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Lime mud, a kind of industrial waste that produced in paper mill, was proposed as CO2 sorbent in calcium looping process. The carbonation performance of the lime mud was investigated in a dual‐fixed bed reactor (DFR) and a thermogravimetric analyzer (TGA). The carbonation kinetics of the lime mud in the chemical reaction controlled stage was analyzed by a surface reaction‐controlled kinetic model. The results show that the lime mud presents much poorer carbonation performance during the chemical reaction controlled stage compared with the limestone, mainly due to the high content of chlorine in the lime mud. A prewash treatment process was used to decrease the chlorine content to mitigate the sintering of the lime mud when calcined at high temperature. After prewash treatment, the prewashed lime mud shows much higher CO2 capture capacity during the chemical reaction controlled stage compared with the lime mud. A prolonged carbonation process successfully further enhances the microstructure and improves the carbonation performance of the prewashed lime mud in the chemical reaction controlled stage. The lime mud can be effectively used as CO2 sorbent in calcium looping process after prewash treatment and the following prolonged carbonation treatment. [ABSTRACT FROM AUTHOR]

Abstract Lime mud, a kind of industrial waste that produced in paper mill, was proposed as CO2 sorbent in calcium looping process. The carbonation performance of the lime mud was investigated in a dual‐fixed bed reactor (DFR) and a thermogravimetric analyzer (TGA). The carbonation kinetics of the lime mud in the chemical reaction controlled stage was analyzed by a surface reaction‐controlled kinetic model. The results show that the lime mud presents much poorer carbonation performance during the chemical reaction controlled stage compared with the limestone, mainly due to the high content of chlorine in the lime mud. A prewash treatment process was used to decrease the chlorine content to mitigate the sintering of the lime mud when calcined at high temperature. After prewash treatment, the prewashed lime mud shows much higher CO2 capture capacity during the chemical reaction controlled stage compared with the lime mud. A prolonged carbonation process successfully further enhances the microstructure and improves the carbonation performance of the prewashed lime mud in the chemical reaction controlled stage. The lime mud can be effectively used as CO2 sorbent in calcium looping process after prewash treatment and the following prolonged carbonation treatment.

Gas diffusion layers (GDLs) are the most rigid layers in a 5‐layer or 7‐layer membrane electrode assembly (MEA) of a proton exchange membrane fuel cell. Therefore, in the fuel cell analysis, the mechanical properties of GDLs have a great impact on the stress distribution of the membrane as well as the performance of the whole cell. However, the mechanical properties of GDLs are not sufficiently studied. Nonlinear behavior of GDLs under cyclic compression is discussed rarely in literature. The existing model takes both constraints into consideration, but due to a geometrical oversimplification of the carbon paper microstructure, it shows some deviation, which cannot be overcome by selecting appropriate parameters. In this paper, the geometry of carbon paper microstructure has been reanalyzed. Based on the improved geometry formula, a modified model of carbon paper GDL is presented and verified by experimental data. Furthermore, both the experimental mechanical characteristics are achieved as support data for the improved model. [ABSTRACT FROM AUTHOR]

Adsorption cooling and desalination systems have a distinct advantage over other systems that use low‐grade waste heat near ambient temperature. Since improving their performance, including reliability and failure prediction, is challenging, developing an efficient diagnostic system is of great practical significance. The paper introduces artificial intelligence (AI) and an automated machine learning approach (AutoML) in a real‐life application for a computational diagnostic system of existing adsorption cooling and desalination facilities. A total of 1769 simulated data points containing data indicating a failure status are applied to develop a comprehensive AI‐based Diagnostic (AID) system covering a wide range of 42 input parameters. The paper introduces a conditional monitoring system for adsorption cooling and desalination systems. The novelty of the presented study mainly consists of two aspects. First, the intelligent system predicts the health or failure states of various components in a complex three‐bed adsorption chiller installation using the extensive input data sets of 42 different operating parameters. The developed AID expert tool, based on selecting the best from 42 models generated by the DataRobot platform, was validated on the complex, existing three‐bed adsorption chiller. The AID system correctly identified healthy and failure states in various installation components. The developed expert system is very efficient (AUC = 0.988, RMSE = 0.20, LogLoss = 0.14) in predicting emergency states. The proposed method constitutes a quick and easy technique for failure prediction and represents a complementary tool compared to the other condition monitoring methods. [ABSTRACT FROM AUTHOR]

Delivering high power quality in single‐phase distribution has witnessed more challenges especially with the increased penetration of single‐phase distributed generation (DG). This paper proposes a smart solid‐state‐based transformer, which aims to replace traditional ones, for single‐phase distribution laterals, and provide load balancing and protection to the three‐phase main feeders, that is based on connecting the single‐phase lateral to the three‐phase main feeder through a power electronics converter. This converter transfers balanced power from and to the three‐phase feeder while automatically regulating the lateral single‐phase voltage, hence, assuring high power quality without requiring any transformer on‐load tap changer. A 7‐level packed‐U‐cells (PUC) single‐phase inverter topology was used to deliver single‐phase regulated sinusoidal voltage to the AC loads, and at the same time, it is able to deliver DC power to DC loads. The simulation and hardware‐in‐the‐loop (HIL) results have shown that the proposed topology delivers high power quality for both AC and DC loads under different operating scenarios. Moreover, the converter can play the role of a solid‐state protection device coordinated with other up‐ and downstream protective devices. Finally, this system can be integrated within the smart grid allowing more flexibility for automation and efficient control of the grid. [ABSTRACT FROM AUTHOR]

The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems. [ABSTRACT FROM AUTHOR]

In this paper, a proper noun "Region" is used for nm‐scale n‐type dopant‐rich region in p‐type Si crystal. Using this Region, certain solar cells have been assumed. By resonance absorption between photon energy and potential barrier of the Region, the cell can absorb most photons for visible light frequency without passing loss or thermal loss. This light absorption mechanism is different from conventional band gap absorption. Despite this benefit, output voltage is anticipated to decline according to the principle of detail balance. To control the decline, two methods are proposed in this paper. Theoretical energy conversion efficiencies for several cases are calculated with an ideal condition. The calculation result is over 70% as a theoretical value. [ABSTRACT FROM AUTHOR]

In this paper, the wake characteristics of Zell 2000 wind turbine under different tip velocity ratios are studied by using the lattice Boltzmann method and large eddy simulation. The adaptive mesh refinement method is performed to capture the fine flow structure and wake characteristics development. In this paper, we mainly focus on the effect of the tip speed ratio on the flow structure and unsteady characteristics of wind turbine wake. The three‐dimensional flow vorticity structures, the section vorticity diagram, the pressure fluctuation of wake and the lift coefficient of wind turbine wake are utilized to explore the effect of the tip speed ratio on the unsteady physics mechanism of wind turbine wake. With the increase of the tip speed ratio, the distance between two adjacent vortex rings along the axial direction gradually decreases, as the position of the broken vortex circles gradually approaches the center of the blade, separated vortexes are rapidly generated, and the coherent structure appears closer to the wind turbine. A relationship is established between the tip speed ratios and the positions of the broken vortex circles. It is further found that the dominant frequency amplitude gradually increases with the increase of tip speed ratio and the pressure amplitude spectra of vortex increases with the decrease of the distance between the wake and the center of the blade axis. The above series of studies can provide significant physical insight into deep understanding the influence of the tip speed ratio on the wake characteristics of wind turbines. [ABSTRACT FROM AUTHOR]

Due to the difficulties of system modeling, nonlinearity effects, uncertainties, and the availability of Wind Turbines (WTs) SCADA system data, data‐driven Fault Detection and Isolation (FDI) methods for WTs have received increasing attention. In this paper, using the wind turbine SCADA data, an effective FDI scheme is proposed using the K‐Nearest Neighbors (KNN) classifier. The operational data set is labeled by the status and warning data sets, and the labeled operational data set, after eliminating invalid data, feature selection, and standardization, is used for training and validation of the FDI model. Data imbalance, which is common in real data sets, does not affect the performance of the proposed method, hence there is no need for data balancing methods in this algorithm and the performance is not deteriorated by occurring false alarms. Therefore, the proposed method has provided impressive performance in FDI compared with previous research on this data set. Also, many of the fault classes addressed in this paper were not considered in previous works on this data set. [ABSTRACT FROM AUTHOR]

The research addresses the pressing need to understand wind conditions for optimal wind energy utilization, a crucial aspect of achieving global energy sustainability. While previous studies have focused on assessing wind energy potential and terrain roughness, gaps persist in understanding how wind and spatial conditions affect blade geometry. Therefore, this study aims to investigate the impact of local wind conditions on wind turbine blade geometry, integrating nonuniform wind inflow due to wind shear into the theoretical model. Through the paper, it explores blade design for varying inflow conditions and analyses the relationship between terrain type and blade designs. A novel approach to wind turbine blade design, utilizing a theoretical model that combines the local momentum theorem with insights from propeller vortex theory was adopted. Through the analysis, the authors demonstrate that variations in terrain significantly influence blade geometry, underscoring the necessity for tailored turbine designs based on local conditions. The findings reveal discrepancies in blade geometry across different turbine capacities and terrain types, offering novel insights into wind turbine performance optimization. By tailoring blade geometry to specific locations enhances resource utilization, offering insights for energy policymakers. The research provides validated methods for optimizing turbine geometric parameters, opening avenues for increased local investment in wind energy and promoting distributed energy development. Illustrated with wind plants examples, the paper concludes by outlining future research directions in this field. [ABSTRACT FROM AUTHOR]

Real‐time monitoring of the coal caving process in fully mechanized mining is crucial for achieving intelligent and efficient top‐coal caving. While the coal gangue identification method, employing vision and deep learning, has advanced in the realm of intelligent monitoring, it exhibits a dependency on high‐performance hardware. This reliance poses challenges for deploying identification equipment on mobile terminals, hindering the widespread application of this method. To address the issues above, the paper presents a lightweight algorithm, utilizing You Only Look Once version 5s (YOLOv5s), utilizing YOLOv5s for the real‐time perception of the top‐coal caving state in fully mechanized caving mining. We replace the backbone network of YOLOv5s with the ShuffleNetv2 structure that is more suitable for lightweight deployment, and add the Simple Attention Mechanism attention mechanism to the network structure to enhance the model's receptive field and feature expression ability, and reduce the impact of falling debris on the detection results. A dynamic experimental platform for top‐coal caving in fully mechanized caving mining for thick coal seams is set up, and preprocessing operations such as brightness, sharpening, and denoising are performed on the image data sets collected by high‐speed industrial cameras. Research results show that compared with the traditional YOLOv5s, the improved model's P, mAP, F1 score, and other indicators have increased by 3.4%, 2.1%, and 1.1%, respectively, the model size is 70% of the original, and the detection frames per second value has increased by 48.1%. The lightweight algorithm stabilizes the accuracy of coal gangue identification dramatically in real time. It dramatically reduces the computing pressure on the mobile terminal, providing basic theory and practice for real‐time monitoring of fully mechanized coal caving mining. [ABSTRACT FROM AUTHOR]

This paper proposes a preventive maintenance decision model for electric vehicle charging stations based on mutation operators and lifecycle optimization to address the impact of potential faults on maintenance effectiveness. By introducing the particle swarm optimization algorithm with mutation operator, a comprehensive analysis of opportunity service age factor and safety failure probability factor was conducted to establish an indicator system for the operation status of charging piles, and a potential fault identification model was constructed. By optimizing the life cycle, the balance problem between optimal maintenance life and optimal opportunity maintenance life has been solved, thus completing preventive maintenance decisions. The experimental results show that the accuracy of this method in preventive maintenance decision‐making for electric vehicle charging piles can reach 98%, with an average preventive maintenance decision‐making time of 1.6 s for load piles. At the same time, the risk probability value and load loss value are effectively controlled. This study has good application prospects in improving the preventive maintenance effect of electric vehicle charging piles. [ABSTRACT FROM AUTHOR]

How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh. [ABSTRACT FROM AUTHOR]

Microgrids with large‐scale photovoltaic systems constitute a large part of distributed renewable generation in many grids around the world. Managing the performance of such microgrids and especially their interaction with the main power grid is a challenging task, because it requires the control of renewable resources. This paper presents a comprehensive overview of DC‐DC converter structures used in microgrids and presents a new classification for converters. This paper also provides an overview of the control techniques of DC‐DC converters in DC microgrids and the advantages and disadvantages of the control methods are discussed. In connection with the increasing penetration of distributed generation sources (DGS) and renewable sources in power systems and their power management has been raised as a major concern and methods for power management have been investigated in this paper. Finally, a DC microgrid system, which includes a solar system, wind turbine, and battery, is simulated in MATLAB/Simulink software and its performance is analyzed. [ABSTRACT FROM AUTHOR]

The wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach. [ABSTRACT FROM AUTHOR]

Accurate prediction of photovoltaic power generation is a critical technical problem for utilizing solar energy. Aiming at the problem that the model parameters are difficult to obtain in applying photovoltaic power prediction methods, this paper has used long‐term monitoring data of output power, various meteorological data, and solar irradiation intensity of photovoltaic modules. This paper establishes the functional relationship between the output power of photovoltaic modules and the irradiation intensity through Pearson correlation analysis. By deducing the distribution relationship of irradiation intensity, the prediction model of irradiation intensity based on peak sunshine hours and sunshine duration is constructed and based on 340 sites across the country 64 years peak sunshine hours and sunshine duration query database. In this work, the theoretical value of the prediction model on sunny days is close to the measured value (R2 = 0.918–0.985). The solar radiation intensity on rainy days is weak, and the prediction accuracy is low (R2 = 0.838–0.930). The relative errors between the sunshine duration and the peak sunshine hours in the database are less than 4.55% and 4.79%, respectively, under sunny conditions in each quarter, indicating that the accuracy of the database meets the actual needs. [ABSTRACT FROM AUTHOR]

The vehicle's power battery is composed of a large number of battery cells series or in parallel. Due to the manufacturing process error and the different use environments, there are differences between the battery cells, and the battery pack will have inconsistency problems, which will increase the safety hazard. Therefore, it is of great practical significance to identify and warn about the inconsistency of power batteries. Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW) to segment the dataset and update the data of the diagnosis model in real‐time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that the recall ratio and precision ratio of the algorithm are 0.91 and 0.95, respectively, which is more suitable for inconsistent battery cell identification than other methods. If the SW size is 15, the warning effect is the best. Before the vehicle alarm occurs, the algorithm can realize early fault warnings, thus effectively avoiding the safety problems caused by inconsistency faults. [ABSTRACT FROM AUTHOR]

The distribution of fracture clearance is an important factor that determines the conductivity of carbonate fractures. In this paper, the morphological analysis of acid fractures and the evaluation of acid fracture conductivity were carried out to test the characteristics of acid fracture clearance and the conductivity of acid fractures under the conditions of different acid rock reaction rates. Combined with the distribution rule of fracture clearance and the calculation formula of acid fracture conductivity under closure stress, the calculation formula of acid fracture conductivity of carbonate rocks is established and verified, and the influence rule of the distribution of fracture clearance on the conductivity of acid fractures is simulated and calculated. The results show that with the increase of the closure pressure, the conductivity of the fracture with a closure pressure of 60 MPa is about 94% lower than that of the fracture with a closure pressure of 10 MPa. The increase in acid dissolution rate is beneficial to improve the fracture conductivity under various closure stresses, and the acid dissolution is beneficial to reduce the influence of the closure stress on the fracture conductivity of acid dissolution. When the confining pressure is 10 MPa, the acid corrosion rate increases from 0.0001 to 0.0004 g/cm2 s, and the conductivity of the acid corrosion fracture increases from 27 to 420 μm2 cm. As the dissolution rate of acid solution increases, the proportion of large clearance on the fracture surface increases, the average value of total clearance increases, and the proportion of contact on the fracture surface decreases. [ABSTRACT FROM AUTHOR]

The problems of insufficient recognition accuracy, poor real‐time performance and lack of consideration of actual working conditions in the process of intelligent construction of coal mines make this technology still in the research stage and not applied in practical engineering. The purpose of this paper is to establish an accurate and real‐time recognition model, which can quickly distinguish the vibration acceleration signals of coal and gangue under the influence of external factors such as impact position, velocity, and direction by using the different physical properties of coal and gangue particles. Therefore, the accuracy and real‐time of coal gangue recognition model established by different convolutional neural networks (CNN) structures and different position signal input are studied. First, to meet the real‐time requirements, an original CNN recognition model composed of single convolution layer and single pooling layer is established, and the data collected by seven sensors are input in the form of two‐dimensional matrix. However, the stability of the training and test results is insufficient. To solve this problem, once improved CNN (OI‐CNN) recognition model with multiconvolution layers and multipooling layers is built by deepening the network. The experimental results show that the stability and accuracy are improved, but the real‐time performance is poor. Furthermore, through parameter adjustment, the OI‐CNN is changed to the twice improved CNN (TI‐CNN), and the sensor data at different positions are input in the form of one‐dimensional vectors. The results show that the accuracy and real‐time performance of the TI‐CNN coal gangue recognition model are further improved. Finally, according to the research purpose of this paper, the weights of CNN indexes are given, and a multi‐index comprehensive evaluation system (MICES) is established. With the original CNN recognition model as the control, the OI‐CNN recognition model and the TI‐CNN recognition model at different positions are quantitatively compared to obtain the comprehensive evaluation scores of each model. The results show that the MICES of the coal gangue recognition model established based on the TI‐CNN structure and the data input of a single position sensor is the highest, while the sensor position has little effect on the recognition results. [ABSTRACT FROM AUTHOR]

Conventional energy resource problems made power system planners apply more renewable energy sources (RESs) in the power system. RESs have a significant impact on fulfilling the world's energy demand, protecting the environment at least in the exploration phase, and providing energy reliability as well as security for the new generation of power grids. In this regard, wind power generation is considered a promising solution. However, by increasing the share of wind power generation units in the power sector, some challenges, such as voltage problems, transient stability, increasing system losses, and line congestions can occur. Consequently, an alternative solution is required to accelerate wind power units' connections to the grid by preventing the problems caused by the penetration of wind power. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices can play an important role while they can improve power grid dynamic performance. This paper presents a review of the optimization techniques applied to accelerate the penetration of wind energy by exploiting FACTS devices. The topic identifies the objectives and optimization formulations, as well as schemes and models available in the published literature. This survey gives noteworthy insights to the researchers to cover available solutions in these regards. The literature in the field is summarized in different aspects, such as FACTS technology, objective functions, constraints, optimization methods, planning or operation purposes, and load flow types. At each aspect, a comparison is derived to recognize the most important issues. The result of the review is the finding of future work and contributions which can be done in the field of wind energy integration acceleration using FACTS devices and the output of the paper can be used as a guide for researchers to find a new path for research in this area. [ABSTRACT FROM AUTHOR]

It has been widely acknowledged that traditional packers will lose their elastic performance in the context of long periods of operation due to plastic deformation. This paper will introduce a swellable packer that can reduce the failure cases of production effectively. The deformation of the packer rubber in different media and temperatures has been analyzed. The pressure test of several packer rubber under different media is carried out in this paper. The reasonable expansion clearance between the rubber tube and the well wall is obtained by strength calculation to ensure the sealing reliability of the packer. Finally, the thermomechanical coupling calculation of the packer with different structural sizes is carried out. Experiments at different temperatures show that the higher saline concentration is associated with a lower expansion rate and a larger expansion rate in clear water. At the same time, the expansion of volume in clear water increases. In addition, the higher the external temperature is, the larger the temperature gradient is. When the temperature of the outer ring is between 100°C and 140°C, the internal temperature rises to 37°C under the thermomechanical coupling effect. [ABSTRACT FROM AUTHOR]

In the context of a low‐carbon economy, considering the output uncertainties of the renewable energy sources and the fluctuation of electrical price, the cost–benefit analysis is proposed for multi‐investor virtual power plant (VPP) under different risk preferences, and multi‐investor VPP capacity sizing problem is researched. First, the structural organization and flexible regulation characteristics of multi‐investor VPPs are analyzed. Second, integrated model and cost–benefit analysis indexes of VPP are built. Third, an optimal capacity sizing model of multi‐investor VPP is established, aiming at income maximization. Game theory is used to configure the distributed generators in the VPP under various cooperative modes and noncooperative modes of multiple investors. Fourth, according to the installed proportion of each distributed generator and the risk preference coefficient of each investor, the income allocation correction model based on the improved Shapley value method is proposed to stabilize the optimal VPP alliance. Finally, the demonstration project in the animation industry park of Sino‐Singapore Tianjin Eco‐City is employed as representative scenarios, which is used to validate the effectiveness of the proposed model. The results prove that the method proposed in this paper can guide knowledge for the investors with different risk preferences when planning the optimal capacity of multiple distributed generators in the VPP. [ABSTRACT FROM AUTHOR]

Fuel cell technology is a promising alternative to traditional internal combustion engines in various applications, especially in transportation applications. However, the high cost and limited lifetime of fuel cells have hindered their widespread commercialization. Accurately predicting fuel cell lifetime is crucial for reducing the cost of ownership, ensuring safety, and promoting the adoption of this technology. The objective of the present work is to develop a tool that is able to estimate the lifespan of a proton exchange membrane fuel cell and to predict its behavior to anticipate failures. Therefore, this paper contributes to proposing a multi‐input time‐series prediction network based on an echo state network, which takes the future current into consideration. A degradation trend extraction method is proposed in this paper and the remaining useful life of the fuel cell is predicted. Results have shown that the proposed methods in both short‐term and long‐term prediction have achieved satisfying prediction accuracy. [ABSTRACT FROM AUTHOR]

In response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow search algorithm to optimize BP networks' initial values and thresholds (ACMSSA‐BP). The proposed method is based on the sparrow search algorithm, where the number of producers and scroungers is adjusted through an adaptive factor. This improvement effectively transitions the search process from extensive full exploration to localized fine‐tuning search. In the position update phase of the producers, crossover mutation and dynamic search strategies are introduced to increase the diversity of good populations, prevent the algorithm from converging to local optima, and maintain its local search capability in the later stage. Using real transportation data from coal mining flame‐proof tracked vehicles, we applied correlation theory to extract model feature parameters and constructed a training data set to estimate the SOC. The results of both static and dynamic validation experiments have indicated that the ACMSSA‐BP method has delivered impressive performance in predicting SOC, as reflected in the mean absolute error, root mean squared error, and mean absolute percentage error values of less than 1.5%, 1.5%, and 1.6%, respectively. Compared with BP, SSA‐BP, CMSSA‐BP, PSO‐BP, and NARX_NN methods, the ACMSSA‐BP approach demonstrates enhanced accuracy in estimation, significant robustness, and impressive generalization capabilities. [ABSTRACT FROM AUTHOR]

Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices. [ABSTRACT FROM AUTHOR]

Environmental concerns have pushed toward electrified technologies for off‐road vehicle actuations that can lower greenhouse gas emissions and reduce energy consumption. Replacing a central diesel engine with a dedicated electric machine (EM) as a prime mover for the hydraulic supply offers several opportunities for so‐called ePumps (aka electric‐driven pumps) to maximize energy efficiency and limit the usage of electric materials. This paper discusses the impact of different choices for the ePumps architecture (i.e., fixed vs. variable displacement pump; variable speed vs. fixed speed electrical machine), and on their main design parameters in terms of size and efficiency. Although the procedure followed in the study could be extended to different types of electric and hydraulic units, the paper particularly considers ePumps based on permanent magnet synchronous machines combined with axial piston machines. The importance of properly considering the ePump drive cycle and its cooling requirements is taken into account while addressing energy efficiency, mass, and overall compactness of the solution. The results show that an ePump based on a variable displacement pump, when compared to fixed displacement ePumps, reduces the electrical machine size both in volume and mass up to 40%, when the high‐pressure demand is not combined with high flow rate demand, thus decreasing the cost of the EM. In all drive cycles, the variable speed EM–fixed displacement pump architecture has a higher efficiency, ranging from 1% to 5%, compared to the case of fixed speed EM–variable displacement pump. Finally, the paper compares the advantages and shortcomings of each ePump architecture presented, based on representative drive cycles. [ABSTRACT FROM AUTHOR]

With the escalating urgency for sustainable energy alternatives, solar power in urban landscapes has gained prominence. Building‐integrated photovoltaic (BIPV) systems are pivotal in this shift, blending efficient energy generation with architectural aesthetics. This review casts a spotlight on BIPV technologies, with a special emphasis on the less‐explored semitransparent photovoltaics (PVs). These systems are not only energy generators but also natural light facilitators, setting them apart from their opaque PV counterparts. Advancements in both transparent and opaque PV, such as crystalline silicon, are discussed. However, the paper prioritizes semitransparent PV's unique benefits, including harmonious integration with building design and simultaneous energy and daylight management. An exhaustive examination of current literature and developments sketches BIPV's trajectory, highlighting applications, challenges, and technological strides. The narrative extols the aesthetic, financial, and efficiency merits of BIPV, particularly the semitransparent variants' blend of functionality and design. Performance optimization of BIPV systems is scrutinized across various environments and architectural styles. This meticulous discussion seeks to clarify the status quo, emerging research avenues, and future prospects of BIPV in fostering a greener energy transition. The review compares BIPV configurations with traditional solar PV systems, charting a path for enhanced energy production, cost efficiency, and aesthetic integration, with semitransparent PV as a key player. By exploring the interplay between architectural design, energy efficiency, and urban planning, this paper aims to solidify the groundwork for future research, reinforcing the sustainability narrative in urban energy infrastructure. [ABSTRACT FROM AUTHOR]

With the continuous transformation of the global power system, ensuring the flexibility and stability of the power system has become a direction that the global energy industry has been striving for. This puts forward higher requirements for load forecasting, especially high‐precision short‐term residential load forecasting. However, the high volatility and uncertainty of electric consumption make this problem challenging. Existing methods usually focus on capturing temporal correlations and ignore the underlying spatial correlations in historical loads, which is crucial for accurate forecasting. In this paper, we propose a novel spatial–temporal gated fusion network (STGNet) for short‐term residential load forecasting including not only individual residential load but also aggregated load. By seeking both adaptive neighbors and temporal similarity neighbors, we construct a dynamic graph based on a data‐driven method. On this basis, we provide an adaptive graph gated fusion mechanism to capture fine‐grained node‐specific spatial dependence more accurately and comprehensively. Finally, a bidirectional gated recurrent unit (BiGRU) is utilized to learn the temporal dependence of load data. Extensive experiments on real‐world datasets are conducted and demonstrate the superiority of STGNet over the existing approaches. [ABSTRACT FROM AUTHOR]

Literature shows that only 56% of Kenyan households had access to electricity, with rural areas having the lowest access rate at percent. The high cost of extending the power grid to remote areas and power losses on distribution are significant challenges facing rural electrification. In addressing power accessibility problems, especially in rural areas, there is a need for tapping hydropower generation through the invention and implementation of in‐duct turbines to maximize the utilization of already existing pressurized water ducts that supply water in various parts of Kenya for hydropower generation. Makueni County is endowed with gravity‐fed water ducts with high potential for hydropower which can innovatively be produced by application of in‐duct turbines. This paper focuses on the assessment of energy needs and applications in rural areas. The research design was exploratory and experimental in nature. It was exploratory because, through an assessment, it sought to explore and identify the potential areas within the water supply lines for the production of hydropower to supply hydropower in Makueni County. It was experimental because the researcher developed (designed and fabricated) a hydro turbine for use in the production of hydropower from gravity water ducts of a diameter raging 100 mm. The research revealed that 62% (98) used solar power for lighting their homes, while 17% (28), 12% (20), and 8% (12) used lanterns, electricity, and kerosene lamps, respectively. Among the fuels assessed was firewood which was identified as the most used fuel at 89% (140). This was followed at a distance far by paraffin at 6% (9) of respondents. The households at 100% (158) identified electricity as a potential source of lighting for their household. The study recommends harnessing hydropower to enhance reach to 100% of the rural communities. The energy availability will provide opportunities for communities and institutions in rural areas to open their minds to business development and engage in income‐generating activities like the rearing of poultry and the development of light industries like the gridding of maize and other cereals. [ABSTRACT FROM AUTHOR]

Monitoring the health of lithium batteries is a crucial undertaking in ensuring the safe and dependable functioning of electric vehicles. Data‐driven methods have been proved to be an effective method for identifying the complex degradation process of batteries. To augment the precision of predicting the remaining useful life (RUL), this paper introduces a pioneering architecture for a denoising autoencoder (DAE). This architecture integrates a stacked convolutional neural network with subsequent layers of bidirectional gated recurrent units within an encoder–decoder framework. The utilization of the DAE network is employed as a means to effectively capture and represent the intricate and nonlinear knowledge associated with degradation data acquired from measured sources. Simultaneously, the reconstruction loss is incorporated into the total loss to improve the accuracy and generalization of the prediction model. The efficacy of the proposed approach is substantiated through the utilization of data sets sourced from the NASA Ames Prognostics Data Repository. The comparative findings suggest that the proposed approach demonstrates an exceptional ability to achieve precise and robust estimation in predicting the RUL, surpassing other advanced methodologies. [ABSTRACT FROM AUTHOR]

The high‐voltage pulse discharge technology, which is based on the electrohydraulic effect, can generate powerful, controllable, and repetitive electrohydraulic shock waves (EHSWs) in underground confined space, so it has been applied in the petroleum industry to improve the permeable of the reservoir. In this paper, a numerical model based on LS‐DYNA is built to study the damage analysis of the cement sheath and rock subjected to EHSWs during perforation completion. The results show that the stress state of the cement sheath and rock surrounding the perforation hole is determined by the shock pressure from both the perforation hole and the wellbore; the damage of the cement sheath and rock is mainly caused by tensile stress; the damage zone of the cement sheath is mainly distributed on the inner surface and near the perforation hole, while the damage zone of rock is distributed near the perforation hole; after multiple discharges, the damage of cement sheath and rock gradually accumulates, and the damage zone eventually forms a vertical plane along perforation holes. Overall, these results provide guidance for the safe application of the high‐voltage pulse discharge technology in the oil field. [ABSTRACT FROM AUTHOR]

The prediction of the efficiency of oil well pumping systems plays an important role in optimizing the energy efficiency parameters of these systems. Currently, the prediction of oil well pumping system efficiency relies primarily on mechanistic models, but these models are often overly complex in predicting efficiency. Some researchers have attempted to use deep learning to predict system efficiency, but due to insufficient consideration of influencing factors and the causal relationships between these factors and system efficiency, they often include irrelevant variables as influencing factors, leading to less accurate prediction models. In this paper, a hybrid model (MDS–SSA–GNN) is proposed for the prediction of pumping well system efficiency. The model consists of six parts: Pearson's product moment correlation coefficient (PPMCC), multidimensional scaling (MDS) transform, maximum–minimum normalization, sparrow optimization algorithm (SSA), graph neural network (GNN), and maximum–minimum inverse normalization. First, the size of the correlation coefficient between each influencing factor and the system efficiency is quantitatively calculated by using PPMCC. Second, the main influencing factors are downscaled by using MDS and normalized based on the principle of maximum–minimum normalization. Third, the GNN algorithm is used for the prediction of the pumping unit system efficiency, and the SSA algorithm is used for the optimization of the initial values of the network parameters. Finally, the prediction results are obtained by the maximum–minimum antinormalization. To validate the model's accuracy, this study randomly selected 100 actual oil wells for comparative analysis and analyzed the impact of structural parameters of the hybrid algorithm on the prediction accuracy of system efficiency. The analysis results demonstrate that the proposed model can effectively predict system efficiency and has a certain role in improving the accuracy of oil well pumping system efficiency predictions. [ABSTRACT FROM AUTHOR]

The basis for judging whether the battery pack is balanced is generally whether the voltage of the battery cell is consistent because the voltage of the battery cell is very easy to obtain. However, when the voltage of battery cells is the same and the battery management system (BMS) turns off the equalization, it will get poor balance performance. In this paper, a new balancing strategy is proposed, while the calculated state of charge (SOC) difference between the battery cells instead of the voltage reaches the set value, the BMS turns on or off, and the difference in the SOC between battery cells is obtained by adding a small amount of calculation to the difference in voltage between battery cells, due to the long balancing time of the battery, the battery will enter a steady state at the end of balancing, based on the voltage difference of battery cell, the voltage drop generated by the balancing current on the ohmic resistance and polarization resistance is added, combined with the open circuit voltage (OCV)‐SOC curve, the difference in battery voltage can be indirectly converted into a difference in SOC. This strategy will appropriately delay the battery balancing time to make up for the drop in the voltage of the balanced battery cell after the BMS turns off the balancing. In this paper, the proposed strategy and performance are analyzed in detail, the experimental platform is designed to verify, and the battery pack is tested under static and discharge conditions. Although the equalization goal is the same voltage between battery cells, using the voltage‐based equalization strategy, the voltage difference between the battery cells is 8 and 6 mV, respectively, while the strategy proposed in this paper is 2 and 3 mV, which has better performance. [ABSTRACT FROM AUTHOR]

Managed pressure cementing (MPC) is a new technology based on managed pressure drilling, which has a greater advantage in facing narrow density window formations. However, the existing pressure prediction models during MPC injection stage consider fewer factors and have lower accuracy. To this end, combined with the characteristics of the injection stage, a predictive model of the distribution of annular fluid type was first proposed. Then, based on the experimental results, fluid density and rheology as a function of temperature and pressure were fitted. The governing equation of temperature‐pressure field was established. Eventually the fluid performance parameters–temperature–pressure coupling prediction model was developed in this paper. By comparing the predicted pump pressure with the measured pump pressure, the maximum relative error is not more than 10%. Using this model, the fluid type distribution, temperature field distribution, and pressure field distribution were investigated. The results indicated that the distribution of fluid types in the wellbore presented a complex variation, with up to 10 fluids in the casing and up to five fluids in the annulus. The trend of temperature field is complex, with three turning points. The larger the formation temperature gradient, the higher the fluid temperature in the annulus. The influence law of fluid heat conduction coefficient is reversed at 6750 m. Decreasing drilling fluid density will trigger gas channeling, while increasing drilling fluid density will increase the risk of fracturing formation, and safe operation can be realized by MPC. The variation of the static pressure in the casing is more complicated than in the annulus and the annular circulation pressure in the eccentric casing is smaller than that of the concentric casing, which is due to the smaller annular friction pressure. This study can provide a theoretical basis for the prediction of hydrodynamic parameters during the MPC injection stage. [ABSTRACT FROM AUTHOR]

A large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver. [ABSTRACT FROM AUTHOR]

Solid fossil fuels still support the development of the energy and chemical industry. The clean and efficient utilization of solid fossil fuels can effectively alleviate the problems of energy shortage and environmental pollution. An in‐depth understanding of the kinetic process, models, and mechanism of pyrolysis of solid fossil fuels is also of great significance for the exploration and utilization technologies of petroleum resources. Thermogravimetric analysis is an effective method to study the pyrolysis process and kinetics with the advantages of high sensitivity, repeatability, and reliability for data acquisition. Researchers have conducted a lot of research on the thermogravimetric kinetics of solid fossil fuels in recent years, but there is a lack of systematic summary and comparison of kinetic methods, research progress, and trends. Therefore, a comprehensive study on pyrolysis kinetic principles, methods, and models of solid fossil fuels by thermogravimetric analysis is carried out in this paper. The influencing factors of pyrolysis kinetics of solid fossil fuels using thermogravimetry are elaborated and discussed in detail. This study also provides progress in recent years and prospects of thermogravimetric kinetics of solid fossil fuels. It is expected that this paper can provide certain technical support and theoretical guidance for the research on pyrolysis kinetics based on thermogravimetric analysis, and the exploration and development, clean, and efficient utilization of solid fossil fuels. [ABSTRACT FROM AUTHOR]

Various heating disturbances and faults in the heating network are necessary to be controlled and handled by some intelligent heating strategies with the increasing complexity of the heating network. This paper constructed the dynamic modeling of the heating system using standard thermal resistance and obtained a dynamic heat current model of the heating system. On this basis, we analyzed the heat transfer performance of the heating system. Five disturbances are selected, including the behavior of users, indoor heat source, heat exchanger heat transfer performance deterioration, pipe blockage, and pipe leakage. The effects of different disturbances on the overall system and user side water supply temperature were obtained by establishing a dynamic model for segmented heating pipelines. Feasible control methods for the heating network and load side are sorted out, mainly changing the water supply's temperature and the water supply's flow rate to reduce the water supply's fluctuation. Five specific control strategies are proposed for five types of disturbances. Comparing the case without control and the case with control, the results show that the fluctuation of water supply temperature is significantly reduced, and the control strategies can reduce the impact of disturbances on the heat network system and customers and improve the comfort of customers in the presence of disturbances. under the disturbance of pipeline leakage, the method proposed in this article reduces the temperature fluctuation amplitude by 75% and the fluctuation duration by 60%. [ABSTRACT FROM AUTHOR]

To improve the discharge equalization efficiency of the battery and prevent the occurrence of overdischarge, in this paper, the 18,650 ternary lithium battery is taken as the object of investigation, and an alternating equalization control system for the discharge process of serial cells is proposed. The system implements the alternating discharge of serial cells by switching on and off, using state of charge (SOC) as the equalization variable, and eventually completes the equalization control of the entire battery pack. Discharge simulations were performed in Matlab/Simulink for faulty and normal operating conditions of the battery pack, respectively. The findings indicate that even in the presence of a malfunction, the battery pack can continue to operate continuously for a while; in contrast, under ideal circumstances, the battery pack is capable of maintaining SOC balance throughout the discharge process. Eventually, five batteries are used to construct the experimental platform for the alternating equalization system. The battery pack can still perform selective discharge under fault conditions until the battery pack reaches the discharge cutoff condition. Under normal conditions, the maximum SOC difference of all five batteries can stabilize at about 1%. The experimental results show that the proposed equalization control system can achieve the equalization of battery discharge and prolong the discharge time, and can prevent the occurrence of battery over discharge. [ABSTRACT FROM AUTHOR]

In this paper, a micro‐bare‐tube heat exchanger structure with the tube bundle diameter varying gradually in the range of 1.0–0.4 mm is proposed. The airside friction and heat transfer performance correlations for this structure are given. This paper applies CFD simulation to develop and verify the correlation of the diameter gradually varied micro‐bare‐tube heat exchangers' performance. Compared with the traditional same diameter micro‐bare‐tube heat exchanger, the new structure not only reduces the pressure drop, but also improves the thermal hydraulic performance. The friction coefficient f is decreased by 8.59%, the pressure drop is reduced by 43.14%, the heat transfer coefficient j is increased by 12.07% compared to the constant diameter micro‐bare‐tube heat exchanger. At the same time, it reduces the void volume by 47.32% and the tube bundle manufacturing material by 28.57%. The proposed airside performance prediction equation can make the error of more than 70% source data within 10%, and the error of more than 90% source data within 20%. It is suggested to apply this empirical equation to 7–29 rows. [ABSTRACT FROM AUTHOR]

The remanufacturing industry is experiencing significant growth, resulting in a substantial increase in its carbon footprint. Unfortunately, this issue is not receiving adequate attention at present. Moreover, existing methods for analyzing carbon footprints in remanufacturing systems are inefficient and inaccurate due to the complex relationship between carbon emissions and the dynamic nature of the systems. In this paper, we propose a multisource data‐driven approach for carbon footprint analysis of remanufacturing systems. First, we examine the relationship between carbon emissions and the uncertainty surrounding end‐of‐life (EOL) product components, as well as the corresponding resource recovery strategies, such as reuse, recycle, and remanufacture. This analysis allows us to define the carbon footprint boundary of remanufacturing systems. Next, we establish a carbon footprint model for recycling strategies using recycling data. Additionally, we propose a carbon footprint model for remanufacture strategies by utilizing the back propagation neural network based on particle swarm optimization algorithm and data from the materials flow, energy flow, and waste flow of these strategies. By combining these models, we can accurately calculate the carbon footprint of remanufacturing systems. To demonstrate the effectiveness of our approach, we present a case study on the remanufacturing of an EOL automobile engine. This case study showcases how our methodology can be utilized as a valuable tool for analyzing carbon footprints and identifying strategies to reduce carbon emissions in remanufacturing systems. [ABSTRACT FROM AUTHOR]

Sanding production of the underground gas storage (UGS) in the depleted reservoir will cause the decline of effective storage capacity, and the erosion of well string and auxiliary equipment, and other engineering problems. A deep understanding of the sanding production contributes to maintaining the injection and production capacity of gas storage and the long‐term safety of the UGS. In this paper, core samples of five main sedimentary microfacies were drilled from the UGS in middle China. The mechanical properties with different conditions of the moisture content in multi‐cycling injection–production process were tested. The critical drawdown pressure (CDP) calculation methods for the UGS were established based on the stress analysis near the wellbore and the Mogi–Coulomb criterion. Adopting the finite element method simulation on the geological model of the target UGS, the geo‐stress of the formation was calculated by inversed analysis and validated based on the Kaiser effect of rock. Then, the CDP of two target wells of the UGS were investigated. The effects of the water moisture and the cycling times of the injection–production process on the CDP were also analyzed. The geo‐stress distribution shows that the horizontal maximum principal stress ranges from 53 to 68 MPa, and the horizontal minimum principal stress is 46–58 MPa in the target reservoir section, and the vertical minimum principal stress is 69–77 MPa in the target reservoir. The calculated CDP changed with the types of the sedimentary microfacies in the range of [6.27, 8.77] MPa, in which the CDP of the mixed mud and sand flat sedimentary facies was the lowest. The weakness of the CDP with the increasing of the moisture content and the cycling times were analyzed quantitively. [ABSTRACT FROM AUTHOR]

In their comment on our 2021 paper "How Green is Blue Hydrogen," Romano et al. purport to provide "a more balanced perspective on blue hydrogen, which is in line with current best available practices." We strongly disagree. First, we categorically dismiss their presentation on methane emissions. Methane dominates the greenhouse gas footprint of blue hydrogen in our analysis, and our estimates were based on very recent, peer‐reviewed science. Romano et al., in sharp contrast, use only three sources: (1) a 2015 non‐peer‐reviewed report (which gave reasonable values, although at the low end, since based on older science, but nonetheless compatible with our paper); (2) a 2018 report from the International Energy Agency (which also gave values consistent with ours, but has been updated by the Agency in a 2022 report to give much higher values that are very consistent with ours); and (3) a value from a cartoon on a web site from an oil and gas industry trade group (i.e., not supported by any data or references and is simply wrong based on peer‐reviewed science). We cannot stress enough that the Romano et al. criticism of our methane emission rates is based totally on this industry web‐site cartoon. Beyond the methane issue, our analysis used actual data for capture of carbon dioxide from real‐world operations. Romano et al. dismiss the use of real‐world data, and instead rely on presentations from theoretical studies. We find this fanciful. But even if one accepts their theoretical values for carbon capture, the greenhouse gas footprint of blue hydrogen remains unacceptably high because of methane. We unequivocally stand by our analyses and conclusions: There is no room for blue hydrogen in a decarbonized energy future. The Romano et al. result derives from a cartoon and hypothetical guesses, not scientific data. [ABSTRACT FROM AUTHOR]

This paper is written in response to the paper "How green is blue hydrogen?" by R. W. Howarth and M. Z. Jacobson. It aims at highlighting and discussing the method and assumptions of that paper, and thereby providing a more balanced perspective on blue hydrogen, which is in line with current best available practices and future plant specifications aiming at low CO2 emissions. More specifically, in this paper, we show that: (i) the simplified method that Howarth and Jacobson used to compute the energy balance of blue hydrogen plants leads to significant overestimation of CO2 emissions and natural gas (NG) consumption and (ii) the assumed methane leakage rate is at the high end of the estimated emissions from current NG production in the United States and cannot be considered representative of all‐NG and blue hydrogen value chains globally. By starting from the detailed and rigorously calculated mass and energy balances of two blue hydrogen plants in the literature, we show the impact that methane leakage rate has on the equivalent CO2 emissions of blue hydrogen. On the basis of our analysis, we show that it is possible for blue hydrogen to have significantly lower equivalent CO2 emissions than the direct use of NG, provided that hydrogen production processes and CO2 capture technologies are implemented that ensure a high CO2 capture rate, preferably above 90%, and a low‐emission NG supply chain. [ABSTRACT FROM AUTHOR]