulation results show that in the slurry injection stage and α wave packing stage, the packing pressure is low and the residual pressure window is large. In the β wave packing stage, the packing pressure increases rapidly, the residual pressure window shrinks rapidly, and finally, at the end of β wave packing, the residual pressure window becomes narrow. The viscosity of carrier fluid has a significant impact on the pressure window of packing operation. For the design pump rate of 5.6 bpm, the packing cannot be completed with 5.0 cp viscosity carrier fluid. For the carrier fluid with high viscosity, the multi‐β wave packing technique of pump reduction control pressure at the end of the β wave packing stage is used, and this process is simulated in this paper. Considering three constraints: not sand packed in the drilling string, without premature bridging in the α wave packing stage, and α and β wave packing pressure control conditions, the safe range of pump rate is determined under different operation safety margin conditions, which can provide technical support for on‐site operation. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC), due to its high efficiency and good stability, is one of the most efficient and effective sources of alternative renewable energy. The LCL filters are utilized to attenuate the harmonics caused by inverter‐based distributed power generation systems (DPGS). In this paper, a systematic robust control design is presented for an LCL‐type grid‐tied PEMFC inverter. The proposed nested control includes the capacitor current feedback and the outer grid current loops, considering the computational delays. The proposed control results in more accurate and realistic modeling to transfer the power of PEMFC and inject a high‐quality current into a weak and harmonics‐polluted network. Also, by utilizing the proposed method, the computational delays that affect the damping capability are eliminated, and the performance of the control system is improved. In addition, designing a virtual series and parallel impedances strengthens the phase and amplitude of the inverter output impedance. As a result, the robust operation of the inverter for significant changes in network impedance is improved, and the system well attenuates the mains voltage harmonics. The simulation results in Matlab/Simulink environment confirm the performance of the control system and the suitable quality of the injected current of the PEMFC system into the weak and harmonic‐polluted network. [ABSTRACT FROM AUTHOR]

Traditionally, longwall top coal caving (LTCC) panels adopt the single‐window drawing mode and their drawing efficiency is relatively low. The synchronous multi‐window drawing (SMWD) mode is a remarkable approach to resolve the incompatibility between shearer coal mining and top‐coal drawing. The present paper analyzes the caving characteristics of top coal under SMWD in LTCC mining of extra‐thick coal seam. The caving and migration of top‐coal fragments were simulated by using the continuum–discontinuum element method (CDEM). The results show that the SMWD mode can effectively improve the drawing smoothness with larger caving channels compared to the traditional single‐window drawing mode. The main caving channel for top‐coal fragments was directly above the assembled multi‐window (AMW), and the size of the AMW had a great influence on the distribution of the velocity field. Additionally, the simulated draw body of the initial drawing adopted an elliptical shape, which changed into a variation ellipse with the increasing size of the AMW. The boundary of the simulated draw body of the initial drawing presented an irregular shape that deviated from the results predicted by the Bergmark–Roos model. Furthermore, the drawing efficiency increased with the expansion of the AMW, whereas the recovery ratio first increased and then decreased with the expansion of the AMW. Finally, field tests revealed that the SMWD mode can greatly improve the drawing efficiency. Thus, the recovery ratio under A2 and A3 SMWD modes was higher than that of the traditional single‐window drawing mode. Based on the results of field tests and CDEM simulation, it is recommended to adopt A2 and A3 SMWD modes to enhance the top‐coal recovery ratio and drawing efficiency. These research results could constitute a technical reference for automated highly efficient top‐coal drawing and high‐yield LTCC panel extracting extra‐thick coal seam. [ABSTRACT FROM AUTHOR]

It is well known that the magnitude of impact load is one main reason for polycrystalline diamond compact (PDC) cutter failure rapidly when drilling conglomerate formation. However, there are many factors affecting the magnitude of impact load. To assess and mitigate these impacts, the discrete element model of PDC cutting‐broken conglomerate has been established, and the influence law of cementation strength difference (CSD), gravel diameter, and cutting velocity on impact load and gravel fragmentation in the process of cutting broken conglomerate is performed. What is more, the laboratory experiment of rock cutting by different shaped PDC cutters under different CSD and gravel diameters has also been conducted, and the correctness of numerical simulation results is verified by laboratory experiment. Meanwhile, the cutting depth and impact load of different shape PDC cutters under different CSD and gravel diameters have been analyzed. First, the simulation results show that the stripping mode of gravel mainly occurs when the CSD is large, and the corresponding axial and tangential impact load (mean axial peak cutting force [MAPCF]/mean tangential peak cutting force [MTPCF]) is relatively small. As the decrease of CSD, the gravel will be directly broken and the magnitude of the MAPCF/MTPCF will gradually increase, and the probability of impact damage to the cutting cutter will incrementally increase. In addition, larger gravel diameter and cutting velocity will lead to larger MAPCF/MTPCF, and the mode of gravel fragmentation changes gradually from being stripped to being directly broken with the increase of gravel diameter. Second, the laboratory experiment results not only verify the correctness of the simulation results but also find that ridged diamond element cutter has the smallest impact load and the largest cutting depth under different CSD and gravel diameters. This paper can provide a theoretical foundation for the optimization of drilling parameters and PDC bit design in drilling conglomerate formation. [ABSTRACT FROM AUTHOR]

In this paper, the thermal performance improvements of a heat removal system like an electronic system have been analyzed. The studied case is a horizontal channel in which two partially inclined baffles are attached with variable height and number. The channel is crossed by a forced convective flow of a cooling fluid (air). This numerical work evaluates the influences of the height and number of the baffles on the enhancement of the heat transfer rate. The mathematical model of this system is composed of nonlinear partial equations that the analytical solution for them is very complex, hence the need for numerical analysis is mandatory with the aid of a finite volume method. Accordingly, The numerical results are presented in axial and transverse velocity, temperature, local and average Nusselt number, local friction coefficient, pressure drop, heat transfer rate, and turbulence kinetic energy. The results revealed that it is possible to improve the thermal performance of the considered system by adopting designs that allow the maximum heat transfer rate with the minimum energy loss. In addition, results show that at the lowest Reynolds number (Re = 10,000), as the height of baffles rises from 0.01 to 0.03 m (growth by 200%), the heat transfer rate augments about 59.09%. Moreover, at the highest evaluated Reynolds number (Re = 87,300), by increasing the height of baffles up to 200%, the heat transfer rate increases by approximately 50.53%. Furthermore, employing a higher number of baffles leads to more heat transfer rates and a significant pressure drop. [ABSTRACT FROM AUTHOR]

In this paper, a three‐dimensional simulation model of the engine cylinder is established using commercial simulation software AVL‐Fire, and the accuracy of the model is verified with experimental results. The effects of diesel/biodiesel/n‐butanol blends on diesel engine performance, and combustion and emissions characteristics were investigated by performing simulation operations on the model. The combustion process in the cylinder was simulated for 0%, 5%, 10%, 15%, and 20% of n‐butanol in the fuel mixture. The results showed that at 75% load, the brake thermal efficiency of D95N5, D90N10, D85N15, and D80N20 engines increased by 0.84%, 1.68%, 2.48%, and 3.19%, respectively, and soot emissions decreased by 2.76%, 5.52%, 8.25%, and 10.96%, respectively, compared with D100. However, the brake power of the engine was reduced, the brake ratio fuel consumption increased, and NOx emission increased. In addition, at all loads, soot and CO emissions decrease as the percentage of n‐butanol in the fuel mixture increases. [ABSTRACT FROM AUTHOR]

Compared with the traditional "dynamic‐static" load rockburst induced by working face mining, a new typical disaster with concealment characteristics has appeared in deep thick coal seam roadways, namely, static creep rockburst, the occurrence mechanism of which remains unclear. Through the method of combining theory with practice, this paper elucidated the mechanical mechanism of static creep rockburst of deep thick coal seam roadways, along with the CT inversion of its rockburst risk. Next, by establishing the creep model and three‐dimensional creep formula under static conditions, it was observed that the main conditions for the unstable creep of deep thick coal seam roadway were high stress and thick coal seam. In addition, by analyzing the unstable creep failure process of surrounding rock in static roadways, it was found that the weakening of surrounding rock strength and stress relief were the reasons for starting rockburst and reducing impedance. The mechanical conditions of creep rockburst caused by stress superposition in weak areas of the roadway under static conditions were proposed, and the criterion of rockburst possibility was given. Finally, according to the characteristics of spontaneity and time delay of such a rockburst disaster, the static creep rockburst evaluation of thick coal seam roadway before mining was achieved by CT inversion, and the research conclusions were thoroughly verified. [ABSTRACT FROM AUTHOR]

Future energy scenarios include both green and blue hydrogen, as both are needed to scale up decarbonized energy supply. A transition to renewable energy is predicted over time, but the urgency we are facing cannot be met by renewable energy alone. For blue hydrogen production, the overall greenhouse gas (GHG) emissions are primarily affected by emissions from natural gas production, processing, and transport (CO2 and methane), as well as process efficiency and carbon capture ratio. Few complete and updated analyses are available that cover the entire blue hydrogen value chain with the best available technology for process facilities, as well as the proper design, operation, and maintenance of all relevant systems and infrastructure. This paper analyses the GHG intensity of blue hydrogen, using recent data as well as input from technology providers on state‐of‐the‐art gas reforming technologies. Data are primarily based on natural gas production and transport in the North Sea Basin, with gas export from the Norwegian continental shelf to continental Europe or the United Kingdom, and with blue hydrogen production either in Norway or near pipeline landfall in the European Union or United Kingdom. Some data related to potential blue hydrogen production in the US Appalachian region are also given. The data show that the properly designed and operated value chains for blue hydrogen supply, with minimal emissions from natural gas supply and high carbon capture in hydrogen production, will give a major reduction in GHG emissions from energy end‐use. GHG footprint for end‐users based on blue hydrogen will typically be 80%–90% lower than for direct supply and use of natural gas. [ABSTRACT FROM AUTHOR]

This paper proposes an improved deep learning‐based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series‐based environmental inputs. Generally, artificial neural network‐based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time‐based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer‐based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological‐year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non‐zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours; resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real‐time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive control (MPC) and its application on a three‐phase four‐leg inverter in an islanded mini‐grid to compensate for unbalanced load conditions while minimizing the neutral‐to‐ground voltage (NGV). The proposed approach uses the novel discrete‐time model of the four‐leg inverter and LC filter to predict the control variables. These predictions are evaluated by using a multiobjective cost function. The proposed method is based on the minimization of the cost function to select the best vector state of the power converter to be applied in the next sampling instance. The cost function in this study has two objectives: the adequately tracked low voltage reference and minimization of the NGV, simultaneously. The stability analysis is also defined to investigate the stable dynamics of the proposed MPC. The effectiveness of the suggested method is verified through simulations considering the single/three‐phase and balanced/unbalanced loads. The distinguished results of the proposed work indicate that the proposed MPC algorithm provides high power quality under unbalanced and nonlinear load conditions with high stability. The robustness of the proposed controller is also studied with both inductive and capacitive filter parameter variations. [ABSTRACT FROM AUTHOR]

The emission of greenhouse gases from fossil fuels is the main environmental impact of energy resources. Moving toward the use of renewable energy resources is the only solution to tackle these problems. In this paper, a configuration of the hybrid biomass and geothermal energy resource is examined for cooling and electricity generation. In this study, water as a geothermal working fluid is heated up due to combustion of syngas produced in a digester and produce power in a steam turbine. So, the remaining heat of this geofluid runs an absorption chiller for cooling production. The results reveal that the energy and exergy efficiencies of this proposed cycle are 30.8% and 15.5%, respectively. Moreover, the economic analysis of this cycle concludes that the payback period is 2.8 years and the simple payback period for this configuration is 2.6 years. Moreover, the highest exergy destruction rate of 52.4% belongs to the absorption refrigeration cycle. Whereas the lowest exergy destruction of the steam line is the lowest value of 17.4%. [ABSTRACT FROM AUTHOR]

This paper studies the width of narrow coal pillars, mining‐induced failure characteristics, and surrounding rock control effect of gob‐side entry driving (GED) adjacent to 2‐1208 filling working face with an approximately 900 m depth. Laboratory experiments, numerical simulations, loosening circle tests, and engineering practices are conducted. The mechanical properties of the filling body, the distribution and evolution law of the second invariant deviatoric stress (J2), and the variation in the plastic zone of the surrounding rock in GED are studied. The conditions of various coal pillar widths and the gob backfilled or not of the adjacent working face are also considered. The results of roadway driving along the filling working face in the deep coal mine are that: (1) The evolution law of J2 and the plastic zone features of the surrounding rock in GED have changed significantly. Thus, it is unreasonable to still adopt the customary theory of narrow coal pillar determination and roadway support design method of GED adjacent to nonfilling working face. (2) The plastic zone of GED has typically asymmetric distribution characteristics during driving and retreating, which is mainly concentrated at the upper corner of the virgin coal rib. And the elastic zone changes slightly by the influence of mining‐induced pressure on the working face, indicating that support in this area is easier. (3) During the stable period of roadway driving, there includes a "ring‐shaped" J2 depression zone at the upper corner of the coal pillar rib. While the peak zone distribution of J2 in the virgin coal rib of GED is approximately "crescent‐shaped," and the peak zone is inclined to the upper corner of the virgin coal rib, which implies that this area is a crucial control region. (4) During the retreating period of panel 2‐1210, the direction of the maximum principal stress of GED gradually deflects from the direction of the gob to that perpendicular to the roof and floor, which represents that the key support area of GED has changed. Therefore, the advanced support of GED needs to be reinforced by single hydraulic props. The width of the coal pillar was determined to be 5 m, and a targeted truss anchor cable support method with bidirectional resistance function is proposed for the roof and both ribs, which realized the stability control of GED during deep filling mining. [ABSTRACT FROM AUTHOR]

Due to rapid socioeconomic growth and current environmental concerns, reducing global greenhouse gas emissions is a key step toward sustainable development. In recent years, some researchers have begun to adopt new grid management technologies such as virtual power plants (VPP) that allow them to improve energy management and cost and reduce gas emissions. The integration of distributed energy resources (DER), such as solar photovoltaic and wind power, combined with micro‐turbines and energy storage systems and with the support of VPP intelligence, will contribute immensely to improving micro‐grids (MGs) energy performance and reducing gas emissions. In this paper, an expert multi‐objective feasibility enhanced particle swarm optimization algorithm is adopted for the optimal scheduling of energy management system to reduce the total operating cost and net emission simultaneously. The proposed algorithm is tested on an Algerian reel building‐MG integrated VPP with DERs and combined cooling, heat, and power generation. Finally, different cases are simulated for a 24‐h time and discussed to demonstrate the validity of the proposed VPP energy system and the effectiveness of the adopted algorithm to reduce gas emissions and the total operating cost. [ABSTRACT FROM AUTHOR]

In this paper, a novel organic flash Rankine cycle (OFRC) system for low‐temperature heat recovery is analyzed and optimized using the 3E (energy, exergy, and economic) analysis method and particle swarm optimization algorithm, respectively. Five environmentally friendly organic fluids and three typical heat source conditions are simultaneously discussed during the parametric analysis and optimization process, to obtain a comprehensive understanding of the thermo‐economic characteristics of this novel system. The main analysis results show that the biggest exergy loss of the OFRC system is caused by the condenser, followed by the evaporator. About 70% of the specific power cost is caused by the capital investment of the system, and more than 60% of the capital investment is spent on purchasing the high‐pressure and low‐pressure expanders. Under the same operating conditions, the working fluids with high critical temperatures can achieve higher cycle thermal efficiency and lower specific power costs than those with low critical temperatures. The optimization results show that the OFRC system is able to achieve a better thermodynamic performance than the organic Rankine cycle (ORC) and organic flash cycle (OFC) systems, and when the heat source's inlet temperature is set to 100°C, 120°C, and 140°C, R245fa, R290, and R152a are, respectively, recommended as the best working fluid for the OFRC system. Besides this, it is found that the OFC system has the worst thermo‐economic performance, and the ORC system can achieve the lowest specific power cost. A new finding is that, for all three systems, the higher the critical temperature of the working fluid, the lower the specific power cost of the system. [ABSTRACT FROM AUTHOR]

Microwave heating (MW) and freeze fracturing (FF) have been widely used for enhancing the permeability of coal reservoirs. In this paper, the effects of combined MW–FF treatments on coals are experimentally investigated through nuclear magnetic resonance. T2 distributions and fractal dimensions of coals under MW, FF, and the combined MW–FF treatments are compared and analyzed. The results show that the T2 area, porosity, and fractal dimensions of coals increase to varying degrees after MW, FF, and MW–FF treatment. The T2 spectrum peaks increase from 2 to 3 and macrofractures can be observed after the coal was treated with the combined MW–FF treatments. However, these phenomena are not observed under MW or FF treatment. The combined MW–FF treatments can effectively fracture coals with less microwave energy and less liquid nitrogen freezing cycle owing to its intensive thermal shock fracturing. Therefore, the combined MW–FF treatment is favorable for fracturing the coals and hence favorable for the CBM production. [ABSTRACT FROM AUTHOR]

In this paper, a Random‐XFEM technology was employed to simulate hydraulic fracture interaction with the natural void in a random field of Young's modulus. The random distribution of Young's modulus is characterized by random field theory, while the stress and pressure fields fracturing were solved by the extended finite element method. And a Random‐XFEM iteration algorithm was proposed to solve the hydraulic fracture propagation problem. Then the proposed model was verified against the KGD model and numerical study. A series of numerical examples were presented to analyze the interaction mechanism of hydraulic fracture and void in a random field. The numerical results show that the random field of Young's modulus has a great impact on the hydraulic fracture propagation path. Under the random distribution of Young's modulus, the hydraulic fracture would deviate from the direction of maximum horizontal principal stress. And the random field parameters (Young's modulus mean and point variance) have different effects on the hydraulic fracture propagation path. Also, the interaction patterns of hydraulic fracture and void are greatly affected by random fields. [ABSTRACT FROM AUTHOR]

The coaxial magnetic gear is a new type of noncontact transmission mechanism, which avoids the inherent defects of the mechanical gearbox due to gear meshing transmission. To study a coaxial magnetic gear (CMG) with high torque density and low torque ripple to replace the mechanical gear in the semidirect drive wind turbine, a novel umbrella shaping inner rotor CMG (US‐CMG) with an auxiliary modulation ring is proposed in this paper. The novel structure adopts a CMG with a dual modulated ring to increase the electromagnetic torque. In addition, the magnetic block of the poled inner rotor is divided into three pieces magnetized by Halbach arrays, and the middle piece is umbrella‐shaped to reduce torque ripple. Based on the topology of the US‐CMG and the principle of the magnetic field modulation, the operation mechanism of the US‐CMG is analyzed. A contrastive analysis among the US‐CMG, Halbach magnetized dual‐modulator CMG (HM‐CMG), and surface‐mounted CMG (SM‐CMG) is established to identify the performance of the US‐CMG. Compared with the SM‐CMG, the results show that the electromagnetic torque of the US‐CMG has increased by 65.46%, and the torque ripple of the US‐CMG is 57.6% of that of the SM‐CMG. [ABSTRACT FROM AUTHOR]

Off‐grid renewable energy systems are a solution for power generation in areas where access to the grid is not possible or cost‐effective; in particular, low population rural areas located far away from grid lines or in rugged terrains. Furthermore, over‐reliance on fossil fuels for supplying global energy demand has led to the depletion of nonrenewable resources and environmental issues. This paper presents a feasibility assessment of an off‐grid hybrid renewable energy system for a remote rural area in Kohgiluye and Boyer‐Ahmad Province in Iran. Regarding the available energy resources in the region, a photovoltaic (PV)‐biomass energy system is considered. HOMER Pro software is utilized to find the optimized sizing of the PV‐biomass system to fulfill this load demand. The objective function in this optimization is the system's total net present cost. Due to the rapid fluctuation of economic factors in the country, several rates of inflation and discount rate are considered to investigate their influence on the costs of the system. The optimization result for the current inflation rate of 40% and the discount rate of 18% in the country proposes a hybrid energy system consisting of a 3 kW biogas‐fueled generator, 4.74 kW PV, 10 kWh battery, and 2.07 kW converter to meet the 2.64 kW peak load and 14.53 kWh/day consumption of the community. The total net present cost and cost of energy are $93,057 and 0.0933 $/kWh, respectively. Finally, the environmental assessment of the proposed hybrid system shows an annual CO2 emission of 2.95 kg, which means 99.9% CO2 emissions mitigation compared to a conventional coal‐based electrical plant. [ABSTRACT FROM AUTHOR]

Achieving carbon neutrality targets requires substantial financial support. Effective utilization of renewable energy is an essential means of promoting energy‐saving and emission reduction to realize a green and circular economy. This paper combines carbon trading mechanisms with the operational mode of Chinese micro‐grid enterprises. This method uses the real options theory to construct an investment decision model for photovoltaic (PV) micro‐grid enterprises. The study aims to analyze the impact of carbon trading revenue on a numerical simulation of PV micro‐grid enterprise investment decisions. The findings demonstrate that when projected growth rates, volatility standard deviations, and risk‐free interest rates rise in the carbon trading market, the carbon price threshold rises, causing businesses to seek out investment opportunities with higher future carbon prices. The increase of micro‐grid generation technology acceptance probability, the availability of resources to meet generation investment requirements, and the generation project cycle will decrease the carbon price threshold and accelerate enterprise investment. Studying the influence of carbon trading prices on micro‐grid investment has specific guidance and practical significance for future government subsidy policies and the investment strategies of grid enterprises. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze local autoignition induced by secondary flame acceleration. The secondary flame acceleration process and the local autoignition formation mechanism at the condition of the secondary flame acceleration propagation were investigated by utilizing an improved designed constant volume combustion bomb (CVCB) with two perforated plates. Primary flame and secondary flame propagation were recorded via high‐speed schlieren photography. The results showed that there were three distinct stages in the process of flame propagation through two perforated plates, which were the primary jet flame stage, secondary jet flame stage, and flame‐shock interaction stage. The morphologies of the flame were analyzed by high‐speed Schlieren images, flame velocities, and pressure oscillation including primary jet propagation, secondary flame formation and acceleration, and location autoignition. Results show the effect of initial pressure on flame propagation, flame velocity, and pressure oscillation. Acceleration of secondary flame and local autoignition phenomenon was more precisely demonstrated as unburnt gases were compressed by flame waves (different velocities) with initial pressure increasing. Based on the morphology of flame, three different types of flame combustion modes were captured at different initial pressure involving turbulent flame, deflagration, and quasi‐detonation. The present work might provide a new insight for combustion science in a confined space, such as autoignition due to compression effect of secondary jet flame and primary flame interaction. It also provides the theory guidance and research direction towards further studies on propane storage vessel deflagration. [ABSTRACT FROM AUTHOR]

In this paper, to analyze the oxygen corrosion of the casing string under the nitrogen blanket condition of the salt cavern underground gas storage and to predict the casing's service life, an indoor corrosion weight‐loss experiment was used to study the effects of oxygen concentration, temperature, and pressure on the gas and liquid phase of N80 steel pipe corrosion laws. The corrosion products were examined using scanning electron microscopy, X‐ray fluorescence spectroscopy, and X‐ray diffraction analyses. Finally, the corrosion life of the casing was predicted based on the uniform corrosion rate and casing strength check. The results indicated the following: (1) under the experimental conditions, the gas–liquid two‐phase corrosion rate gradually decreased with the increase in nitrogen concentration, while when the concentration was 78%–95%, the liquid‐phase corrosion was serious, and in the liquid nitrogen environment (nitrogen concentration is 99.9%), the liquid‐phase corrosion rate was lower than the oilfield corrosion control index (0.076 mm/a); (2) the temperature and pressure had no significant effect on the casing corrosion; (3) with the increase in nitrogen concentration, the service life of the inner and outer pipes under different working conditions gradually increased, with the minimum service life equaling 28 years when the concentration was 99.99%; (4) nitrogen concentrations of 99.99%, 95%, 90%, and 78% can meet the column strength of the 3‐year cavity‐building cycle, while under the 5‐year cavity‐building cycle, it is recommended that the lower limit of the purity is 90%. Overall, the research results will provide a strong information basis for the construction of salt cavern gas storage cavities. [ABSTRACT FROM AUTHOR]

This paper proposes an improved version of Barnacles mating optimizer (BMO) for solving the optimal allocation problem of distribution generator (DGs) in radial distribution systems (RDSs). BMO is a recent bioinspired optimization algorithm that mimics the intelligence behavior of Barnacles' mating. However, like with any metaheuristic optimization approach, it may face issues such as local optima trapping and low convergence rate. Hence, an improved BMO is adopted based on the quasi oppositional (QOBMO) and the chaos maps theories (CQOBMO). The two improvement methods are applied to increase the convergence performance of the conventional BMO. To prove the efficiency of the improved QOBMO and CQOBMO algorithms, 23 benchmark functions are used, and the accomplished results are compared with the conventional BMO. Then, the improved algorithm is applied to minimize the total power and energy losses in the distribution systems considering the uncertainty of DG power generation and time‐varying load demand. The uncertainty of DG is represented using photovoltaic‐based DG (PVDG). The improved method is employed to find the optimal power scheduling of PVDG and battery energy storage (BES) during 24 h. Two standard IEEE RDS (IEEE 33‐bus and IEEE 69‐bus) are used to simulate the case studies. Finally, the obtained results show that significant loss reductions (LRs) are achieved using the improved BMO where LRs reach 65.26%, and 68.86% in IEEE 33‐bus and 69‐bus, respectively, in the case of PVDG integration. However, using PVDG and BES the energy loss reductions reach 64% and 67.80% in IEEE 33‐bus and 69‐bus, respectively, which prove the efficiency of the improved BMO algorithm in finding the optimal solutions obtained so far. [ABSTRACT FROM AUTHOR]

Trombe walls figure among many passive devices used in the Mediterranean climate to minimize heating demands in residential buildings. The thickness of this massive wall is a critical parameter that influences the effectiveness of the system. Insufficient wall thickness conducts to an important interior temperature fluctuation, and huge wall thickness will increase costs and thermal resistance. In this paper, the optimum thickness of four different construction materials (concrete, stone, adobe, and brick), which can be used in the Trombe wall, was determined using an energetic and economic analysis. The energetic results with TRNSYS software show that the best materials, which can contribute to a reduction by 50% in heating loads of a single room, are stone and concrete. For the economic analysis, the life cycle cost and the payback period were calculated for each construction material. The results show that the optimum thickness for stone and concrete are, respectively, 34 and 32 cm with a payback period of 2.85 and 2.65 years. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) power is emerging as one of the most viable renewable energy sources. The recent enhancements in the integration of renewable energy sources into the power grid create a dire need for reliable solar power forecasting techniques. In this paper, a new long‐term solar PV power forecasting approach using long short‐term memory (LSTM) model with Nadam optimizer is presented. The LSTM model performs better with the time‐series data as it persists information of more time steps. The experimental models are realized on a 250.25 kW installed capacity solar PV power system located at MANIT Bhopal, Madhya Pradesh, India. The proposed model is compared with two time‐series models and eight neural network models using LSTM with different optimizers. The obtained results using LSTM with Nadam optimizer present a significant improvement in the forecasting accuracy of 30.56% over autoregressive integrated moving average, 47.48% over seasonal autoregressive integrated moving average, and 1.35%, 1.43%, 3.51%, 4.88%, 11.84%, 50.69%, and 58.29% over models using RMSprop, Adam, Adamax, SGD, Adagrad, Adadelta, and Ftrl optimizer, respectively. The experimental results prove that the proposed methodology is more conclusive for solar PV power forecasting and can be employed for enhanced system planning and management. [ABSTRACT FROM AUTHOR]

Building a complex fracture network in a deep shale gas reservoir during multicluster hydraulic fracturing is challenging. On the one hand, hydraulic fractures tend to propagate along shale bedding planes, thus leading to a limited hydraulic fracture height; on the other hand, under the effect of high in situ stress and weak plane of fractures, multihydraulic fractures can hardly initiate and propagate uniformly. All of these will jeopardize the performance of hydraulic fracturing. This paper considers the influence of natural fracture network, shale beddings, fracturing‐fluid leakage, and high in situ stresses, and builds a numerical model to simulate multifracture propagations along vertical direction during hydraulic fracturing in deep shale reservoirs. The model is developed based on the theory of solid–fluid coupling and finite element method with pore pressure cohesive element technique are used to simulate multicluster fracture initiation and propagation. Moreover, the model is verified by published experiment data, and a good agreement between model and experiment results is observed. The influences of pump rate and fracturing‐fluid viscosity on fracture geometry are discussed, and the flow dynamics around each perforation cluster under different perforation parameters are also studied. Finally, suggestions to improve overall outcome of multicluster hydraulic fracturing are also provided. [ABSTRACT FROM AUTHOR]

The integrated energy system is an important prerequisite for the sustainable transformation to the low‐carbon power system. Therefore, this paper aims to provide readers with insights into the existing research about the planning and operation models of integrated energy systems. First, the general structure of integrated energy systems is elaborated, and the new issues faced by the low‐carbon planning and operation for the integrated energy systems are represented. Next, the technical framework and the general model are proposed. Furthermore, the typical models and solution technologies of the low‐carbon planning and operation are summarized based on specific models operating in multiple scenarios. Finally, based on the characteristics and framework of the integrated energy system, the future research direction is given, which is expected to guide the sustainable development and low‐carbon transformation of energy systems. [ABSTRACT FROM AUTHOR]

In this paper, a grey prediction model group is employed to quantitatively study the impact of COVID‐19 on natural gas consumption in Chongqing, China. First, a grey prediction model group suitable for the prediction of Chongqing's natural gas consumption is introduced, which consists of GM(1,1), TWGM(1,1), and the newly‐developed ODGM(1,1). Then, the model group is constructed to predict Chongqing's natural gas consumption in 2020. Finally, compare the predicted results of the model group with the actual consumption and quantitatively analyze the impact of the epidemic on natural gas in Chongqing. It is found that the impact of the epidemic on the consumption of natural gas in the first quarter of the year is very small, but relatively bigger in the second and third quarters. The study is of positive significance to maintain the supply and demand balance of natural gas consumption in Chongqing in the background of COVID‐19; and it enriches and develops the theoretical system of grey prediction models. [ABSTRACT FROM AUTHOR]

This study has attempted to compare two thermodynamic cascade cycles, which in this paper are presented as Systems A and B. System A was consisted of a steam Rankine cycle (SRC) as a top cycle and an organic Rankine cycle (ORC) as a bottom cycle. System B was consisted of the same SRC as the top cycle and a Kalina cycle as the bottom cycle. The comparison of both systems has been made by changing a number of parameters. The chosen parameters for this comparison were bottom cycle mass flow rate, aperture area of the collector, top cycle condensing pressure, and bottom cycle turbine inlet and outlet pressures. Also, a short economic evaluation has been made between two proposed cascade cycles (Systems A and B). The result indicated that the Kalina cycle shows superiority over the ORC as the bottom cycle in a solar‐driven SRC. Furthermore, it was determined that the most effective parameters on the overall efficiency of the systems are the condensing pressure of the top cycle and the outlet pressure of the bottom cycle turbine. Also, among the chosen parameters, the outlet pressure of the bottom cycle turbine had the highest effect on the efficiency of the bottom cycles. Considering the economic aspect, the results showed that the levelized costs of energy for both systems are quite equal at 0.011 ($/kWh). [ABSTRACT FROM AUTHOR]

Parameter extraction of photovoltaic (PV) models based on measured current–voltage data plays an important role in the control, simulation, and optimization of PV systems. Despite the fact that various parameter extraction strategies have been dedicated to solving this problem, they may have certain drawbacks. In this paper, an effective hybrid optimization method based on adaptive rat swarm optimization (ARSO) and pattern search (PS) is presented for effectively and consistently extracting PV parameters. The proposed method employs the global search ability of ARSO and the local search ability of PS. The performance of the new algorithm is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. The extraction of parameters from several PV models, such as single‐diode, double‐diode, and PV modules, confirms the performance of the suggested method. Simulation results show that the proposed method surpasses other state‐of‐the‐art procedures in terms of accuracy, reliability, and convergence speed. [ABSTRACT FROM AUTHOR]

With the development of multi‐energy technology, the electric‐heat integrated energy system has become an important research direction for multi‐energy joint supply. The dynamic characteristics and energy storage capacity of heat supply network provide potential for joint dispatching of electric heating energy system. Aiming at the problem of electric‐heat joint dispatching, this paper presents an operation optimization model of electric‐heat integrated energy system considering the virtual energy storage characteristics of heat supply network. Firstly, according to the characteristics of transmission delay and user temperature fuzzy, the virtual energy storage characteristics of heat supply network are studied, and a model of the dynamic transfer of energy in the heat system was built. Then, the operation optimization model of the electric‐heat integrated energy system is established to minimize the operation cost. In order to improve the robustness of scheduling optimization results, the Monte Carlo Simulation embedded Quantum Particle Swarm Optimization algorithm is proposed to solve the model. In order to prove the validity of the proposed model, this paper selects a park (a 36 node thermal system) in the northwest region of China as a simulation case. The results show that the operation optimization method considering the virtual energy storage of heat supply network will greatly enhance the complementary potential of the electric‐heat integrated energy system and reduce the operation cost of the system. [ABSTRACT FROM AUTHOR]

The pipeline inspection gauge (PIG) is one of the most significant gauges in the field of oil–gas storage and transportation. It is essential to use PIG to clean and detect petroleum and natural gas pipelines. Predicting the deformation and stress distribution of the cup on the PIG is essential before pigging. To enhance these predictions, especially the deformation of the cup, a method for measuring the deformation of the cup based on reverse measurement was introduced and a novel finite element (FE) simulation model for investigation of the deformation of cups was proposed and verified in this paper. The point cloud data of the cup before and after entering the pipe can be obtained by the three‐dimensional (3D) laser scanner. Comparing the obtained point cloud data can measure the deformation of the cup. In addition, a 3D FE model was created to predict the deformation of the cup and the effective stress on the contact area. The result shows that there is a linear relationship between circumferential deformation and interference, while there is a nonlinear relationship between axial deformation, effective stress, and interference. These results provide a better understanding of the complex behavior of PIG during pigging. [ABSTRACT FROM AUTHOR]

In this study, we highlight some important factors in the specimen preparation methods for evaluating photovoltaic backsheet properties after accelerated aging. Two different sequences are considered: Method (I) cut‐then‐age: cut into 1‐cm wide strips and then expose to stress, and Method (II) age‐then‐cut: expose a larger sheet to stress and then cut into 1‐cm widths for mechanical property measurements. We also compare the effect of three cutting methods, (a) tensile specimen punch, (b) paper cutter, and (c) fresh razor blade. Several commercial backsheets were evaluated, with stress exposures including (a) pressure cooker test (PCT), (b) dry ultraviolet (UV) radiation exposure, and (c) UV combined damp heat tests. Fourier transform infrared spectrometer (FTIR) and intrinsic viscosity (IV) were used to analyze the materials on unstressed materials and samples exposed to PCT. The results show that both the cutting method and the time of cutting have an impact on the backsheet mechanical properties. Under UV exposure, Method II, age‐then‐cut, generally resulted in a higher average value, with more variation than Method I; however, if the side strips from Method II were excluded, the variation dropped to the same level. This is because the specimens at the sides of the sheet get additional damage from UV light from the exposed sides of the sample. In contrast to UV exposure, PCT specimens prepared by Method II result in lower average values and higher variability. This is attributed to embrittlement through the bulk of the sample where the cutting of embrittled specimens appears to result in more edge defects which can then initiate a break at smaller strains. The data suggest that for UV exposures, the specimens should be cut after aging and the exposed side specimens discarded, and that for PCT exposures, the specimens should be cut before the exposure. [ABSTRACT FROM AUTHOR]

The vertically‐jointed coal seam, especially after water drainage, is easy to lead to large‐area coal wall spalling (CWS). To alleviate the problem, a strut subjected to equivalent central‐concentrated force was proposed for elaborating critical conditions for coal instability based on CWS characteristics, determining the main control factors. The sensitivity analysis of factors driving CWS revealed that the coal between 3.3 and 4.56 m was the most vulnerable. A microblasting technique was proposed for controlling CWS by increasing the friction coefficient at the weakened‐primary coal interface, improving the coal stress, and reducing the equivalent mining height. The paper presented the application of universal distinct element code models to simulate the CWS mechanism with different joint mechanical parameters and microblasting heights. The reduction of spalling intensity indicates that the microblasting weakening of the upper part coal could improve the overall stability of the coal. LS‐DYNA is employed for simulating the fractures development between blasting holes, and the rationality of optimized blasting parameters is revealed. The fracture characteristics of the observation hole in field demonstrated that the hole was effectively fractured by microblasting. The spalling parameters including depth, width and frequency were obviously reduced, which showed the rationality in microexplosion controlling spalling. [ABSTRACT FROM AUTHOR]

This paper explores artificial intelligent training schemes based on multilayer perceptron, considering back propagation and genetic algorithm (GA). The hybrid scheme is compared with the traditional support vector machine approach in the literature to analyze both fault and normal scenarios of a centrifugal pump. A comparative analysis of the performance of the variables was carried out using both schemes. The study used features extracted for three decomposition levels based on wavelet packet transform. In order to investigate the effectiveness of the extracted features, two mother wavelets were investigated. The salient part of this work is the optimization of the hidden layers numbers using GA. Furthermore, this optimization process was extended to the multilayer perceptron neurons. The evaluation of the model system performance used for the study shows better response of the extracted features, and hidden layers variables including the selected neurons. Moreover, the applied training algorithm used in the work was able to enhance the classifications obtained considering the hybrid artificial intelligent scheme been proposed. This work has achieved a number of contributions like GA‐based selection of hidden layers and neuron, applied in neural network of centrifugal pump condition classification. Furthermore, a hybrid training method combining GA and back propagation (BP) algorithms has been applied for condition classification of a centrifugal pump. The obtained results have shown the good ability of the proposed methods and algorithms. [ABSTRACT FROM AUTHOR]

With the rapid increase of renewable energy generation worldwide, real‐time information has become essential to manage such assets, especially for systems installed offshore and in remote areas. To date, there is no cost‐effective condition monitoring technique that can assess the state of renewable energy sources in real‐time and provide suitable asset management decisions to optimize the utilization of such valuable assets and avoid any full or partial blackout due to unexpected faults. Based on the Internet of Things scheme, this paper represents a new application for the Supervisory Control and Data Acquisition (SCADA) system to monitor a hybrid system comprising photovoltaic, wind, and battery energy storage systems. Electrical parameters such as voltage, current, and power are monitored in real‐time via the ThingSpeak website. Network operators can control components of the hybrid power system remotely by the proposed SCADA system. The SCADA system is interfaced with the Matlab/Simulink software tool through KEPServerEX client. For cost‐effective design, low‐cost electronic components and Arduino Integrated Development Environment ATMega2560 remote terminal unit are employed to develop a hardware prototype for experimental analysis. Simulation and experimental results attest to the feasibility of the proposed system. Compared with other existing techniques, the developed system features advantages in terms of reliability and cost‐effectiveness. [ABSTRACT FROM AUTHOR]

Converting CO2, a major component of greenhouse gas in the atmosphere, into value‐added fine chemicals is beneficial from environmental and economic aspects. Except for various methods for CO2 reduction such as thermal catalysis, photocatalysis, electrocatalysis and biological reduction, hydrothermal reduction of CO2 to organics is becoming a novel and promising strategy. The hydrogen formed in situ from high‐temperature water could avoid the serious issues of hydrogen storage and transportation. This paper summarizes the recent advances on CO2 conversion to formic acid with metal‐ and biomass‐based compounds under hydrothermal conditions mainly focusing on the role of high‐temperature water, autocatalysis mechanism of metal reductants, and interfacial catalysis mechanism of metal/metal oxides. The effects of several experimental variables including temperature, reaction time and pH of the solution on formic acid yield are systematically illustrated as well. Finally, future efforts for fundamental researches and industrial applications of hydrothermal CO2 reduction are discussed. [ABSTRACT FROM AUTHOR]

Recently, phase change materials (PCMs) are acquiring much attention for the overall enhancement of photovoltaic (PV) systems' performance, because of their extraordinary property in temperature management. Many researchers have been incorporating PCMs in PV systems. However, the tested materials are principally organic PCMs. In this review, a comprehensive literature of PV systems with utilization of inorganic PCMs is introduced. Salt hydrates are the focus materials. They, in general, have comparative properties as organic PCMs and are better from safety and environmental standpoints. The characterization, properties, and applications of the salt hydrates in PV‐PCM research are discussed in this paper. Indeed, even most research shows promising outcomes, there is still need in data collection for more detailed and accurate system performance evaluation. Therefore, the real‐life application of this type of PV‐PCM system is still a long way from clear due to the discovered challenges. Finally, the possibilities of future research on PV‐PCM systems are highlighted, innovative technologies are to be discovered and addressed to defeat the current issues including system reliability and economic attainability. [ABSTRACT FROM AUTHOR]

Inertial weight adaptive quantum particle swarm optimization (DCWQPSO) algorithm can effectively improve the problem of particle falling into local extreme value. But the particle is still possible to fall into local extreme value in the later stage of particle evolution. When it is applied to photovoltaic multi‐peak maximum power tracking (MPPT), the tracking efficiency is not only reduced, but also may lead to tracking failure under the condition of sudden tracking of photovoltaic light intensity. To solve the above problems, this paper proposes a photovoltaic maximum power tracking (MPPT) control algorithm combining Lévy flight strategy with DCWQPSO algorithm. Lévy flight is a non‐Gaussian random process. The algorithm introduces Lévy flight strategy to change the mutation formula of particles and uses the characteristics of Lévy flight short step and occasionally long step jump search to improve the diversity of particles in the algorithm population. The algorithm proposed in this paper enhances the particle diversity, improves the convergence accuracy and speed of the algorithm, and overcomes the defects of the DCWQPSO algorithm. Simulation results demonstrate that the MPPT control algorithm proposed in this paper has fast‐tracking speed and high precision, which can effectively improve the maximum power tracking efficiency and dynamic quality of photovoltaic power generation system under uncertain environment, and it also has good robustness. [ABSTRACT FROM AUTHOR]