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Owing to a long-term, extensive development model and inadequate industrial development planning, cases of atmospheric environment pollution frequently occur in Hebei province. By using such approaches as the Spatial Gini Coefficient, the Herfindahl–Hirschman Index and location entropy, this paper analyzes the spatial-distribution characteristics of atmospheric pollution-intensive industries such as the thermal power industry in Hebei province. As shown, atmospheric pollution-intensive industries, such as the thermal power industry in Hebei province, excessively cluster. As industrial agglomeration continuously intensifies, the spatial imbalance becomes increasingly prominent. Taking the number of days with excellent air quality as a benchmark, this paper divides prefecture-level cities of Hebei province into four types of industrial management and designs targeted strategies for the optimization of atmospheric pollution-intensive industries, such as the thermal power industry. In terms of policies, Type I and Type II cities are advised to strengthen the transfer of atmospheric pollution-intensive industries such as the thermal power industry, and Type III and Type IV cities are advised to improve capacities in atmospheric self-purification and green-technology innovation in a bid to help government departments to scientifically manage atmospheric pollution-intensive industries such as the thermal power industry. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation prediction constitutes a significant research area within the realm of power system artificial intelligence. Accurate prediction of future photovoltaic output is imperative for the optimal dispatchment and secure operation of the power grid. This study introduces a photovoltaic prediction model, termed ICEEMDAN-Bagging-XGBoost, aimed at enhancing the accuracy of photovoltaic power generation predictions. In this paper, the original photovoltaic power data initially undergo decomposition utilizing the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm, with each intrinsic mode function (IMF) derived from this decomposition subsequently reconstructed into high-frequency, medium-frequency, and low-frequency components. Targeting the high-frequency and medium-frequency components of photovoltaic power, a limiting gradient boosting tree (XGBoost) is employed as the foundational learner in the Bagging parallel ensemble learning method, with the incorporation of a sparrow search algorithm (SSA) to refine the hyperparameters of XGBoost, thereby facilitating more nuanced tracking of the changes in the photovoltaic power's high-frequency and medium-frequency components. Regarding the low-frequency components, XGBoost-Linear is utilized to enable rapid and precise prediction. In contrast with the conventional superposition reconstruction approach, this study employs XGBoost for the reconstruction of the prediction output's high-frequency, intermediate-frequency, and low-frequency components. Ultimately, the efficacy of the proposed methodology is substantiated by the empirical operation data from a photovoltaic power station in Hebei Province, China. Relative to integrated and traditional single models, this paper's model exhibits a markedly enhanced prediction accuracy, thereby offering greater applicational value in scenarios involving short-term photovoltaic power prediction. [ABSTRACT FROM AUTHOR]

Lightning has a certain degree of potential threat to the safe operation of overhead transmission lines. In order to make targeted lightning protection arrangements and reduce the impact of lightning on overhead transmission lines, it is necessary to conduct lightning risk assessments on overhead transmission lines. This paper proposes a lightning failure risk assessment method for overhead transmission lines based on a modified Dempster–Shafer theory. First, analyze the historical lightning failure data of the line, determine the lightning failure impact factors, and use confidence to express the relationship between the lightning failure and the impact factor; then, use entropy weight theory and gray relational theory to calculate the value of mass function, and modify it on this basis; finally, use Dempster–Shafer theory to determine the trust degree and fit this with the calculated lightning trip rate to produce the risk assessment. This paper analyzes the lightning failure data of overhead transmission lines in some areas of Hebei Province. The results show that, compared with the evaluation method of the Dempster–Shafer theory, the accuracy of the evaluation is improved to a certain extent after correcting the mass function value. It can be seen that this method can integrate and comprehensively consider different data and can provide a reference for preventing damage to transmission lines by lightning strike weather. [ABSTRACT FROM AUTHOR]

With the accelerating urbanization process in China, the extensive economic growth model with high energy consumption and high pollution aggravates atmospheric environmental pollution and restricts the sustainable development of the social economy. The Beijing–Tianjin–Hebei region has the most severe haze pollution and the most acute contradiction between resources, environment and economic development. Based on the physical coupling theory, this paper constructs a coupling coordination degree model of the economy, environment and high haze pollution industries, such as the thermal power industry. Through literature analysis and expert investigation of the economic, industrial and environmental data of the Beijing–Tianjin–Hebei region from 2006 to 2018, the coordinated development of the economy, environment and high haze pollution industries, such as the thermal power industry, in the above region, was dynamically monitored. The monitoring results show that the development level of the Beijing, Tianjin and Hebei region is significantly different, and the three subregions have not entered the stage of coordinated development. Moreover, since 2015, they have been in a bottleneck period of development and have not achieved a higher level of progress. The three subregions have different advantages and restraining factors. For example, the backward level of Hebei's economy and high haze pollution industries, such as the thermal power industry, restrict the coordinated development of the region as a whole. Only by accelerating the high-quality development of the Hebei economy can we break through the bottleneck of the development of Beijing, Tianjin and Hebei. [ABSTRACT FROM AUTHOR]

As the top emitter of carbon dioxide worldwide, China faces a considerable challenge in reducing carbon emissions to combat global warming. Carbon emissions from coal consumption is the primary source of carbon dioxide emissions in China. The decomposition of the driving factors and the quantification of regions and industries needs further research. Thus, this paper decomposed five driving factors affecting carbon emissions from coal consumption in China, namely, carbon emission intensity, energy structure, energy intensity, economic output, and population scale, by constructing a Kaya-Logarithmic Mean Divisia Index (Kaya-LMDI) decomposition model with data on coal consumption in China from 1997 to 2019. It was revealed that the economic output and energy intensity effects are major drivers and inhibitors of carbon emissions from coal consumption in China, respectively. The contribution and impact of these driving factors on carbon emissions from coal consumption were analyzed for different regions and industrial sectors. The results showed that carbon emissions from coal consumption increased by 3211.92 million tons from 1997 to 2019. From a regional perspective, Hebei Province has the most significant impact on carbon emissions from coal consumption due to the effect of economic output. Additionally, the industrial sector had the most pronounced influence on carbon emissions from coal consumption due to the economic output effect. Finally, a series of measures to reduce carbon emissions including controlling the total coal consumption, improving the utilization rate of clean energy, and optimizing the energy structure is proposed based on China's actual development. [ABSTRACT FROM AUTHOR]

The Beijing-Tianjin-Hebei region is subject to the most severe haze condition in China. Against the backdrop of the coordinated development of Beijing, Tianjin, and Hebei, it is of great significance to explore the space-time distribution characteristics of high haze pollution industries in the above region. The purpose of this article is to find high haze pollution industries scientifically, analyze the spatial distribution characteristics of high haze pollution industries in Beijing-Tianjin-Hebei correctly, and formulate effective reduction measures for the spatial distribution of high haze pollution industries in different regions of Beijing-Tianjin-Hebei. It hopes that the measures provide a basis for effective to reduce high haze pollution in the Beijing-Tianjin-Hebei region. Using the Gini coefficient and location entropy, this paper explores the space-time characteristics of high haze pollution industries in 13 cities of the Beijing-Tianjin-Hebei region. The results show that those aforesaid high haze pollution industries present an obvious clustering trend in Beijing, Tianjin, Shijiazhuang, Handan, and some areas along the economic axis of China, overall flooding into Eastern coastal and Southern inland areas. By industry, the petroleum processing and coking processing industry, chemical raw materials and chemical products industry, manufacture of non-metallic mineral products, and ferrous metal smelting and rolling processing industry witness a declining clustering trend, as opposed to the electricity, heat production, and supply industry. Meanwhile, core cities of the Beijing-Tianjin-Hebei region have developed high haze pollution industries, and the hinterland lags in economic development. It is necessary to face up to the differences in economic development among areas in the Beijing-Tianjin-Hebei region, promote the staggered development of high haze pollution industries, and build a high haze pollution industry chain with coordinated economic and environmental development, thus reducing the economic gap with each area, and realizing the coordinated development of economy, industry, and environment. [ABSTRACT FROM AUTHOR]

Urban metabolism has emerged over the past decades as an important new paradigm of regional and urban sustainability governance towards a Chinese national scheme of 'carbon neutrality' by 2060. Hebei province in China faces twin pressures related to its supply of water and energy resources, which has brought humans and nature into conflict. Overcoming this tension in the human-land relationship in Hebei and determining a suitable development path for the future has become a core issue for the achievement of coordinated development within the Beijing–Tianjin–Hebei region. This paper constructs a system to simulate the metabolism of water, energy, and human relationships, and uses this model to carry out simulations for Hebei province. The model establishes five scenarios: a natural development scenario, economic growth scenario, water conservation development scenario, energy conservation development scenario, and low carbon scenario. The simulation results show that, without intervention, the natural development scenario results in greater pressure on supply gaps and a greater demand for water and energy, with more production of industrial waste gas and domestic wastewater discharges. The economic growth, water conservation development, and energy conservation development scenarios focus on single economic, water conservation, and energy conservation measures by looking at core economic, water, and energy elements within the metabolic system; however, solving issues with individual elements merely leads to other, remaining problems. Under the low carbon scenario, issues with multiple elements in Hebei's metabolic system are considered more comprehensively, so the simulation results are better than those in the other scenarios, and it better fits the future orientation of sustainable development of Hebei province. [ABSTRACT FROM AUTHOR]

Just as the double-pipe heat exchanger is being utilized in an increasing number of applications, its research content is also deepening. For this paper, based on the air-conditioning cold and heat source project of a building in Handan, Hebei Province, a 300-meter medium-shallow well double-pipe heat exchanger was used for heating and cooling, and a corresponding heat transfer model was established. The changes of parameters such as the inlet and outlet temperature, heat exchange (with and without a temperature gradient), and borehole wall temperature distribution between a single borehole, double boreholes, and four boreholes over one year in medium-shallow wells were simulated and analyzed. By comparing the obtained experimental data and the simulation data, the accuracy of the heat transfer model was verified. This provides a theoretical basis for the further advancement of the project and lays the foundation for an in-depth study of multi-borehole double-pipe heat exchangers. [ABSTRACT FROM AUTHOR]

Against the backdrop of increasingly serious global climate change and the development of the low-carbon economy, the coordination between energy consumption carbon emissions (ECCE) and regional population, resources, environment, economy and society has become an important subject. In this paper, the research focuses on the security early warning of ECCE in Hebei Province, China. First, an assessment index system of the security early warning of ECCE is constructed based on the pressure-state-response (P-S-R) model. Then, the variance method and linearity weighted method are used to calculate the security early warning index of ECCE. From the two dimensions of time series and spatial pattern, the security early warning conditions of ECCE are analyzed in depth. Finally, with the assessment analysis of the data from 2000 to 2014, the prediction of the security early warning of carbon emissions from 2015 to 2020 is given, using a back propagation neural network based on a kidney-inspired algorithm (KA-BPNN) model. The results indicate that: (1) from 2000 to 2014, the security comprehensive index of ECCE demonstrates a fluctuating upward trend in general and the trend of the alarm level is "Severe warning"-"Moderate warning"-"Slight warning"; (2) there is a big spatial difference in the security of ECCE, with relatively high-security alarm level in the north while it is relatively low in the other areas; (3) the security index shows the trend of continuing improvement from 2015 to 2020, however the security level will remain in the state of "Semi-secure" for a long time and the corresponding alarm is still in the state of "Slight warning", reflecting that the situation is still not optimistic. [ABSTRACT FROM AUTHOR]

A novel multi-objective robust optimization model of an integrated energy system with hydrogen storage (HIES) considering source–load uncertainty is proposed to promote the low-carbon economy operation of the integrated energy system of a park. Firstly, the lowest total system cost and carbon emissions are selected as the multi-objective optimization functions. The Pareto front solution set of the objective function is applied by compromise planning, and the optimal solution among them is obtained by the maximum–minimum fuzzy method. Furthermore, the robust optimization (RO) approach is introduced to cope with the source–load uncertainty effectively. Finally, it is demonstrated that the illustrated HIES can significantly reduce the total system cost, carbon emissions, and abandoned wind and solar power. Meanwhile, the effectiveness of the proposed model and solution method is verified by analyzing the influence of multi-objective solutions and a robust coefficient on the Chongli Demonstration Project in Hebei Province. [ABSTRACT FROM AUTHOR]

To study the influence of gas pressure on coal permeability evolution, we conducted experiments on coal samples from the No. 9 coal seam in Tangshan Coal Mine, Hebei Province, China. Different gas pressures (helium and nitrogen) were applied, and nitrogen-induced deformations were measured. We also analyzed the coal samples' pore structure using mercury injection porosimetry, obtaining pore surface fractal dimensions. The increase in nitrogen pressure from 0.3 MPa to 3 MPa resulted in an elevation of adsorption strain from 0.168 × 10−3 to 1.076 × 10−3, with a gradual decrease observed in the extent of this increase. However, the permeability of coal samples initially decreased from 16.05 × 10−18 m2 to 4.91 × 10−18 m2 and subsequently rose to 5.69 × 10−18 m2. Helium showed similar trends to nitrogen, with average permeability 1.42–1.88 times higher under the same pressure. The lowest permeability occurred at 1.5 MPa for helium and 2.5 MPa for nitrogen. Gas absorptivity plays a crucial role in coal permeability evolution. Additionally, we observed coal's compressibility to be 7.2 × 10−11 m2/N and corrected porosity to be 53.8%, considering matrix compression. Seepage pores larger than 100 nm accounted for 80.4% of the total pore volume, facilitating gas seepage. Surface fractal dimension Ds1 correlated positively with micropore volume, while Ds2 and Ds3 correlated negatively with pore volume and gas permeability. [ABSTRACT FROM AUTHOR]

An integrated energy system (IES) involving a large number of decision-makers causes problems of bad coordination between energy sub-networks and the IES and it is not able to fully consider the multi-energy complementarity among multiple decision-makers. In this context, firstly, this paper constructs an energy optimal dispatching model of an IES based on uncertain bilevel programming. The upper model takes the transformation matrix of energy hubs as the upper decision-maker, taking the minimum operation cost of the IES in the form of confidence as the objective function; the lower model takes each optimal operation plan of the electric power sub-network, the thermal energy sub-network, and the gas energy sub-network as the lower decision-makers, aiming at the operation economy of each sub-network and considering their operation as necessary constraints. Secondly, a firefly algorithm with chaotic search and an improved light intensity coefficient is designed to improve the proposed model. An empirical analysis was conducted on a pilot area of an integrated energy system in Hebei Province. The results show the following: (1) The typical daily operating cost of the integrated energy system in winter is lower than that in summer; (2) under the same load level, the typical winter and summer running costs of the integrated energy system are lower than that of the traditional microgrid; (3) compared with the particle swarm optimization algorithm, the improved firefly algorithm proposed in the paper has obvious advantages both in terms of running cost and solution time; and (4) when the confidence of the objective function and the constraints increases, the operating cost of various schemes also increase. The above results validate the effectiveness of the energy optimal dispatching model of the IES and the economy of the system operation under the multiple decision-maker hierarchy. [ABSTRACT FROM AUTHOR]

Biomass, as a renewable and clean energy resource, plays a vital role in energy security and greenhouse gas reduction across the world. This paper reports on our newly established technology: a downdraft fixed-bed biomass gasification system using nut shells (mainly apricot kernel shells) for electricity generation, heating and partially activated carbon production at the same time. Particularly, the key features of the gasification reactor will be presented in detail. In the commercial plant (3 MW scale) located in Hebei province, China, the typical energy conversion from apricot kernel shell gasification is as follows: 47% syngas, 44% char (partially activated carbon), 5% hot water, and 4% energy loss. The main gasification temperature is 600–800 °C, while the activation zone is 850–900 °C. The commercial system has currently been in operation for 4 years. Considering the partially activated carbon as a stable carbon carrier, the whole system features negative CO2 emissions. [ABSTRACT FROM AUTHOR]

Water inrush disasters from the coal seam floor occur frequently due to the high water pressure of the Ordovician limestone aquifer, multiple aquifers and strong mining disturbance. We presented a model of water-resisting key strata (WRKS) to investigate the mechanism of floor water inrush from multiple aquifers in deep coal mines. Roof hydraulic fracturing (RHF) for controlling floor water inrush and multi-parameter monitoring were proposed and validated in the Xingdong coal mine in Xingtai, Hebei Province. The results indicated that the periodic weighting step of the test working face after RHF was 9.53 m, which was 61.42% less than that of the working face without RHF (24.7 m). The floor failure depth was 30 m, which was 34.4% less than that of the zones without RHF (45.7 m). Hydraulic fracturing weakened the strength of the overlying strata to control the weighting step and reduce the mining disturbance stress, and the stability of the floor WRKS was enhanced, thereby preventing water inrush from the coal seam floor. The research results provide a solution for preventing floor damage and floor water inrush under strong mining disturbance and in complex hydrogeological environments in deep mining. [ABSTRACT FROM AUTHOR]