The research work proposes a novel high-gain boost converter (HGBC) with a Grasshopper Optimization Algorithm (GHO) Maximum Power Point Tracking (MPPT) for a solar photovoltaic (SPV)system. Traditionally used boost converters have low voltage gain, high switching stress, and poor dynamic response under partial shading and fast varying irradiance conditions. Further, traditional MPPT methods exhibit slow convergence behavior and are unable to achieve accurate global MPPT under nonlinear operating conditions. The HGBC utilizes a multi-inductor and multicapacitor topology, which establishes high voltage conversion gain in addition to low switching stress with enhanced output stability. The GHO algorithm dynamically varies the duty cycle to facilitate rapid and precise global MPPT. The proposed system is modeled and analyzed using MATLAB simulation under steady-state and dynamic irradiance (500-1000 W/m2). Results exhibit that the proposed HGBC-GHO achieves a peak efficiency of 93.6%, which is greater than that of Whale Optimization Algorithm (92.4%), Adaptive Neuro Fuzzy Inference Systems (91.4%), Marine Predator Algorithm (MPA) (91.2%), Fuzzy Logic Control (90.4%), and Artificial Neural Network (ANN) (89.8%). It shows a fast convergence time of 0.17 s, low output ripple at 1.2%, and reduced computational effort with only 65 iterations, compared with MPA's 150 and ANN's 125 iterations. At dynamic irradiance (1000, 700, and 500 W/m2), the output power levels of the system are stable at 204.5, 108.1, and 56.02 W, respectively, with a settling time of less than 36 ms. These results demonstrate improved tracking accuracy, reduced oscillations, and enhanced dynamic responses, thus confirming the proposed HGBC-GHO's performance potential for SPV applications.
Direct steam generation (DSG) in linear parabolic collectors represents an effective pathway to improve solar-thermal energy utilization by eliminating the constraints of conventional heat-transfer fluids and enabling higher operating temperatures. This study develops a comprehensive energy and exergy modeling framework for a DSG solar collector receiver, integrating optical–thermal characteristics with detailed representations of radiative, convective, and conductive heat-loss mechanisms. The receiver performance is investigated under varying solar irradiance levels and mass flow rates, and the axial thermal behavior is resolved into three distinct regions corresponding to single-phase heating up to saturation, two-phase evaporation, and superheated steam heating. The numerical approach is validated through a grid-independence study, demonstrating solution stability beyond 1000 elements, and through consistency checks with published correlations for temperature evolution and two-phase pressure-drop behavior. The results indicate that, at a solar irradiance of 1000 W m − 2 and an optical quality of 0.8, the receiver achieves a thermal efficiency of 92.17%. Exergy analysis reveals an overall receiver exergy efficiency in the range of 35–38% at 900 W m − 2 , assuming a reference temperature of 298 K, underscoring the impact of irreversibilities associated with finite-temperature heat transfer and environmental losses. The distribution of exergy destruction shows that radiative and ambient losses account for 44.9%, followed by conduction (31.8%) and convection (23.3%). These findings provide practical guidance for receiver design improvements and operational strategies aimed at more effective solar energy exploitation.
Rural electrification in livestock-dominated regions of Sub-Saharan Africa remains constrained by high grid extension costs and dependence on diesel-based generation. This study investigates the technical, economic, and environmental feasibility of converting cattle dung into electricity through a decentralized biomass waste-to-energy (WtE) system in Madu, Uganda. Livestock waste availability, biogas potential, digester performance, energy output, and system economics were evaluated using field data and established conversion models. Results indicate that dung from 120 cattle can sustain continuous biogas production, yielding approximately 99,000 m3 of methane annually. The biogas-powered combined heat and power system generated 18-19 MWh of electricity per month, alongside significant thermal energy recovery. The system demonstrated stable operation with a hydraulic retention time of 35 days and no observed process instability. Economic analysis revealed a levelized cost of electricity of 0.064 USD kWh-1 and a simple payback period of six years, substantially outperforming diesel-based rural electricity generation. Environmental assessment showed avoided emissions of approximately 183 tCO2 yr-1, in addition to improved waste management and nutrient recycling benefits. The findings confirm that cattle-dung-based WtE systems offer a technically reliable, economically competitive, and environmentally sustainable solution for rural electrification. Integrating such systems into rural energy planning and climate mitigation frameworks could significantly enhance energy access and support low-carbon development in livestock-rich rural communities.
The rapid rise in building construction creates the energy demand for nearly half of the world'senergy demand. To minimize energy consumption in buildings, a concept called net-zero energybuilding (NZEB) is gaining popularity in developing countries and is being implemented in Indiaas well. The NZEB aims to match the on-site renewable energy generation available at the buildinglocation with the building energy consumption (BEC) without relying on grid energy. To attain thisconcept in a real-time scenario, it requires information about the energy generation at the buildingand the energy consumption at each instant. It is necessary to predict the dynamically varyingbuilding loads to easily manage the available sources without the involvement of the grid. Thiscan be achieved by designing an accurate prediction model. This article presents a comparativeassessment of recent metaheuristic algorithms for hyperparameter optimization of a support vector regression (SVR) model to enhance the prediction performance of BEC. The analysis wasconducted using hourly campus-scale energy consumption data collected from the National Institute ofTechnology Silchar, Assam, India, from 1 March 2018 to 29 February 2020, comprising 17,544 samples.The Polar Fox Optimization algorithm, Flood Algorithm, and Hiking Optimization Algorithm (HOA)were comparatively evaluated for SVR hyperparameter tuning in this application. The mean absoluteerror (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE),R-2,percent-age BIAS (PBIAS), and Willmott's Index (WI) error metrics are usedto evaluate the performance ofthe optimized SVR models. The recently developedHOA algorithm exhibits better prediction accur-acy with an MAE of 8.3099 kWh, RMSE of 11.1283 kWh,R2of 0.9986, MAPE of 2.7820%, PBIAS of-0.0759%, and WI of 0.9996 when compared to other models. The comparative results for differentmodels show that the recent metaheuristic optimization methods can improve the performance ofSVR model for accurate BEC prediction in NZEB applications.
Based on the coupling coordination theory, this article empirically explores the coordinated development between industrial digitalization and energy conservation and carbon reduction in Zhejiang Province. Against the backdrop of digital economy and dual carbon goals, this article explores the interaction mechanism and development level between industrial digitalization and energy conservation and carbon reduction, so as to provide a reference for regional high-quality green development. This paper establishes a comprehensive evaluation index system composed of two subsystems, namely industrial digitalization development and energy conservation and carbon reduction. An improved coupling coordination degree model is used to measure panel data of 11 prefecture-level cities in Zhejiang Province from 2016 to 2023. The results show that the coupling coordination degree between industrial digitalization and energy conservation and carbon reduction in Zhejiang Province presents an overall fluctuating upward trend, gradually transitioning from the primary coordination stage to the intermediate coordination stage. However, there are obvious regional and industrial heterogeneities in the coupling coordination level across Zhejiang Province. For instance, leading regions such as Huzhou have achieved high-level coupling coordination through innovative measures including carbon efficiency codes. In contrast, traditional industrial agglomeration areas face greater transformation pressure and exhibit relatively low coordination levels. Empirical analysis reveals three key paths of energy conservation and carbon reduction coordinated with digitalization. These are technology empowerment, structural optimization, and management innovation path.
As oil and gas exploration expands into ultra-deep wells and extreme temperature environments, the metal seal of the casing head suspension must maintain reliable sealing under ultrahigh pressures (UHPs) of 175 MPa coupled with extreme temperatures. To investigate the performance of metal seals in casing head hanger under extreme temperatures (−47 °C and 125 °C) and UHP (175 MPa), this study systematically conducted simulation analysis and experimental validation. The thermo-mechanical sequentially coupled simulation results indicate that during heating to 125 °C and cooling to −47 °C, the stress distribution and effective contact width of the sealing ring exhibit low sensitivity to temperature variations. Under high-temperature conditions, the variance of contact stress decreases by an average of 5.3%. Furthermore, the influence of the sealing ring's installation preload on metal seal performance under the combined effects of 175 MPa UHP and extreme temperatures was investigated. When the preload is below 2.45 mm, increasing the preload significantly enhances the effective contact width and improves the uniformity of contact stress distribution. This suggests that a preload close to 2.45 mm ensures sealing reliability and stability under UHP and extreme temperatures. Finally, a 175 MPa high/low-temperature sealing test was performed on an 8 1/8″ full-metal-seal mandrel-type casing hanger. Under 125 °C/175 MPa conditions, the pressure drop was 0.6 MPa after 15 min of pressure holding, while under −47 °C/175 MPa conditions, the pressure drop was 1.16 MPa, confirming the robust reliability of the metal seal. This study provides an important reference for the sealing structure design of ultra-deep and UHP wellhead equipment. Future research may further expand the temperature range and hold duration to more comprehensively evaluate long-term service reliability.
The Lower Paleozoic Qiongzhusi Formation is an important next target for shale gas exploration and development in South China, following the Wufeng-Longmaxi Formation. The total organic carbon (TOC) content is a key indicator of shale gas enrichment; however, systematic comparative studies of the factors controlling the organic matter enrichment of the Qiongzhusi Formation shales across the western Hunan-Hubei and northern Guizhou region are lacking. In this study, we compared the sedimentological and geochemical characteristics of the Qiongzhusi Formation shale deposited at the passive continental margin versus within the cratonic depression in the western Hunan-Hubei and northern Guizhou region and explored the main controlling factors and formation models of the differential organic matter enrichment. The results show that the shale intervals with high TOC values are located at the base of the Qiongzhusi Formation and were formed in environments characterized by low terrigenous input, high paleoproductivity, and favorable preservation conditions. Subsequently, the basin rifting gradually weakened, and the sea level continuously fell, which manifested as increased terrigenous input and deteriorating preservation conditions. In addition, the ocean current activity weakened as the sea level fell, resulting in a corresponding decline in the paleoproductivity. Therefore, the vertical decrease in the TOC content of the Qiongzhusi Formation was jointly controlled by the reduced paleoproductivity, poorer preservation conditions, and increased terrigenous input. Laterally, from the passive continental margin toward the interior of the cratonic depression, both the sea level and the intensity of the oceanic circulation exhibited decreasing trends, accompanied by simultaneous deterioration of the preservation conditions and paleoproductivity, resulting in progressively lower TOC contents.
This research examines the difficulties in estimating the price elasticity of energy demand during periods of extreme market volatility, with a focus on the Czech Republic as a small open economy. Utilising a detailed dataset of monthly electricity and natural gas consumption from January 2021 to December 2024, we evaluate the consistency of regression models over different time periods and the impact of auxiliary variables. Our results indicate that models based on three- and four-year datasets are notably more dependable than those using only two-year data. Monthly data analysis reveals low absolute short-term price elasticities for electricity (0.062-0.167) and natural gas (0.091-0.205), indicating that energy demand remains highly inelastic even amid sharp price increases. This suggests that price signals alone are not sufficient to drive significant short-term reductions in energy use.
The Early Permian witnessed significant shifts in both the paleogeographic location and paleoclimatic conditions of the North China Craton, leading to the formation of extensive coal deposits. This study investigates the No. 7 and No. 8 coal seams of the Lower Permian Damiaozhuang Formation in the Kaiping Coalfield, located on the northeastern margin of the North China Craton. Through integrated analyses using Gas Chromatography-Mass Spectrometry, X-ray Diffraction, Inductively Coupled Plasma Mass Spectrometry, and Electron Probe Microanalysis, this study elucidates the enrichment mechanisms of rare earth elements (REEs) and reconstructs the paleoenvironmental conditions of coal formation. The results indicate that the No. 7 and No. 8 coal seams exhibit significant concentration in REEs, with average concentrations of 319.38 ppm and 690.27 ppm, respectively. The ratios of (La/Lu)(N), (La/Sm)(N), and (Gd/Lu)(N) indicate a light RRE (LREE)-enriched pattern. Monazite, identified as discrete grains disseminated within clay minerals, serves as the host mineral for REEs in coal deposits. The enrichment of REEs in coal is attributed to the combined influence of synsedimentary processes, REE-bearing minerals, organic matter, and clay minerals. Geochemical proxies, including Sr/Ba, Cu/Zn, Ni/Co, and delta Eu values, together with the ternary diagram of fluorene compounds and cross-plots of alkyl dibenzothiophenes/alkyl dibenzofurans versus Pr/Ph ratios, indicate an oxidizing freshwater depositional environment. Furthermore, Sr/Cu ratios exceeding 5 suggest a warm to hot paleoclimate during the coal formation. The bimodal distribution pattern observed in the mass chromatograms of n-alkanes, combined with parameters such as Pr/C-17, Ph/C-18, Carbon Preference Index, and Odd-to-Even Predominance, indicates that the organic matter was derived predominantly from higher plants, with a minor contribution from aquatic organisms. The sedimentary provenance is primarily identified as felsic rocks. This study elucidates the enrichment mechanisms of REEs in coal and reconstructs the associated coal-forming environment, providing a theoretical basis for the exploration and identification of REE-enriched coals.
With the increasing call for sustainable energy storage, solid-state batteries (SSBs) stand out as promising for dealing with the safety, energy density, and lifespan performance bottlenecks associated with commercial lithium-ion systems. Nonetheless, factors such as dendrite formation and unstable electrode–electrolyte interfaces still hamper their commercial adoption in grid-scale renewable applications. In this work, we present a quantum-informed artificial intelligence framework that combines quantum chemistry-based interfacial descriptors and machine learning algorithms to predict, suppress, and optimize dendritic propagation and interfacial breakdown in lithium-metal SSBs. The framework benefits from density functional theory-based quantum simulations to guide the training of graph-based artificial intelligence predictors, thus offering material and interface design in a realistic operational environment. Our work shows enhanced thermal stability, ionic conductivity, and interface coherence and provides a strategic step toward the practical applications of deployable SSBs for smart grids. The model is consistent with the Sustainable Development Goal objectives for affordable energy, climate action and innovation, and it represents a road for scalable, sustainable, and intelligent energy storage.
Deep shale gas in western Chongqing is a critical successor to conventional resources, but its distinct enrichment mechanisms make sweet-spot identification-particularly of productive layers-significantly challenging. To address this, we developed an integrated approach combining high-fidelity gas-content testing under simulated high-temperature, high-pressure (HTHP) conditions with advanced mineralogical characterization using automated mineralogy (TIMA). Four exploration wells in comparable sedimentary-structural settings were analyzed to establish correlations between reservoir properties and gas content. Results show that the lower Longmaxi Formation in central-western Chongqing exhibits optimal reservoir quality, with high porosity, total organic carbon (TOC), brittleness, and gas content. A substantial free-gas fraction enhances recoverability. The reservoir's quartz-organic matter microfacies provides strong mechanical brittleness, adsorption capacity, and gas supply. High TOC and abundant authigenic microcrystalline quartz are key controls on gas accumulation. These findings clarify the geological drivers of deep shale gas enrichment and provide a direct basis for geology-engineering integration in sweet-spot optimization and development planning.
Heavy-oil reservoirs operating under solution-gas drive may exhibit foamy-oil flow behavior, in which dispersed gas and delayed gas mobility enhance oil recovery beyond conventional expectations. However, predicting foamy-oil production remains challenging because of complex multiphase transport processes and strong sensitivity to operational conditions, particularly pressure depletion rate. To address this challenge, this study develops a simulation-informed machine-learning surrogate framework for rapid and interpretable prediction of foamy-oil production under controlled pressure depletion conditions. A calibrated thermal–compositional model was constructed in CMG-STARS using laboratory depletion experiments conducted in a 2-m sand-pack system. A simulation-based design-of-experiments (DOE) approach was then employed to generate datasets spanning realistic ranges of fluid properties, relative permeability characteristics, and foamy-oil kinetic parameters. Gradient-boosting machine-learning models were trained to reproduce key production responses, including oil rate, gas rate, gas–oil ratio, and cumulative recovery. The resulting surrogate models achieved high predictive accuracy, with coefficients of determination exceeding 0.95 and average prediction errors below 5%, while reducing computational time by several orders of magnitude compared with full-physics simulations. Explainable machine-learning analysis was further applied to quantify the relative importance of governing parameters. The results indicate that pressure depletion rate is the dominant control on production behavior, followed by gas liberation kinetics and critical gas saturation. The proposed framework demonstrates how simulation-informed surrogate modeling combined with explainable machine learning can provide both rapid prediction capability and transparent sensitivity analysis for complex foamy-oil production systems. The workflow therefore enables efficient scenario evaluation and provides a practical decision-support tool for forecasting and optimizing foamy-oil production strategies.
The present study develops and evaluates a hybrid Organic Rankine Cycle–Turboexpander (ORC–TE) system integrated into a Natural Gas Pressure Reduction Station (NGPRS) to recover both thermal and pressure-exergy losses. A comprehensive thermo-exergoeconomic model is formulated by coupling first- and second-law energy equations with component-level cost functions. The system performance is analyzed under steady-state conditions using a MATLAB–Engineering Equation Solver hybrid computational framework. Three conflicting objectives were simultaneously considered: maximizing Exergy Efficiency and Thermo–Environmental Synergy Indicator, while minimizing the total cost rate. The obtained Pareto fronts revealed strong coupling between thermodynamic enhancement and cost reduction, with an optimal compromise (Benchmark Case Study, BCS) Exergy Efficiency of 0.71, an Thermo–Environmental Synergy Indicator of 0.0275, and a Normalized Total Cost Rate of 0.47. A comprehensive sensitivity analysis identified the turbine isentropic efficiency and gas inlet temperature as the most influential parameters. To select the final operating configuration, a TOPSIS decision-making framework employing equal, entropy, and sensitivity-based weighting schemes was applied to the Pareto data. Among these, the sensitivity-derived weights produced the most stable and physically consistent ranking, yielding the highest normalized closeness coefficient of 0.73. Overall, the developed optimization framework demonstrates that the ORC–TE hybrid can achieve up to 84 % exergy efficiency with a 28 % reduction in total cost rate compared with the baseline Pressure Reduction Station, confirming its high techno-economic and environmental viability for industrial natural gas networks.
Based on detailed analyses of fine-grained sedimentary characteristics and rock assemblages in coal measures, as well as correlations between macroscopic and microscopic sedimentary components, this study systematically investigates the types of fine-grained sedimentary fabrics in coal-bearing strata. A combination of macroscopic description, petrographic characterization, experimental analyses, and maceral identification was employed to classify the fabrics. According to mineralogical composition-particularly the abundance of key minerals-together with textural attributes and sedimentary structures, the fine-grained sedimentary fabrics of the coal measures are classified into carbonaceous clayey fabric, siliceous-clayey mixed fabric, organic-clayey composite fabric, and clayey silty fabric. On this basis, the lithofacies associations of the coal measures, the dominant sedimentary facies, and their spatial and temporal variations are further analyzed. The results demonstrate that systematic characterization of fine-grained sedimentary fabrics in coal measures is of considerable significance for reconstructing the paleogeography of coal-forming basins and for evaluating the reservoir potential of fine-grained sedimentary rocks.
Interconnected microgrid systems (IMSs) provide a strong foundation for improving the effectiveness of multiple distributed energy resources (DERs); however, operating numerous DERs in tandem remains a major obstacle. This paper presents a distributed control strategy (DCS) based on a federated learning (FL) fuzzy-optimised recurrent neural network (F-RNN), which uses an adaptive fractional-order proportional–integral–derivative (FOPID) controller to regulate frequency deviation. The control problem is formulated as a fractional-order consensus control strategy, using a Lyapunov-based framework that captures the dynamic behaviour of the RNN. Controller parameters are obtained from the dynamics of the energy function to update neuronal states, whereas FL is used during training to enhance network performance through improved information exchange. The proposed controller was demonstrated in Simulation/MATLAB and in real-time OPAL-RT Hardware-In-The-Loop under various conditions like load-demand uncertainty, stochastic loads, presence or absence of communication delay, communication link failure and source loss. Results based on quantitative analysis show that the proposed controller will be superior to the conventional controllers. More specifically, the proposed controller provides up to 73% reduction in integral absolute error, 33% lowering of the integral time absolute error, and greater than 70% improvement in integral time-weighted squared error as compared to the RNN–FOPID controller. The settling time has been improved from approximately 6.1 s down to 3.0 s with the peak frequency deviation being reduced by nearly 80%, illustrating the improved damping and transient performance of the proposed method.
Green hydrogen is recognized as a critical energy carrier for deep decarbonization, yet many production pathways remain reliant on fossil fuels or conventional photovoltaic electrolysis. This study presents a comparative performance assessment of a solar thermal-driven hydrogen production system integrating an organic Rankine cycle (ORC) with an alkaline electrolyzer. The proposed configuration comprises solar thermal collectors, a pressurized sensible heat storage tank, an ORC power unit, and an electrolyzer. A dynamic MATLAB-based simulation framework was developed to evaluate eight system configurations under identical meteorological conditions over a full year of hourly operation. The investigated scenarios examined the combined effects of collector technology (evacuated tube vs. parabolic trough), ORC working fluids (R245fa and n-pentane), and system sizing parameters. Performance was assessed based on annual hydrogen yield, overall solar-to-hydrogen efficiency, and productivity per unit collector area. The optimal configuration—employing parabolic trough collectors, an R245fa working fluid, a 180 m 2 collector field, and an 8 m 3 storage tank—achieved an annual hydrogen production of 752.8 kg·year − 1 (4.705 kg·m − 2 ·year − 1 ), corresponding to 25,094 kWh·year − 1 of hydrogen energy output. The overall solar-to-hydrogen efficiency reached 7.03%, with a solar-to-ORC efficiency of 10.2%. Results underscore the critical role of thermal availability and storage stability in enhancing ORC operating hours and hydrogen yield, demonstrating the viability of solar thermal ORC–electrolyzer systems for sustainable hydrogen production in high-insolation regions.
Traditional research on coal seam borehole pressure relief has primarily focused on qualitative static stress transfer, while the continuous dynamic evolution of the pressure relief effect remains under-explored. Given the significant creep characteristics of deep coal, this study employed numerical simulation tests and in-situ electromagnetic wave computed tomography (CT) detection to investigate the dynamic evolution characteristics of stress transfer, energy release, and the rock burst hazard index in the roadway ribs following borehole pressure relief. Furthermore, the influence of parameters such as borehole diameter, spacing, and depth on the evolution of the pressure relief effect was examined. The results indicate that the evolution of the borehole pressure relief effect in deep creeping coal can be categorized into two stages: instantaneous pressure relief and creep-induced pressure relief. After drilling, the creep deformation of the coal mass increases, and fractures around the borehole gradually propagate. Consequently, the stress, stored energy, and the rock burst hazard index in the pressure relief zone continuously decrease, although this attenuation decelerates as the duration of creep increases. As the borehole diameter and depth increase, while spacing decreases, the reduction rates of stress, energy, and the rock burst hazard index during the creep-induced pressure relief stage accelerate significantly, thereby enhancing pressure relief efficiency. Field applications in roadways prone to rock bursts have demonstrated that the anomaly index of the coal mass's absorption coefficient within the electromagnetic wave CT detection area gradually increases, while the rock burst hazard index decreases. In deep mining roadways, it is crucial to accurately determine the advanced pre-relief distance to fully leverage the dual effects of instantaneous and creep-induced pressure relief, thereby enhancing the effectiveness of rock burst prevention.
The mechanical behavior of deep tight sandstone reservoirs, particularly the brittle–plastic transition under high confining pressure, is critical for drilling safety and efficient reservoir development. However, under deep-reservoir confinement (typically >90 MPa), the micro–macro origin of the brittle–plastic transition and the phase-dependent damage pathway remain insufficiently quantified. This study investigates the intrinsic relationship between macroscopic mechanical response and microscale damage mechanisms of tight sandstone under high confinement. Tight sandstone from the Permian Jiamuhe Formation in the Junggar Basin was selected, and a digital rock model was constructed using a three-dimensional Voronoi polyhedron algorithm informed by mineral composition (XRD) and mechanical parameters, with pores and clay units incorporated to represent heterogeneity. The model parameters were calibrated, and the digital-rock scale was assessed by comparison with laboratory stress–strain curves at σ 3 = 0–60 MPa, and then extended to numerical triaxial compression tests under confining pressures of 0–200 MPa. Damage evolution was simulated using the equivalent modulus method to analyze stress–strain characteristics, damage progression, and equivalent stress distribution. The results show that: (1) At the macroscopic scale, compressive strength and Young's modulus increase significantly with confinement, reaching 1005 MPa and 86.1 GPa, respectively, at 200 MPa, while post-peak behavior shifts from brittle fracture to plastic softening. (2) At the microscale, confinement fundamentally alters the damage path. At 0 MPa, damage initiates in hard minerals (quartz, K-feldspar) due to localized stress concentrations (>133 MPa), coalescing into macroscopic cracks. At 200 MPa, the sequence reverses: softer minerals (albite, muscovite) yield plastically under stresses of 576–672 MPa (damage factor <0.8), whereas quartz, even under stresses up to 1152 MPa, remains intact because confinement suppresses crack propagation, acting instead as the load-bearing skeleton in the plastic stage. (3) The brittle–plastic transition is controlled by three cooperative mechanisms: stress-field homogenization, reversal of the damage path, and transformation of the damage mode.
This study presents a comparative assessment of energy sustainability potential using the Integral Sustainability Potential Index (ISPI), which integrates four indicators: energy intensity (EI), the share of renewable energy sources (RES) in electricity generation, the diffusion of ISO 50001-certified energy management systems (EnMS), and political readiness. The methodology is applied to Central and Eastern European countries (Poland, Romania, Bulgaria, Slovakia, and Ukraine). The results reveal substantial cross-country differences in sustainability potential, primarily driven by institutional capacity, policy coherence, and the scale of ISO 50001 implementation. Poland demonstrated the highest ISPI value (0.68), reflecting moderate EI, a relatively high RES share, widespread adoption of ISO 50001 in the industry, and strong political support for EnMS. Romania (ISPI = 0.56), Bulgaria (ISPI = 0.39), and Slovakia (ISPI = 0.37) exhibit a medium level of sustainability potential, supported by notable RES penetration and policy initiatives but constrained by the limited diffusion of ISO 50001. Ukraine shows a significantly lower ISPI value (0.32), which is associated with high-EI (≈ 2.68 toe/1000 €), a lower RES share, weak implementation of ISO 50001, and insufficient policy support for EnMS. Cross-country analysis reveals a consistent pattern: countries with the lowest ISPI values (0.32–0.37) have the highest EI (>95 kilogrammes of oil equivalent/1000 €) and the lowest ISO 50001 certification density (<10 per million populations), while the country with the highest ISPI (0.68) combines moderate EI with the highest certification density. Overall, the findings indicate that economies with more developed managerial and policy frameworks have higher sustainability potential despite structural challenges in the energy sector.
To address water accumulation in old goafs of deep coal mines, which readily forms water inrush hazards and makes grout diffusion difficult to predict, we established a three-dimensional continuous spatial distribution model of porosity and permeability in the goaf based on O-ring and key stratum theories. A multiphysics-coupled numerical model combined with response surface methodology (RSM) was used to quantitatively analyze the effects of influencing factors and their interactions on grout diffusion behavior. The results show that grout diffusion within the goaf is strongly asymmetric: the horizontal diffusion distance exceeds 25 m, the upward vertical diffusion distance exceeds 5 m, and the downward vertical diffusion distance exceeds 2 m. The permeability of the porous medium and grouting pressure promote diffusion, whereas grout viscosity inhibits it. The RSM-based grout diffusion volume prediction model was applied to the design of a fly-ash grout backfilling project in a goaf of a coal mine in Shaanxi Province. Considering pressure attenuation along the flow path and the heterogeneous seepage field, a borehole spacing of 40 m was determined, providing a theoretical basis and technical support for water-hazard control and optimization of grouting parameters in similar old goafs of deep coal mines.
