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A traditional system-level thermoelectric model requires enormous computing power and time for simulation analysis, especially when multiple optimization algorithms are combined. This paper initially proposed a comprehensive surrogate mode for the accurate modeling, field analysis, and fast optimization of thermoelectric generators. The surrogate model is made up of an artificial neural network (ANN) and a conditional generative adversarial network (cGAN), which can achieve a prediction accuracy of 97 % and a structural similarity (SSIM) of 0.954. Using a twisted tape annular thermoelectric generator (TT-ATEG) as an example, the generalization capability of the comprehensive surrogate model is demonstrated by studying the change of the twisted tape geometry parameters on the output performance and field distribution of the annular thermoelectric generator. The combination of the surrogate model and NSGA-II achieves fast optimization of key parameters under different working conditions, and the computational efficiency is improved by 99.97 %. Meanwhile, the cGAN part can predict the pressure and heat flow fields within 5s to provide intuitive visual feedback. The trained surrogate model requires less computer arithmetic power compared to the traditional multi-physics field simulation software. The successful application of the comprehensive surrogate model in this work provides a new solution idea and an optimization method for system-level TEG. • A comprehensive surrogate mode for performance prediction and optimization. • Surrogate Model Integrates ANN for Data-Based and cGAN for Image-Based Modeling. • Data-based performance computational efficiency improved by 99.97%. • Rapid physical field visualization provided within 5 seconds. [ABSTRACT FROM AUTHOR]

By purchasing emission reduction equipment or reducing production scale, it is possible to effectively decrease CO 2. Therefore, understanding the marginal abatement cost (MAC) associated with these methods is crucial for making decisions. However, previous studies using the directional distance function (DDF) approach often mis-specified production functions and neglected data noise. They also assumed decision-making units (DMUs) to be on the production frontier and used proportional changes in inputs and outputs as abatement paths. This paper addresses these limitations by developing the convex quantile non-radial directional distance function (CQR-NDDF) method, which estimates the MAC of CO 2 and determines optimal abatement paths for DMUs without assuming a specific production function, employing linear programming techniques. Applying this method to 30 provinces in mainland China from 2011 to 2019, the study finds that China's CO 2 MAC increased from 182 to 247 yuan/ton. The lowest-cost abatement path varies by province and time. The club convergence and ordered probit model are employed to conclude that the second industry and urbanization increase the MAC of CO 2 , while factors such as foreign direct investment, openness level, and human capital decrease the MAC. Moreover, the CQR-NDDF method yields significantly lower MAC estimates than the NDDF method. In conclusion, this paper provides new insights into China's CO 2 MAC, emphasizing the importance of considering inefficiency and data noise in MAC estimation. We anticipate that utilizing CO 2 MAC as a benchmark for carbon trading market prices could lead to an increase in prices within China's carbon trading market. • A novel method for estimating CO 2 MAC has been developed. • Ignoring inefficiency will result in an overestimation of CO 2 MAC. • Three different emission reduction pathways are considered in the estimation. • The heterogeneity of the lowest cost abatement pathways has been identified. [ABSTRACT FROM AUTHOR]

Achieving precision in the prediction of buildings' dynamic heat load is crucial for the advancement of smart heating systems. This research highlights the urgent need to enhance the accuracy of models predicting dynamic heat load. Through literature review, distinguished machine learning and regression algorithms were chosen to formulate prediction models. These models employ a data time-step adaptive strategy, a physics-guided loss function, and fundamental principles of heat transfer. Optimization algorithms of a mathematical nature were utilized to fine-tune the parameters and the framework of long short-term memory (LSTM) and multi-layer perceptron (MLP) models. An analytical comparison was undertaken between physics-guided models and those not guided by physics. Principal conclusions are: 1) Pelican optimization algorithm (POA)-LSTM model emerges as superior in heat load prediction accuracy of an office building, with percentage errors for actual and simulated datasets ranging from −6.7 % to 5.8 % and −5.2 %–4.5 %, respectively, and the mean absolute percentage error (MAPE) standing at 2.3 % and 1.3 %. 2) The linear regression model exhibits the lowest precision, with a MAPE of 17.5 % and 4.0 % for the 7-day prediction results in the actual and simulated datasets, respectively. These findings provide support for improving heat load prediction in heating systems. • The authors have implemented a total of 20 physically-guided models for predicting building thermal load, among which the POA-LSTM model demonstrates the highest accuracy. • The disparities in thermal load prediction accuracy between the physically-guided machine learning models and the physically-guided mathematical regression models have been identified. • The accuracy of thermal load prediction was compared between the five physical guidance models proposed in this paper and the five non-physical guidance models mentioned in other papers. [ABSTRACT FROM AUTHOR]

The utilization of supercritical carbon dioxide (sCO 2) in the Brayton cycle presents several advantages, such as compact equipment, high efficiency, and rapid response time. The sCO 2 power cycle can be applied in coal-fired power, solar thermal power, and nuclear power systems. Over the past 10 years, many scholars have researched sCO 2 power systems. The compression (pressurization) process is a core thermodynamic process in sCO 2 power cycles, achieved through the use of compressors. Therefore, compressors are important components in sCO 2 power systems. This paper provides a summary of recent studies on compression processes and compressors for sCO 2 power systems. The impact of near-critical-point characteristics of the compression process on equipment and sCO 2 power cycle systems is discussed. The investigations of the performance parameters, design considerations, design methods, and performance prediction methods of sCO 2 compressors are reviewed. The typical research results on CO 2 condensation at the compressor inlet, the effects of operating conditions on compressor performance, as well as optimization of design parameters, are also summarized. Additionally, it provides a summary of sCO 2 compressor prototypes developed by research institutes worldwide and experimental studies. Finally, the current issues with sCO 2 compressors are addressed, and the main future research directions are proposed. This paper will contribute to the development of compressors and promote the acceleration of the commercialization of sCO 2 power systems. • Summary of recent studies on compression processes for sCO 2 power systems. • Near-critical-point characteristics of compression process is discussed. • Summary of compressor prototypes by research institutes and experimental studies. • Current issues and future research directions on sCO 2 compressors are presented. [ABSTRACT FROM AUTHOR]

This paper analyses the effect of energy poverty on respiratory health for a sample of 34 sub-Saharan African countries over the period 2000–2020. Using ordinary least squares (OLS), fixed effect, Driscoll-Kraay, Lewbel 2SLS, Kiviet and S-GMM, the results provide strong evidence of a negative and significant effect of access to electricity and access to clean energy for cooking on respiratory disease in sub-Saharan Africa. This result remained robust to changes in the variables of interest and to alternative estimation techniques. Furthermore, the results showed the existence of a non-linear relationship between energy indicators and respiratory diseases, including a U-shape and an N-shape. Based on these results, we encourage policy makers to better orient their energy policies towards the least polluting sectors in sub-Saharan Africa. In this way, the respiratory health of the inhabitants of sub-Saharan Africa can be improved. • This paper differs from existing work on energy poverty worldwide in the following respects: • It proposes a relevant determinant of respiratory health that has not been sufficiently studied in Sub-Saharan Africa. • It uses robust analytical methods to produce these estimates (Lewbel 2SLS and Kiviet). • It proposes useful policy recommendations for reducing energy poverty and reducing respiratory disease. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic (PV) power forecast is crucial to power systems. Solar irradiance forecast is the fundamentals of PV power forecast. Current researches merely focus on improving the forecast accuracy. There hasn't been comprehensive study on the spatial-temporal characteristics of solar irradiance forecast errors. Thus, this paper analyzed the error characteristics of solar irradiance forecast provided by European Centre for Medium-Range Weather Forecasts (ECMWF), one of the most accurate numerical weather prediction (NWP) products. ECMWF's forecasts were compared with ERA5 solar irradiance reanalysis data to reveal the error distribution characteristics. Four-year (2017–2020) data from the geographical region bounded by 63°N, −126°W, 21°S, and 36°E (covering most parts of Europe and North America) were studied. Experiments show that solar irradiance forecast errors peak at noon of the local time. Furthermore, correlations between solar irradiance forecast errors and other weather variables were also revealed. Experiments show that solar irradiance forecast errors have negative correlation with low cloud cover forecast errors and positive correlation with air temperature forecast errors. The possible reasons for the correlation relationship were also analyzed in detail. This paper systematically reveals the spatial-temporal characteristics of solar irradiance forecast errors and provides a useful guideline for solar PV system operation. • Error distribution of ECMWF's solar irradiance forecast in Europe and North America is revealed. • Solar irradiance forecast errors in Europe are smaller than those in North America. • Solar irradiance forecast errors peak at noon of local time. • Solar irradiance forecast errors have slight seasonal and monthly differences. • Correlation between solar irradiance forecast errors and other weather variables is revealed. [ABSTRACT FROM AUTHOR]

There is a long way for solid-state batteries from the laboratory to large-scale application and commercialization. To overcome a series of challenges, researchers and innovators seek to further understand the processing-structure-properties relationships of solid-state batteries. However, less literature explores the advances and opportunities in solid-state battery technology based on patent analysis. The paper adopts the technology of Natural Language Processing (NLP) to analyze patent documents and reveal the advances and opportunities for developing solid-state battery technology by constructing the patent Information Relation Matrix (IRM). This paper finds innovation activities in developing solid-state batteries have been increasingly active in recent decades, but are uneven across organizations. The electrolyte is a priority area of technology development, and the advances in developing solid-state batteries are perfecting conductivity, reducing interfacial resistance, and improving density and stability. By contrast, the opportunities are to reduce cost, prevent short circuits, and prolong the life cycle. The paper perfects the extant method of constructing IRM and gives insight into the advances and opportunities for developing solid-state batteries. Our findings can help innovators better understand advances in solid-state batteries or opportunities for developing solid-state batteries, from a global perspective. • Adopting the technology of Natural Language Processing to analyze patent documents. • Using the Information Relation Matrix to reveal technology advances and opportunities. • There are advances and opportunities for developing solid-state battery technology. • The advances are perfecting conductivity, reducing interfacial resistance, etc. • The opportunities are to reduce cost, prolong the life cycle, etc. [ABSTRACT FROM AUTHOR]

Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD) is designed. First, the penalty factor and the number of mode decompositions of VMD are optimized using the AOA algorithm and the original data are decomposed using the optimized VMD. Then the decomposed data is predicted using the ensemble deep random vector functional link network (edRVFL) model. The edRVFL uses rich intermediate features for the final decision, which can make the final result closer to the real data. In order to strengthen the anti-interference ability to the outliers, this paper robustly improves the edRVFL model, and the improved model is called ORedRVFL. ORedRVFL reduces the impact of outliers by introducing regularization and norm to balance the relationship between training error and weights. The experiments have proved that the model proposed in this paper outperforms other models in terms of anti-interference ability and prediction accuracy. • An outlier-robust edRVRFL prediction model is developed. • Use VMD to decompose the data and enhance the model prediction capability. • Fuzzy entropy is used as a criterion for data aggregation. • AOA optimizes the VMD hyperparameters to enhance the decomposition effect of VMD. [ABSTRACT FROM AUTHOR]

This work describes the one-dimensional, thermo-hydraulic model of the sCO 2 cycle Sofia developed to investigate optimal control methods and the behaviour of the cycle during operation. This dynamic model includes all devices such as turbomachinery, heat exchangers, valves, and piping including heat loss, in line with concept of the 1 MWe sCO 2 cycle, to be realised in the site of a fossil power plant in the Czech Republic. The model assembly and calculations were conducted using the commercial Modelica-based library ClaRa + using the simulation environment Dymola and in combination with another Modelica-based library, UserInteraction; the real-time simulations, with some parameter changes during the calculation, are made and described in this paper. Nominal parameters were achieved during the steady-state simulation, except for the lower mass flow of sCO 2. Transient simulation of power turbine start-up from standby state and results are also presented in this paper. The nominal state is achieved with the semi-automatic procedure in approx. 3 h. The simulation results allow more detailed analyses of control methods and a better understanding of real device control and behaviour during start-up, shutdown, or other transients. Careful manipulation of turbine valves in coordination with the pressuriser operation was identified as crucial for optimal control of the system. Also, the initial amount of CO 2 in the pressuriser affects its behaviour during transients. • Models of the PCHE and BPHE heat exchangers were prepared, and turbomachines models determined by CFD simulations were used. • Control system of the electric heater, which is the largest facility in the Sofia cycle, was designed. • Real-time simulations were performed using ClaRa+ and UserInteraction Dymola libraries. • Careful trubine valves manipulation and corresponding pressuriser control were identified as key control interventions. • Initial amount of CO 2 in the pressuriser affects its behaviour during transients. [ABSTRACT FROM AUTHOR]

To identify the influence of digital transformation on energy consumption, this paper introduces digitalization and energy elements into the multi-sector energy efficiency analysis model. Furthermore, this paper empirically tests the influence of digital transformation on energy efficiency from the micro level. The results show that the digital transformation indeed improves the enterprises energy efficiency, in which enterprises with higher energy efficiency are more affected. The improvement effect of digital transformation on enterprise energy efficiency continues to increase over time. In comparison, digital transformation shows more effective improvement effect on energy efficiency in enterprises that substantive transformation priority, with same group and maturity stage. Mechanism analysis shows that digital transformation can improve the energy efficiency of enterprises by reducing costs, increasing income, improving production efficiency and promoting technological innovation. In addition, the digital inclusive finance, government subsidies, intellectual property protection and service industry opening show positive regulatory effects. This paper proposes that, with the goal of peak carbon dioxide emissions and carbon neutrality, enterprises should actively increase digital investment and strengthen digital management of energy utilization. [Display omitted] • Digital transformation indeed improves the enterprises energy efficiency. • The promotion effect of digital transformation increases over time. • Digital transformation drives efficiency improvement and technological innovation. • Digital transformation contributes to cost reduction and benefit increase. [ABSTRACT FROM AUTHOR]

Climate change leads to the frequent occurrence of natural disasters. It has had a severe impact on agricultural economic growth in China. Based on data in China from 2004 to 2020, this paper uses a fixed-effect model to discuss the effects of natural disasters on agricultural economic growth. The empirical results are as follows. (1) Natural disasters negatively affect agricultural economic growth. The regression coefficient of natural disasters on agricultural economic growth is −0.1183. (2) The inhibition of agricultural economic growth by natural disasters is more prominent in areas with low government attention and average agricultural production conditions. (3) The mechanism analysis shows that natural disasters inhibit agricultural economic growth through channels that cause the spread of diseases and pests. (4) Expanding the scale of agricultural insurance can prevent the impact of natural disasters on agricultural economic growth. This research provides policy recommendations for preventing the negative impacts of natural disasters. Agricultural producers should raise awareness of disaster management and improve the accuracy of disaster warning mechanisms. • This paper measures the level of natural disasters in China. • This paper expands the application field of functional data analysis methods. • This paper examines the mechanism of natural disasters affecting agricultural economic growth. • This paper provides policy suggestions for ensuring agricultural economic growth. [ABSTRACT FROM AUTHOR]

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

Preventing the impact of natural disasters on urban-rural income inequality helps to achieve shared prosperity. This paper uses the functional entropy weight method to calculate the level of natural disasters from 2004 to 2020. Then, this paper discusses the impact of natural disasters on the urban-rural income gap. The empirical results are as follows. (1) Natural disasters have expanded the urban-rural income gap. The regression coefficient is 0.2663, which is significant at the 1% level. (2) In areas with a high proportion of the rural population, the impact of natural disasters is more prominent. (3) Mechanism analysis indicates that natural disasters have widened the urban-rural income gap by restraining rural investment and agricultural production. Finally, this paper suggests strengthening investment in the agricultural sector to mitigate the negative impact of natural disasters. • This paper uses the functional entropy weight method to calculate the level of natural disasters. • This paper quantitatively measures the impact of natural disasters on the urban-rural income gap. • This paper examines the mechanism of the impact of natural disasters on the urban-rural income gap. • This paper provides policy suggestions for narrowing the urban-rural income gap. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), have been widely used to predict the intermittency of wind power; however, the non-linear activation functions and backpropagation mechanisms in DL models increase computational complexity and energy consumption. This paper proposes a prediction model based on Green Learning (GL) to reduce energy consumption. The proposed GL model replaces the feature extraction of activation functions with a hybrid feature extraction approach combining categorical and numerical features. We also employ cluster centroids and quantile regression forest for classification/regression to eliminate the need for backpropagation in optimizing hyperparameters. Using Taiwan as a case study, this paper evaluates the risk of fluctuations in regional wind power generation in 2030. In simulations, the proposed GL model achieved excellent accuracy with energy consumption significantly lower than that of DL models. Our analysis also revealed that by 2030, fluctuations in wind power generation during the winter will exceed 40% of the peak supply capacity in the central region, indicating the need to enhance the resilience of regional power systems. • This is the first study to apply green learning to wind power prediction. • This study discriminated seasonal climate patterns at the regional level. • This study assessed the fluctuation risk under large-scale wind power integration. • The maximum hourly variability in wind power is projected to exceed 6,010 MWh by 2030. • We recommend the continued deployment of regional energy storage systems. [ABSTRACT FROM AUTHOR]

The paper explores Coastal Energy-Based Industrial Clusters (EBICs) and their role in advancing energy efficiency and sustainability through collaborative innovation. Economic growth theory and energy sustainability have been introduced into industrial clusters to illustrate indicators that have a greater impact on the development of EBICs. This paper proposes an EBICs development model based on the Cobb-Douglas function, in which accounts for various factors that drive the progress of such clusters. The outcomes of the economic model also provide insights into how the interaction of various factors affects the economic growth of EBICs and their eventual dominance in the energy market of coastal regions, dependent on gradually investing in areas such as research and development (R&D). Different development strategies demonstrate that the final development of a cluster has low dependence on the cluster's initial advantages. The study also illustrates how clusters can gradually monopolize the energy market, even with initial disadvantages. Through quantitative analysis, it showcases the transformation process of developing advantageous clusters into sub-clusters. Next, an energy symbiosis framework for coastal is proposed, which places greater emphasis on the multi-energy complementary system and reduces production costs. Finally, this paper also sheds light on shaping energy strategies for public authorities who shape economic policies at various levels of aggregation and in diverse dimensions. • Multi-energy complementarity provides a new path for sustainable development of coastal energy. • Hardware investment is critical to the development of energy-based industry clusters. • Clusters with better natural advantages may become sub-clusters due to incorrect development strategies. • The essence of energy cluster development is the result of the interaction of multiple influencing factors. • The economic growth of energy clusters depends on the proportion of investment in software and hardware. [ABSTRACT FROM AUTHOR]

The core of the energy transformation under the carbon-neutral vision is the gradual replacement of high-carbon energy by zero-carbon and low-carbon energy. However, the large-scale deployment of clean energy technology in turn relies on key minerals such as copper, aluminum, zinc, and nickel. Based on this, this paper conducts research on the demand for key minerals in clean energy transition and the role of key minerals in clean energy transition. In particular, this paper calculated the demand for mineral supply for China's carbon neutrality target, and the impact of some mineral resource constraints on the carbon neutrality path. Relevant results can provide a decision-making basis for the future development and utilization of key mineral resources, as well as the implementation of future carbon-neutral goals. • We evaluate the impact of crucial mineral resource on the carbon neutrality path. • Copper's availability greatly affects the adoption of new energy technologies. • Limited resources hinder early carbon capture and storage development. • Negative emission technologies are necessary for China's carbon neutrality. [ABSTRACT FROM AUTHOR]

To accelerate the realization of the "carbon neutrality and carbon peaking" (dual carbon) goal, the "Opinions on Financial Support for Carbon Neutrality and Carbon Peaking" (the "Opinions") advocates the comprehensive adoption of tax incentives, government procurement and market-diversified investment measures to support the green low-carbon innovation transformation of key industries. Taking China's strategic new energy vehicle (NEV) industry from 2009 to 2021 as a sample, this paper empirically investigates the innovation effect of the measures in the "Opinions" on listed NEV enterprises. The results show that: (1) Both "supply-side" tax incentives and "demand-side" government procurement encourage NEV enterprises to innovate independently, jointly and efficiently, and the "supply-side" tax incentives have a stronger innovation effect. At the same time, compared with joint innovation, government policies are more conducive to independent innovation of NEV enterprise. (2) Market venture capital also encourages NEV enterprises to innovate. Government policy signals to the market can attract venture capital, and the "demand side" government procurement signal effect is stronger. (3) Tax incentives and venture capital, government procurement and venture capital have played an innovative synergistic effect on NEV enterprises. (4) There is an innovation failure in the combination of tax incentives and government procurement, but the innovation effect appears after the introduction of market venture capital. This paper not only supports the effectiveness of the innovative measures in the "Opinions", but also recognizes the continuation of the vehicle purchase tax exemption policy for NEVs. • "Opinions" measures encourage NEV to innovate independently, jointly and efficiently. • "Supply-side" tax incentives have a stronger innovation effect. • "Demand side" government procurement has a stronger signal effect. • Government policy and venture capital play an innovative synergistic effect. • There is an innovation failure in policy mix before venture capital intervention. [ABSTRACT FROM AUTHOR]

The scale of pig breeding in China is the largest in the world, and the pollution problem of breeding wastewater is serious. Supercritical water gasification (SCWG) process is a clean and efficient treatment technology for breeding wastewater. This paper involved constructing the molecular model of the primary organic components in pig breeding wastewater. The effects of time, temperature and mass concentration, and the interactions of different components on the SCWG characteristics of pig breeding wastewater were studied by molecular dynamics (MD) simulation, and the decomposition process of lignin and lipid molecules in the SCWG process were analyzed, and the possible intermediates were explored. The results showed that the effect of parameters on carbon gasification efficiency (CE) and H 2 production follows the order of temperature > time > mass concentration. During the gasification of lignin and lipid molecules, the C–O–C bond was broken first, and the gasification rate of lignin molecules was slow due to the benzene ring. Interactions among different organic decompositions hindered the gasification process. This paper will provide theoretical guidance for the selection of operation parameters and the control of products in the actual pig breeding wastewater treatment process. • The MD model of wastewater containing complex organics was established. • The effects of key parameters on SCWG of pig breeding wastewater were studied. • The detailed SCWG mechanisms of pig breeding wastewater were analyzed. • The intermediate products from SCWG of pig breeding wastewater were explored. • The interaction mechanisms of different organics during SCWG were analyzed. [ABSTRACT FROM AUTHOR]

Currently, lignite-fired boilers often feature low thermal efficiency, difficult coal pulverizing and poor combustion stability owing to lignite having high water content. A novel lignite-fired power generation process is proposed in this paper with using a superheated-steam-drying open-pulverizing-system boiler (SSD-OPSB) and a deep utilization of waste heat from the steam for coal drying. Through using an open pulverizing system, the problems of low furnace temperature and low boiler efficiency are resolved. Meanwhile, the operation safety of the coal pulverizing system is ensured by using superheated steam as the drying agent. This paper described the working principle and engineering feasibility of the SSD-OPSB process, and established analysis model for its thermo-economic performance. Energy savings and environmental benefits of applying the process were calculated and discussed based on an in-service 600 MW supercritical power unit firing lignite with water content of 39.5% at its turbine heat acceptance condition. The result shows that for the power unit, applying the SSD-OPSB process can decrease coal consumption by 6.86% under the same power supply, meanwhile recovering water by 132.2 t/h and reducing CO 2 emission by 27.78 t/h. It indicates that the SSD-OPSB process would significantly benefit lignite-fired power plants regarding energy-saving and environmental protection. • Lignite-fired boilers can benefit greatly from applying open coal pulverizing systems. • The benefits include marked coal-savings, enhanced coal combustion and water recovery. • A novel superheated-steam-drying open-pulverizing-system was proposed for applications. • Waste heat and water recovery system was designed to the new power generation process. • The model of analyzing thermo-economic performance of this process has been developed. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of solar gas heaters that utilize an air gap between the glass cover and the absorber, along with radiating gas. By using a participating fluid as the working gas, thermal radiation can be absorbed, emitted, and scattered, enhancing the heat transfer between the absorber and the flowing gas. Free convection airflow inside the air gap adds to the forced convection airflow through the solar collector's duct. The study utilizes the κ-ε model and the discrete ordinate method for radiative intensity computation to analyze the flow and energy equations. Numerical results indicate a significant increase in thermal efficiency, particularly at lower gas mass flow rates, when employing radiating gases with high radiative absorption coefficients. The research shows about 40% improvement in thermal efficiency is obtained in test cases at a gas mass flow rate of 0.01 kg/s. A gas flow optical thickness of τ = 2 is found to be optimal among the investigated parameters. In addition, this paper demonstrates high efficiency of up to 70% for plane solar collectors without the need for configuration changes by using working gases with high radiative properties, such as pressurized CO2. • Effect of radiating gas on the performance of solar gas heaters (SGHs) is examined. • An air gap is embedded between the glass cover and the absorber. • The efficiency of SGH has increased 40% for low mass flow of 0.01 kg/s. • The optical thickness of τ = 2 for the gas flow gives the best performance. • A simple plane collector could have efficiency of 70% just by utilizing a working gas with high radiative properties. [ABSTRACT FROM AUTHOR]

This paper establishes a multi-objective optimization model for railway heavy-haul trains, focusing on reducing carbon emissions and improving transport efficiency. The model integrates optimization of the route and the vehicle load rate, significantly reducing carbon emissions and enhancing transport efficiency. It addresses the challenges and characteristics of heavy-haul trains, introducing multi-objective optimization problems related to transport carbon emissions and efficiency. Using a pigeon-inspired optimization algorithm, the model considers joint constraints between carbon emissions and transport efficiency objectives. To overcome challenges in multi-objective transportation problems, the paper proposes a forward-learning pigeon-inspired optimization algorithm based on a surrogate-assisted model. This approach calculates the quality of the candidate solution using a surrogate model, reducing time costs. The algorithm employs a forward-learning strategy to enhance learning from non-dominant solutions. Experimental validation with benchmark functions confirms the effectiveness of the model and offers optimized solutions. The proposed method reduces carbon emissions while maintaining transport efficiency, contributing innovative ideas for the development of sustainable heavy-duty trains. [Display omitted] • A model is build for carbon emissions reduction and transport efficiency enhancement. • The model combines route and load rate optimization. • The model achieves carbon emission reductions while improving transport efficiency. • A novel meta-heuristic algorithm for solving the railway heavy-haul trains model. • The proposed algorithm introduces fresh insights for railway heavy-haul trains. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind speed is significant importance in various applications such as renewable energy management, weather prediction, and aviation safety. However, more papers only focus on the time series of wind speed, ignoring the impact of other factors on wind speed, which will reduce the effectiveness of wind speed prediction. Therefore, an innovative methodology is suggested for both point and interval forecasts of multivariate wind speed time series using a Dual-layer Long Short-Term Memory (NVDL) in this paper. The proposed multivariate forecasting model takes into account the dependencies and correlations among different variables, which are essential for capturing the complex dynamics of wind speed variations. The first layer of the Dual-layer LSTM is responsible for capturing the temporal dependencies within each variable individually, while the second layer captures the interdependencies among the variables. By incorporating this dual-layer architecture, our model effectively captures the complex spatiotemporal patterns present for multivariate wind speed information. The results obtained from both interval and point prediction demonstrate that the proposed methodology outperforms all comparative models in the precision and stability of wind speed forecasting. Therefore, the proposed forecasting methodology, characterized by minimal prediction errors and exceptional generalization ability, can be a reliable tool for smart grid programming. • This article proposes an enhanced version of the dual-layer LSTM structure. • A self-adaption preprocessing to manage the impact of noise-induced interference. • Fuzzy interval forecasting can give the upper and lower of wind speed effectively. [ABSTRACT FROM AUTHOR]

Porous surfaces are a common modified surface used to enhance boiling heat transfer. In this paper, a method for preparing gradient porous surfaces is presented, that is, magnetic stirring is added to the electrolyte and the cathode surface to be deposited is placed vertically downward. Within the Cl ion concentration range of 30–140 mg/L, increasing the Cl ion concentration results in lager pore sizes. When the Cl concentration was greater than 120 mg/L, the average pore size of the surface gradually increased from the center to the edge, and the number of pores gradually decreased from the center to the edge. All surfaces showed superhydrophilicity. The critical heat flux of Sample#3 is 158.72 W/cm2, which is 67% higher than the smooth surface. The wall superheat of Sample#3 was only 15 °C in the critical state. This porous surface has plenty of nucleation sites. These nucleation sites are gradually activated when the heat flux increases. At low heat fluxes, heat transfer on these porous surfaces presents a disadvantage because the nucleation sites are not fully activated. At high heat fluxes, these porous surfaces show a decrease in wall temperature. Consequently, the porous surface prepared in this paper has good boiling heat transfer at high heat flux. [Display omitted] • An inverted deposition method was designed to prepare gradient porous surfaces. • The gradient porous surface is superhydrophilic and provides a large number of nucleation sites. • An appropriate increase in Cl-ion concentration increases the pore size and pore wall thickness. [ABSTRACT FROM AUTHOR]

Lithium-ion battery is a kind of energy storage devices with complex internal reaction and many factors affecting its performance. Accurate prediction of its SOH (State of Health) is of great significance to prolong its service life and improve safety performance. However, the current prediction for SOH has the difficulties of selecting health factors and using data-driven methods with opaque mechanisms. In this paper, a data-driven model will be established with the capacity change during aging of lithium-ion batteries as a health indicator to realize precise prediction of capacity degradation. Firstly, the feature parameters were extracted and analyzed from the battery cyclic aging test dataset. Two in-situ nondestructive characterization methods, incremental capacity analysis curve and differential thermal voltammetry curve, were utilized to resolve the evolution paths of the feature parameters. After that, a data-driven battery capacity degradation estimation is realized based on the GEP (Gene Expression Programming) algorithm, the performance is compared with the existing vehicle-end and cloud-end models, and a higher-accuracy SOH stacking model is developed with an increase of less than 1 ms in computational time. The results indicated that the GEP model proposed in this paper has obvious advantages in terms of physical explain-ability, computational efficiency and robustness. • Establishing a high-precision battery SOH estimation model based on the GEP algorithm. • The relationship between ICA and DTV curves with aging evolution laws is revealed. • Fusion of GEP model with existing vehicle-end and cloud-end models using stacking strategy. [ABSTRACT FROM AUTHOR]

Decarbonising the heating sector is one of the key elements to realizing the ambitious dual carbon goals of China, which is the largest carbon emitter and energy consumer globally. Currently, district heating (DH) systems have penetrated approximately 88% of the urban heating areas in Northern China. Nevertheless, around 90% of the heating demand in China still relies on fossil fuels. A larger scale integration of renewable energy and waste heat sources into the DH systems is critical for decarbonising the entire heating sector in China. However, a deeper level of comprehension is required to harness its full potential. This paper provides a thorough investigation of the status, potential, and national policy schemes of renewable energy and waste heat recovery in the DH systems of China. Combined with a critical review of recent literature on relevant areas published in both international and Chinese domestic sources, the trends, challenges, and future perspectives are discussed from scientific research and practical implementation aspects. This paper highlights the synergy of the integration of renewable energy and waste heat sources in DH, the energy efficiency improvements as well as the use of thermal storage technologies through the implementation of 4th generation district heating and smart energy systems that could offer a more economically viable pathway forward. • Focus on renewable energy and waste heat (REWH) in the district heating (DH) systems in China. • A systematic review of REWH in Chinese DH systems from status, policy, and literature aspects. • Identification of research gaps based on the review of both international and Chinese journals. • Policy recommendations proposed to promote REWH in Chinese DH systems. • The introduction of international experiences on REWH in DH for analysis in the Chinese context. [ABSTRACT FROM AUTHOR]

Digital infrastructure serves as a crucial foundation for development in the era of the digital economy. In light of this, this paper takes digital infrastructure construction as its focal point. Utilizing Python technology and the Meta-frontier non-radial directional distance function to calculate the level of digital infrastructure construction and industrial energy efficiency. It then employs a dynamic panel data model to analyze the impact effects and mechanisms of digital infrastructure construction on industrial energy efficiency. Additionally, threshold models and modified gravity models are employed to explore the nonlinear effects and spatial impacts of digital infrastructure construction on industrial energy efficiency. The research findings reveal that the enhancement of digital infrastructure significantly improves industrial energy efficiency. Moreover, indicate a shift in the impact of digital infrastructure construction on industrial energy efficiency from inhibitory to promotional as the level of digital infrastructure construction increases. The mechanism of this effect suggests that digital infrastructure construction can promote the improvement of industrial energy efficiency through green technological innovation and the advancement of industrial structure. Further analysis shows that the spatial network characteristics of digital infrastructure construction notably elevate the level of industrial energy utilization efficiency. This study provides practical policy implications for sustainable development. • Digital infrastructure construction markedly enhances industrial energy efficiency. • There is a non-linear picture of the impact of digital infrastructure construction on industrial energy efficiency. • The effects of green technology innovation and advanced industrial structure are crucial channels. [ABSTRACT FROM AUTHOR]

Floating offshore wind turbines, as large equipment for deep-sea wind energy utilization, must be carefully studied for their aerodynamic performances under platform motions. In experiments, the model wind turbine designed by the traditional thrust coefficient similarity is usually insufficient for unsteady aerodynamic reproductions of the prototype wind turbine. In this paper, a dynamic aerodynamic similarity criterion based on the mapping of the optimal tip speed ratio is introduced for wind tunnel model tests, and a model wind turbine is designed using the NREL 5-MW prototype wind turbine. In this case, to avoid the over-height model tower aroused in pitch motions, a method of equivalent surge motions instead of pitch motions is proposed and validated for feasibility. Then dynamic aerodynamic similarities under different pitch amplitudes and frequencies are validated by the Unsteady Blade Element Momentum theory. Results indicate that temporal and spatial characteristics and hysteresis effects of thrust and power coefficients all maintain better similarity with those of the prototype wind turbine than those of the model wind turbine designed by the traditional criterion. This paper provides a criterion with equivalent pitch motions for unsteady aerodynamic studies in wind tunnel model tests, especially the high-fidelity Hardware/Software-In-the-Loop test. • A new similarity criterion is proposed for FOWT wind tunnel model tests. • The feasibility of equivalent surge motions instead of pitch motions is verified. • The problem of over-height model tower is avoided by equivalent pitch motions. • Spatiotemporal and hysteresis characteristics of unsteady aerodynamics are analyzed. • Unsteady aerodynamic performances of the prototype can be reflected more accurately. [ABSTRACT FROM AUTHOR]

To attain China's target of carbon neutrality by 2060, the implementation of the Energy Consumption Trading (ECT) system serves as an effective approach to improve energy efficiency and mitigate CO 2 emissions. Nevertheless, it is crucial to consider not only the importance of carbon reduction effect caused by ECT system but also the associated cost. Therefore, this paper applies the spatial difference-in-differences (SDID) model to assess the direct and spillover impacts of the ECT policy on the carbon abatement cost, which is measured by shadow price of CO 2 emissions and calculated using SBM-DEA model. Panel data encompassing 279 Chinese cities from 2010 to 2019 are employed for analysis, with additional insights provided through heterogeneity and mechanism analyses. The results show that ECT policy increases the local carbon abatement cost in pilot cities but decreases the cost of surrounding cities. Meanwhile, such a declination effect in neighboring cities with a spatial attenuation boundary of 1000 km. The heterogeneity analysis suggests that the implementation of ECT policy leads to an increase in the carbon abatement cost in cities located in non-Eastern Coastal regions, cities with abundant natural resources and cities with small populations. The mechanism analysis indicates that the ECT policy implementation facilitates the improvement of green technology in the neighboring cities of the pilot project, consequently lowering the carbon abatement cost. Besides, this policy also results in a decrease in the carbon intensity of the pilot cities. Owing to the U-shaped association between carbon intensity and the carbon abatement cost, the policy implementation stimulates an increase in the cost within the pilot cities. • This paper estimates the carbon abatement cost (MAC) using SBM-DEA method. • The SDID model is used to investigate the impacts of ECT policy on carbon MAC. • We test whether green technology and carbon intensity are mediating variables. • ECT policy increases the carbon MAC in pilot cities through carbon intensity. • ECT policy lowers the carbon MAC of adjacent cities through the green technology. [ABSTRACT FROM AUTHOR]

The risk associated with the dramatic volatility of oil prices has become an important factor affecting the stability of the financial system. Through a complex network viewpoint, this study uses firm-level data to investigate systemic financial risk contagion from the oil market to the financial markets. Our study considers the risk amplification effect in financial network and provides an in-depth empirical analysis of risk contagion among financial institutions under the oil market risk shock. According to the findings of this paper, systemic financial risk increases dramatically during oil crisis events. This paper also captures important characteristics of the network, including major risk receivers and risk transmitters, and finds that banks suffer more risk losses from the oil market. Furthermore, considering the important role of the financial system in the macro economy, the systemic financial risk caused by oil market risk has long-term negative effects on economic output. • This study examines the risk contagion from oil market to financial markets from complex network perspective. • This paper suggests that systemic financial risk rises significantly during oil crisis events. • This paper finds that banks suffer more risk losses from the oil market risk. • Systemic financial risk caused by oil market risk has long-term negative effects on economic output. [ABSTRACT FROM AUTHOR]

The carbon neutrality agenda requires that carbon lock-in enters an endgame. A well-developed energy system, which is secure, equitable, and sustainable is paramount to getting rid of carbon lock-in and realizing decarbonization. We carry out systematic research to empirically study the possible carbon lock-in reduction effect of energy trilemma based on the instrumental variable-generalized method of moment (IV-GMM) model. We also detect heterogeneous effects in terms of geographical locations and endowments of capital, as well as the impact mechanisms. Moreover, this paper highlights the mediating and threshold role of environmental regulation. We thus get the following conclusions. (1) The paper's primary findings underscore that the continuous increase of energy trilemma exacerbates carbon lock-in and hampers the process of decarbonization; (2) Energy trilemma has a higher stimulating effect on carbon lock-in in provinces that lie in the central region and that have a comparatively low level of physical, social, and human capital; (3) Energy trilemma can also indirectly aggravate carbon lock-in by inhibiting technology innovation and exacerbating carbon emissions intensity; (4) The interaction of environmental regulation and energy trilemma can alleviate the energy trilemma - carbon lock-in nexus, and environmental regulation also shows a significant threshold effect. • We investigate the impact of the energy trilemma on carbon lock-in. • Energy trilemma eradication is an effective way to realize the carbon lock-in endgame. • The nexus between energy trilemma and carbon lock-in is more prominent in provinces with high capital endowments. • Environmental regulation shows significant mediating and threshold effects. • The exacerbate carbon lock-in effect of energy trilemma depends on technology and intensity effect. [ABSTRACT FROM AUTHOR]

To achieve an efficient tidal current turbine (TCT), the impact of three biomimetic trailing edge designs (i.e., slabbing, slab-toothed, and serrated), a blade configuration design (i.e., sweep design) and their various combinations on TCT's power coefficient are numerically and experimentally studied in this paper. The novelty of this paper lies in its pioneering exploration of the synergistic effects of multiple biomimetic designs on the TCT's power generation and start-up performance, as well as the interplays between the sweep design and biomimetic trailing edge designs. Both numerical simulations and experimental studies provide compelling evidence that all three biomimetic trailing edge designs enhance the blade's energy capture efficiency. For example, at a 7° angle of attack, the lift-to-drag ratio for the slabbing trailing edge blade increases by 12.124% and the slab-toothed trailing edge blade increases by 11.770% compared to the standard blade. The incorporation of sweep design and biomimetic trailing edge design produces a further enhancement in both the TCT's power generation and startup performance. In particular, the simultaneous implementation of slabbed trailing edge design and sweep design makes the power generation efficiency of TCT increase by up to 54.72% when compared to TCTs using standard straight blades. • Paper explores synergic effects of multi-biomimetic designs on TCT's efficiency. • Slabbing and slab-tooted tailing edge designs surpass serrated trailing edge design. • Combined use of sweep and slabbing trailing edge design results in best performance. • All biomimetic designs considered in the study boost the TCT's startup performance. [ABSTRACT FROM AUTHOR]

Hydrogen is a promising energy carrier, but safety concerns must be addressed to ensure its widespread use. Aiming at the problem of hydrogen leakage and diffusion in a hydrogen workshop (HWS), a hydrogen diffusion model in the actual temperature stratified environment is established. The law of hydrogen diffusion evolution in this environment is analyzed by simulation and compared with the numerical model. It is revealed that leaked hydrogen in temperature stratified environment will be trapped at a certain height and spread horizontally, which is called the "locking phenomenon". This paper explores the conditions under which this phenomenon occurs by comparing hydrogen diffusion under different temperature gradients and leakage rates. To reduce the risk of "locking phenomenon", the vents were configured for the HWS vents. The results show that the hazard area of hydrogen diffusion is reduced by 41.10% after the vent configuration. This paper provides valuable insights into the evolution of hydrogen diffusion in a temperature stratified environment and proposes an effective solution to mitigate the associated risks. • A hydrogen diffusion model in temperature stratification is established. • The locking height and trajectory of hydrogen are studied. • The necessary conditions for the occurrence of "locking phenomenon" are given. • The deployed vent effectively reduces the hazard area after hydrogen diffusion. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-objective eco-routing strategy aimed at optimizing driving routes by minimizing both energy consumption and travel time. First, the vehicle and powertrain dynamics are modelled. The energy consumption of three powertrain types, including hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV), are calculated based on a white-box model. Second, this paper models the traffic network by considering the intensity of each road. The energy consumption associated with different intensity levels is also modelled using a typical driving cycle. The dual-objective routing problem, considering both energy consumption and travel time, is solved by an Iterative Dijkstra's algorithm. Finally, several simulation results demonstrate that the proposed dual-objective eco-routing strategy achieves a favorable balance between energy consumption and travel time. Specifically, for HEV, the dual-objective eco-routing strategy resulted in a 5.3% reduction in travel time with a marginal increase of only 0.4% in fuel consumption compared to the single-objective eco-route. In addition, the eco-routing strategy exhibits distinct patterns across different vehicle powertrains. HEV and BEV tend to utilize heavy traffic conditions more frequently compared to the CV. • Build the energy consumption models of hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV). • Energy consumption associated with different intensity levels is modelled. • Formulate the dual-objective routing problem, considering both energy consumption and travel time. • Eco-routing strategy exhibits distinct patterns across different vehicle powertrains and traffic conditions. [ABSTRACT FROM AUTHOR]

The erosion problem of semi-open centrifugal pump serving in silt-carrying fluids is serious, and the interaction between the particle migration and vortex evolution aggravates the complexity of erosion on the flow components. In the present paper, the liquid-solid two-phase flow of semi-open centrifugal pump under different particle sizes is simulated based on the four-way coupling Euler-Lagrangian method, in which the fluid-particle interaction and particle-particle interaction are taken into account. In particular, the influence of particle size on the structure characteristics of leakage vortex and erosion characteristics are investigated, and the interrelationship among vortex structure, particle migration and surface erosion behavior is revealed. The results show that the erosion area is mainly distributed in the blade leading edge, the pressure surface leading edge and the hub. When the particle size is 0.1 mm and 0.3 mm, due to the good following characteristic of particles and the inhibition effect on tip leakage vortex (TLV), the particles are easy to impact the trailing edge of blade suction surface and the hub, which induce severe erosion. As the particle size decreases, the number of particles flowing into the tip clearance increases. Therefore, the frequent impact of particles on the TLV leads to the breakup, separation and fusion of the vortex, which aggravates the flow instability. This paper provides a new insight into the hydraulic performance and erosion characteristics of semi-open centrifugal pump in terms of particle-vortex interaction. [Display omitted] • The evolution of leakage vortex structure with particle size is revealed. • The interaction between leakage vortex and particle trajectories is analyzed. • The blade pressure and blade load fluctuation changes greatly by adding particles. • The effect of particle-vortex interaction on erosion behavior is revealed. [ABSTRACT FROM AUTHOR]

Cell voltage inconsistency in a battery pack is an important signal released by the deterioration of battery performance. In this paper, voltage inconsistency is categorized into static inconsistency and dynamic inconsistency, and the latter contains progressive fluctuation fault and sudden fluctuation fault. For voltage dynamic inconsistency, this paper innovatively proposes a fault diagnosis and type identification method based on weighted Euclidean distance evaluation and statistical analysis. Specifically, firstly, the Euclidean distance evaluation method is used to quantify the cumulative length of the voltage curve of each individual cell in the time window, and at the same time, different forgetting factors are weighted on the voltage line segments to mitigate the fault diagnostic delay and memory effect defects of the time window. Then, a voltage abnormality evaluation coefficient is introduced to characterize the degree of inconsistent fluctuation of the cell voltage, and statistical methods are used to find a reasonable threshold. Further, fault type identification algorithm layer is conducted to identify the type of cell voltage fluctuation by using the optimized correlation coefficient method while the cell is detected by the fault diagnosis algorithm layer. Finally, the effectiveness of the proposed fault diagnosis strategy is verified by experimental data, and an online platform is utilized to obtain voltage data with different fault characteristics to test the practical application of the method. By comparing the proposed method with various data-driven methods with the same data evaluation significance, the results show that the method in this paper is more robust to static voltage inconsistency and the length of the time window, and is capable to recognize the various data patterns of potential threats, with higher accuracy and computational efficiency. • Euclidean distance evaluation is used for cell voltage inconsistency fault diagnosis. • Forgetting factor is used to mitigate fault diagnosis delay and memory effect defects of time window. • Optimized correlation coefficient is used to isolate pattern of cell voltage inconsistent fluctuations. • The accuracy is more than 99%. [ABSTRACT FROM AUTHOR]

With the integration of high proportion of distributed photovoltaic(PV), high-accuracy regional PV power forecasting technology can enhance the regional coordinated scheduling capability of the new power system. This paper proposes a regional PV power forecasting model based on an improved time-series dense encoder and graph attention network (ITDE-GAT), which takes into account the spatio-temporal correlations among the regional PV plants. Firstly, an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN) is used to extract the clear-sky and fluctuation components from PV data. Secondly, the combined ITDE-GAT is applied to perform the regional PV power forecasting. Considering the static information, an improved dense encoder network (ITDE) is constructed to extract the temporal and spatial relationships of regional PV. Graph attention network (GAT) is then utilized to explore the spatial correlations among the regional PV. Finally, case study from two actual PV region datasets shows that the proposed model achieves higher forecasting accuracy and exhibits stronger generalization capabilities. The results demonstrate that compared to various advanced deep learning methods, the R2 evaluation metric of the approach proposed in this paper demonstrates, respectively, maximum improvements of 3.4 %, 6.5 %, and 7.8 % for the 1 h, 3 h, and 6 h ahead predictions. ICEEMDAN decomposition results can be interpreted efficiently. Graph attention networks can explore the spatial characteristics between PV plants. The ITDE model based on MLP can effectively improve the accuracy of PV multi-step prediction. The ITDE-GAT model has higher accuracy and stronger generalization ability. [ABSTRACT FROM AUTHOR]

Because the carbon load inside a diesel particulate filters (DPF) affects the DPF regeneration, and the carbon load recognition is significant for the particulate matter (PM) emission control. It is necessary to investigate an on-board DPF carbon load recognition method because the carbon load cannot be directly measured by sensors. Aiming to build a DPF carbon load prediction model adopting the deep learning method, this paper proposes a DPF carbon load identification model based on different experimental parameters using a layered one dimension convolutional neural network (1D-CNN) method. To improve data validity, this paper adopts two data-processing methods. The data pre-processing adopts data splicing method to complete the construction of the original sample set, and the data after-processing uses wavelet packet transform method to establish the feature sample sets. The model adopts the optimal feature dataset constructed by three input parameters, i.e., temperature difference, pressure difference, and exhaust mass flow, and has both high training accuracy and test accuracy above 90 %. The pressure difference is the most important influencing input parameter, and the three-parameter sample set (Δ T + Δ P + Q) has great recognition accuracy and good model stability with the high training accuracy and test accuracy as well as less iteration. • A heavy-duty diesel engine was tested under various conditions of carbon load inside DPF. • The data processing was done by two methods: data splicing and wavelet packet transform. • We proposed a DPF carbon load identification model based on a one-dimension CNN method. • The model adopts 3 parameters, and the 3-parameter sample set has optimal performance. • The 1D-CNN model has both high training accuracy and test accuracy above 90 %. [ABSTRACT FROM AUTHOR]

Wind power is a critical source of renewable energy, but the variability of wind speed can impact the efficiency and reliability of wind power generation. To improve the accuracy of wind speed prediction, this paper proposes a novel system that combines deep learning and decomposition algorithms based on ensemble optimization methods. Firstly, the paper proposes an innovative hybrid modal decomposition (HMD) method that extracts accurate features from wind speed data using Singular Value Decomposition (SVD), modal number selection method, and Variational Mode Decomposition (VMD). Secondly, the variable screening method identifies relevant factors affecting wind speed as fixed inputs. Then, the stationary test method is used to identify the sequences of different features in the mode and establish different prediction models: a bidirectional long-term short-time neural network model (Bi-LSTM) model optimized by artificial hummingbird algorithm (AHA) is used for non-stationary sequences; an ARIMA model is used for stationary sequences. Data from Sotavento and Changma wind farms are used to test this forecasting system. Simulation experiments are carried out from multiple dimensions. The results show that all error indicators are significantly improved, and the proposed prediction system is better than other comparative forecasting schemes. • The research can be practically applied in the field of wind power generation. • The research can help improve the energy utilisation of wind power generation. • A new data decomposition algorithm called HMD is proposed. • A new prediction system based on ensemble optimization idea is proposed. • Combining AHA-optimized neural networks and ARIMA to predict wind speed. [ABSTRACT FROM AUTHOR]

With the appearance of digital computers and microprocessor technologies, the control systems used in nuclear reactors continue to transform from analog devices to digital ones in recent years. A heavy challenge is that applying the pre-existing intelligent load following control algorithms developed in the continuous-time domain to the current digital devices of the nuclear reactors may yield the so-called digital chattering effects, and even trigger system instability. To this end, this paper presents a discrete-time integral terminal sliding mode controller (DTITSMC) coupled with a discrete-time disturbance observer (DTDO) for the load following of a modular high-temperature gas-cooled reactor (MHTGR). The common continuous-time MHTGR model is first transformed into a discrete-time form with consideration of model uncertainties, exogenous disturbances, and discretization errors. Then, on the basis of this discrete-time MHTGR model, a DTDO that can estimate the lumped disturbances, composed of model uncertainties, exogenous disturbances, discretization errors, and unmeasured system states, is developed, subsequently being combined with the designed DTITSMC scheme in order to enhance the system robustness. The proposed DTITSMC scheme coupled with the DTDO is novel in that it can be directly implemented on the digital control system of the MHTGR, while at the same time providing an improved load following control performance, owing to the integration of the state-of-the-art sliding mode control techniques, backstepping control techniques, and the disturbance feedforward compensation supported by the disturbance observer techniques. Both the estimation error convergence of the DTDO and the overall system stability are theoretically proved by the Lyapunov stability theory in this paper. Finally, the feasibility and superiority of the proposed DTITSMC scheme coupled with the DTDO over the previous discrete-time linear sliding mode controller (DTLSMC) and discrete-time proportional–integral–derivative controller (DTPIDC) are further confirmed by simulation studies, where in the absence of lumped disturbances, the value of the accumulative absolute load error (AALE) produced by the proposed DTSMPPC scheme coupled with the DTDO is merely 0.0575% and 0.2675% to that of the previous DTLSMC and the optimized DTPIDC, respectively, while in the presence of lumped disturbances, the value of the AALE produced by the proposed DTSMPPC scheme coupled with the DTDO is merely 0.006% and 0.1952% to that of the previous DTLSMC and the optimized DTPIDC, respectively. • A discrete-time MHTGR model with lumped disturbances is constructed. • A DTDO that can estimate lumped disturbances is developed. • A DTITSMC scheme is proposed to accomplish load following operation of MHTGR. • Control scheme can be directly implemented on digital control system of MHTGR. • Simulations and comparisons reveal superiority of control scheme. [ABSTRACT FROM AUTHOR]

In order to optimize the urea injection volume of the SCR system after fuel change, this paper constructs the three-dimensional models of the marine engine and SCR system. When the engine is doped with hydrocarbon-based biodiesel, this paper determines the optimal doping ratio and injection advance angle of the engine to be 10 % and 27°CA BTDC based on the economy. Under these parameters, the simulation obtains the exhaust parameters of the engine, determines the urea injection area of the SCR system, and calculates the theoretical urea injection volume to meet the urea injection temperature operating point. After blending hydrocarbon-based biodiesel, when the SCR system continues to use the calibrated urea injection volume in pure diesel mode, the requirements cannot be met, so it is necessary to re-calibrate the SCR system urea injection volume. Subsequently, this paper proposes a NOx target conversion rate allocation method based on the average ammonia-nitrogen consumption ratio and working condition usage frequency and taking the engine unit power urea consumption as the optimization target. The results show that the optimized average ammonia specific consumption of the SCR system is 21.45g/(kW⋅h), which is reduced by 6.95 % compared with original calibrated average ammonia specific consumption (22.94 g/(kW⋅h)). • Marine Engine Blending with Hydrocarbon-Based Biodiesel to Reduce Emissions. • Demonstration of changes in engine exhaust parameters due to fuel change. • Performance study of SCR system for fuel change. • Proposing a new optimization method based on urea injection volume. [ABSTRACT FROM AUTHOR]

Gas-liquid two-phase flow exists in nuclear reactor evaporation, vehicle cooling, chemical production falling film evaporation and other processes, and the dynamic measurement of the interfacial wave is of great significance for industrial process monitoring and production optimization. Accurate identification and characterization of interfacial wave is an important prerequisite for scientific research and engineering practice. In this paper, interfacial wave measurement experiments have been carried out using an ultrasonic phased array system and measurement method to obtain gas-liquid phase interface measurement information in gas-liquid flow system. The liquid film thickness is extracted by fitting the gas-liquid interface through two-dimensional data matrix to ladder, detecting the liquid level data, and constructing a three-dimensional interfacial wave distribution model based on dynamic real-time data. The interfacial wave amplitude prediction correlation is developed using various machine learning regression models, which are optimized by parameters tuning algorithms. Laboratory results indicate that the wave amplitude prediction correlation is highly correlated with five inlet parameters, including superficial gas velocity, superficial liquid velocity, gas Reynolds number, liquid Reynolds number, and Lockhart-Martinelli (L-M) parameter. In the seven typical wave amplitude prediction correlations for stratified and plug flow, Least Squares Boosting (LSBoost) provided the best fit with MAPEs of 11.26 % and 18.42 %. The Gray Wolf Optimizer (GWO) is proposed and used to optimized the wave amplitude prediction correlation of LSBoost, and the MAPEs of optimized model are 4.38 % and 17.26 %. This paper provides a reliable method for accurate measurement and methodological analysis of interfacial wave spatial-temporal characteristic parameters of gas-liquid two-phase flow. • A three-dimensional measurement method of interfacial wave based on ultrasonic phased array is designed. • A three-dimensional interfacial wave distribution model based on dynamic real-time data is constructed. • The interfacial wave amplitude prediction correlation is established using multiple machine learning regression models. • Optimization of the wave amplitude prediction correlation by multiple parameter tuning algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of dealing with epistemic, i.e. non-probabilistic, uncertainties in strategic energy planning modelling. Current models have limited consideration of this type of uncertainty compared to probabilistic uncertainty, and also typically lead to overly conservative results. To address this issue, the contribution of the paper is to propose a novel decision support method which combines two decision-making methodologies into a single, internally consistent algorithm, and to show its applicability to real-size energy planning studies. Robust optimization is applied to address constraint uncertainties, while the minimax regret criterion is utilized for uncertainties in the objective function. This approach facilitates energy modelling exercises that can be more closely aligned with decision-makers' preferences for both feasibility and optimality. To demonstrate its effectiveness, the method is applied to a real-size strategic energy planning model, and the algorithm is shown to be able to provide detailed solutions in reasonable times. Ex-post evaluations confirm that this approach maintains robust optimization performance by effectively reducing the occurrence and magnitude of infeasibilities, while satisfying the minimax regret criterion across the entire range of uncertainties. Therefore, this integration preserves the distinct advantages of each methodology without any adverse effects when used together. • Robustness framework for decision-making in the face of epistemic uncertainty. • Decision support method combines robust optimization with min-max regret criterion. • Methodology accounts for decision-makers' feasibility and optimality preferences. • Real-size energy planning model proves applicability in other models or disciplines. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool. • A system is proposed to control ship energy efficiency level by combining an optimization model and a neural network • A simulation model is created in the Ricardo Wave tool and the engine model is validated by actual data. • The ship fuel consumption is estimated by an artificial neural network based on the derived input parameters. • The prediction process is evaluated by error metrics to determine its success. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between financial development and access to electricity for rural and urban India by incorporating economic growth, trade, and foreign direct investment in energy production function. In doing so, we have applied autoregressive distributed lag bounds testing to examine long run cointegration between the variables by accommodating structural break(s) stemming in the series. Our empirical results indicate the presence of cointegration association between access to electricity and its determinants. We note that financial development enhances access to electricity in rural India but declines it in urban India. Economic growth has positive effect on access to electricity. Trade openness and foreign direct investment lower access to electricity in rural India. In urban India, trade openness and foreign direct investment increase access to electricity. The association between financial development and access to electricity is inverted-U shaped for rural and urban India. This paper would present new policy direction for Indian government to enhance access to electricity by using financial development as an economic tool. • Financial development and access to electricity have a strong linkage. • In India financial development enhances access to electricity in rural area but has adverse effect in urban area. • Rise in economic growth of India has increased the access to electricity. • Growth of Trade Openness and Foreign Direct investment increase access to electricity. [ABSTRACT FROM AUTHOR]

As installed wind power capacity continues to rise, the cost of onshore wind power generation in China has fallen, far exceeding the world average. The purpose of this study is to explore the main factors affecting onshore wind power in China and to identify ways to reduce costs. So as to reduce the cost of wind power and promote the large-scale grid-connected use of wind power. Therefore, this paper summarizes various factors that affect the development of onshore wind power. We also provide a comprehensive and repeatable MFLC (multi-factor learning curve) method to evaluate the factors affecting the cost of onshore wind power. Subsequently, the learning rates of various factors are calculated using this method. The results show that empirical learning by increasing installed capacity/generation, improving technology and selecting sites which are rich in natural resources, and appropriately reducing capital investment and material prices can significantly reduce the LCOE (levelized cost of electricity) of onshore wind power in China. Compared with wind power giants of the United States and Germany, the reduction in the cost of onshore wind power generation in China is more dependent on inputs such as capital investment and raw materials, while experience plays a relatively minor role. The ability to make full use of natural resources and to convert wind energy into electricity more efficiently is crucial to reducing the cost of wind power. • This paper summarizes various factors that affect the development of onshore wind power. • A comprehensive and repeatable MFLC method is built in this paper. • The learning rates of various factors are calculated. [ABSTRACT FROM AUTHOR]

Piezoelectric energy harvesters (PEH) are widely used to collect piezoelectric energy. However, adapting to different engineering application environments is difficult for traditional PEHs. To improve the engineering application value of PEH, a hanger piezoelectric energy harvester (HPEH) based on low frequency vibration of hanger is proposed in this paper. A dynamic electromechanical coupling model was established for hanger structure, and the effects of the external load resistance, the polyvinylidene fluoride (PVDF) piezoelectric film installation location, the stability of the PVDF piezoelectric film with different winding methods, and the external excitation amplitude on the energy output performance of the HPEH were studied. Results indicate that the optimal load resistance of the HPEH studied in this paper is 1.0 MΩ; the PVDF piezoelectric film location has a significant impact on the performance of the HPEH, and the output power is highest at hanger L c/4; the PVDF piezoelectric films during ring winding is better than those in spiral winding for the same external cycle excitation; in the external excitation amplitude range of 2 d –6 d (d : hanger diameter), appropriately increasing the external excitation amplitude is conducive to improving the output voltage of the HPEH, with the root mean square voltage increasing by 95.11 % at external excitation amplitude A = 6 d compared with that at the initial excitation amplitude. It provides an innovative solution for the field of cable structures and new energy sources, and the further research and application of this technology is expected to have a profound impact in the field of energy sustainability and engineering technology. • This paper proposes a novel hanger piezoelectric energy harvester. • The key parameters affecting the energy harvesting performance are analyzed. • The engineering application value is increased compare with conventional harvesters. • It is suitable for low-frequency vibrations of cable structures. • It provides a new idea for energy harvesting of cable structures. [ABSTRACT FROM AUTHOR]

The smooth operation of the power system imposes high requirements on wind power forecasting. Given the forecasting challenges brought by the coexistence of strong and weak disturbances in wind power data, this paper proposes a hierarchical anti-disturbance mechanism to raise the accuracy of single-point wind power prediction. In the first-level disturbance processing, we prioritize the treatment of strong disturbances in wind power data through a dual-branch multi-source fusion prediction structure. The wind power subsequences with distinct time–frequency characteristics are divided into two sets, processed by the stationary set prediction branch and the drastic set prediction branch, respectively. For the drastic set, we incorporate microenvironmental multi-source data surrounding the wind turbine as input and propose a parallel-structured attention fusion module CA (Convolution attention fusion block) to provide effective data support for the prediction module, then extract rich fusion features through deep temporal LSTM algorithm and ultimately achieve fine-grained forecasting. For weak disturbances that have not been adequately addressed in the previous step, we conduct a secondary treatment through error correction strategy. By employing the hierarchical processing approach, this paper accomplishes a comprehensive handling of disturbances with different intensities. Experimental results on four data sets show that the proposed model DEWFM (Dual-branch and error correction wind power forecast) demonstrates remarkable performance advantages in comparing with the baseline model and other advanced models, the average values of MAE reaching 1.679, revealing the effectiveness of the proposed method in enhancing the precision of short-term wind power prediction. • Present a method to address the uncertainty in short-term wind power prediction. • A time–frequency hybrid prediction, differentially processing subsequences. • Enhanced LSTM with a multi-source data fusion mechanism for fine-grained prediction. • Error correction handles residual weak disturbances, enhancing prediction accuracy. • The performance evaluation shows the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Real-time acquisition of dynamic wake field information has garnered substantial attention in the wind farm industry, as it provides a crucial data source for intelligent wind farm monitoring and control. However, the existing wind measurement technologies, such as light detection and ranging (LiDAR), are limited to sparse data point measurements. This paper explores the utilization of a Physics-Informed Neural Network (PINN) to reconstruct wind turbine wake dynamics, specifically focusing on the influence of active wind turbine yaw operation on wake evolution. The methodology involves creating a tailored loss function that combines sparse wake measurement data with the Navier-Stokes (NS) equations. More precisely, the neural network incorporates the NS equations as constraints to guide the prediction of physical quantities in the output, including downwind velocity, crosswind velocity, and pressure. Taking the dynamic wake during yawing as a case study, the proposed method showcases remarkable universality and robustness across diverse scenarios involving varying scanning angle intervals, measurement point spacings, frequencies, and noise levels. It successfully captures the dynamic trends in wake evolution during yawing and accurately forecasts the wake trajectory and deflection. Even when tested with actual wake measurement data, the method can still effectively reconstruct the flow field, indicating significant potential for the real wind farm yaw control. • Dynamic spatiotemporal wake reconstruction of a utility-scale yawed wind turbine is carried out in this paper. • A novel approach combining physics-informed neural network with sparse LiDAR measurement is proposed. • The neural network maps spatiotemporal coordinates [ t , x , y ] to flow physical quantities [ u , v , p ] integrated with NS equations. • Various cases with different LiDAR measurement parameters are investigated for sensitivity and robustness analysis. • The new approach shows great effectiveness in reconstructing dynamic wake behavior of trajectory and deflection changes. [ABSTRACT FROM AUTHOR]

China still faces relatively serious energy poverty, and technological innovation is considered effective at alleviating this issue. This paper empirically investigates the impact of regional technological innovation on energy poverty based on panel data from 30 provinces in China from 2001 to 2020. This paper explores the effect of industrial structure distortion in the process of technological innovation on energy poverty. This paper conducts asymmetry analysis and investigates the heterogeneous impact of technological innovation on energy poverty in different areas. The research conclusions are as follows: (1) Regional technological innovation can effectively alleviate energy poverty. (2) Industrial structure distortion moderates the impact of technological innovation on energy poverty, and it weakens the effect of technological innovation in alleviating energy poverty, although the moderating effect is not significant in coal-producing regions. (3) The effect of technological innovation on alleviating energy poverty is more obvious in regions where energy poverty is prominent. (4) The role of technological innovation in alleviating energy poverty shows spatial heterogeneity, and this alleviation effect is more obvious in the western and non-coal producing regions. We also propose several policy implications for alleviating energy poverty. • Regional technological innovation can effectively alleviate energy poverty. • Industrial structure distortion moderates the impact of technological innovation on energy poverty. • Industrial structure distortion hinders the ability of technological innovation to alleviate energy poverty. • Technological innovation is more effective in alleviating energy poverty in regions where energy poverty is severe. • Technological innovation is more effective at alleviating energy poverty in the west and non-coal-producing regions. [ABSTRACT FROM AUTHOR]

It is well known that energy forecasts play an important role in guiding energy policy, economic development and technological progress. Therefore, based on the purpose of energy consumption and production forecasting, this paper proposes a novel structure adaptive discrete grey Bernoulli model, which is innovative in terms of both accumulated generating operator and model structure. In terms of accumulated generating operator, a new fractional order accumulated generating operator is proposed in this paper. The new accumulated generating operator has a different information priority by adjusting the values of the parameters. In terms of model structure, a novel discrete grey Bernoulli model is proposed in this paper. The novel model is well adapted to time series data containing nonlinear information, and can well mine and utilize the information contained in the original data. In addition, the Particle Swarm Optimization (PSO) algorithm was chosen to optimize the model parameters based on algorithm comparison experiments. This enables the model to flexibly adapt to a variety of complex data and has the ability of structure adaptive. Moreover, this paper conducts comparative experiments between the novel model and eight other forecasting algorithms for time series data. The numerical results show that the novel model has better forecasting performance for the data of China's total energy consumption, China's total electricity generation and China's total domestic electricity consumption. In addition, for the model reliability problem caused by the optimization algorithm, the stability and accuracy of the model are verified by Monte Carlo simulation and probability density visualization analysis. Finally, the proposed model predicts the future development trend of energy consumption and production in China. • A novel structural adaptive discrete grey Bernoulli model is established. • The proposed model can grasp nonlinear and fluctuant in various datasets. • The robustness analysis of the novel model is established. • Comparative study shows that the novel model is superior to other models. • Relevant suggestions are put forward according to the prediction results. [ABSTRACT FROM AUTHOR]