Search Results (51)

Aerogels combine ultralow density with high surface area, yet their brittle, open networks preclude tensile deformation and hinder integration into wearable electronics. Here we introduce a prestrain-enabled coaxial architecture that converts a brittle conductive aerogel into a highly stretchable fiber. A porous thermoplastic elastomer (TPE) hollow sheath is wet-spun using a sacrificial lignin template to ensure solvent exchange and robust encapsulation. Conductive polymer-based precursor dispersions are infused into prestretched TPE tubes, frozen, and lyophilized; releasing the prestretch then programs a buckled aerogel core that unfolds during elongation without catastrophic fracture. The resulting TPE-wrapped aerogel fibers exhibit reversible elongation up to 250% while retaining electrical function. At low strains (<60%), resistance changes are small and stable (ΔR/R₀ < 0.04); at larger strains the response remains monotonic and fully recoverable, enabling broad-range sensing. The mechanism is captured by a strain-dependent percolation model in which elastic decompression, contact sliding, and controlled fragmentation/reconnection of the aerogel network govern the signal. This generalizable strategy decouples elasticity from conductivity, establishing a scalable route to ultralight, encapsulated, and skin-compatible aerogel fibers for smart textiles and deformable electronics. Full article

In response to the imperative to mitigate methane—one of the most potent greenhouse gases—this study proposes and tests an integrated workflow for designing methane drainage boreholes targeting post-mining areas in an active underground coal mine (Pniówek, Poland). The workflow combines the following: (1) forecasting methane emissions from goafs and active longwalls for 2024–2040; (2) 3D geological characterization (structural and lithofacies models); (3) selection and sealing of goaf zones; and (4) optimization of well placement, drilling, and performance evaluation of drainage boreholes, including an assessment of energy use from the recovered gas. Applying the method delineated priority capture zones and estimated recoverable rates under multiple scenarios. Preliminary field data from a borehole near seam 362/1 indicate stable methane inflow to the drainage system and a concomitant reduction in methane load within the ventilation network. The integrated design improves targeting efficiency and provides a quantitative basis for scheduling, risk management, and sizing of surface-to-underground infrastructure. The results suggest that systematic drainage of post-mining voids can enhance safety, limit fugitive emissions, and create opportunities for on-site power generation. The approach is transferable to other active mines with legacy workings, provided site-specific calibration and monitoring are implemented. Full article

This study systematically investigates the synergistic modification effects of two high-modulus additives on SBS-modified asphalt through microstructural characterization and performance evaluation. Fluorescence microscopic analysis reveals that the additive particles undergo swelling over time and form an interconnected network structure via phase separation dynamics. Rheological tests demonstrate a significant enhancement in high-temperature performance: at the optimal dosage of 10 wt%, the complex modulus increases by approximately 215%, and the rutting factor improves by about 300% compared to the control group. The results from multiple stress creep recovery (MSCR) tests confirm the material’s superior elastic recovery capability and reduced non-recoverable creep compliance. However, the incorporation of the additives adversely affects low-temperature ductility. The penetration of (two distinct high-modulus agents, designated as HMA-A and HMA-B) HMA-B decreases by approximately 36.8% more than that of HMA-A, accompanied by significantly lower low-temperature toughness. A dosage of 10% is identified as the critical threshold, which maximizes rutting resistance while minimizing low-temperature performance degradation. Based on these findings, this paper proposes an integrated design paradigm of “microstructure–performance–dosage,” recommending HMA-B for high-stress pavement channels and HMA-A for regions with substantial temperature variations. Full article

To address the flood risks driven by climate change and urbanization, this study proposes the DRIRA model (Driving Force, Resistance, Influence, Recoverability, Adaptability). Distinct from BRIC (Baseline Resilience Indicators for Communities) and PEOPLES (Population, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle, Economic Development, Social–Cultural Capital), the model emphasizes dynamic interactions across the entire disaster lifecycle, introduces the “Influence” dimension, and integrates SNA (Social Network Analysis) with a modified gravity model to reveal cascading effects and resilience linkages among cities. Based on an empirical study of 30 cities in the Central Plains Urban Agglomeration, and using a combination of entropy weighting, a modified spatial gravity model, and social network analysis, the study finds that: (1) Urban flood resilience increased by 35.5% from 2012 to 2021, but spatial polarization intensified, with Zhengzhou emerging as the dominant core and peripheral cities falling behind; (2) Economic development, infrastructure investment, and intersectoral governance coordination are the primary factors driving resilience differentiation; (3) Intercity resilience connectivity has strengthened, yet administrative fragmentation continues to undermine collaborative effectiveness. In response, three strategic pathways are proposed: coordinated development of sponge and resilient infrastructure, activation of flood insurance market mechanisms, and intelligent cross-regional dispatch of emergency resources. These strategies offer a scientifically grounded framework for balancing physical flood defenses with institutional resilience in high-risk urban regions. Full article

Geological modeling is generally based on deterministic models, which provide a single representation of reality. Probabilistic modeling is more appropriate when quantifying or understanding the uncertainty associated with a parameter of interest as it considers several equally probable geological scenarios. The object of this study is to quantify the uncertainty in the estimation of the minerals in the Punta Alegre gypsum deposit, by applying a new method based on the simple normal equation geostatistical simulation technique. The Punta Alegre gypsum deposit is a sedimentary deposit of clastic origin, formed by the complex redeposition of salts, gypsum and other sediments. To carry out this research, 50 equiprobable scenarios were simulated, reproducing overburden, gypsum series (different types of gypsum) and intercalated non-mineral lithologies (limestone and other rocks) in a network of nodes measuring 5 × 5 × 5 m, using a training image, composites and prior probability maps as input data. As a result of scaling the previously simulated geological units, three-dimensional models of volume proportions and estimation error for gypsum were obtained for panels measuring 10 × 10 × 5 m. The quantification of the uncertainty of the gypsum volume, determined by the root mean square error, established that the volume estimation error is small at a global scale (6.51%), given that there is no significant variation when comparing the deterministic model with the gypsum proportion model obtained from the 50 simulated scenarios. Conversely, at the local scale, there is a significant variation in gypsum volume of 42% in the 10 × 10 × 5 m panels with a future impact on recoverable mining resources, given the uncertainty at a local scale, which will cause an increase in mining dilution due to the inclusion of non-mineral lithologies within the extracted mineral that will be sent to the processing plant. On the other hand, it will cause changes in the mining company’s plan in areas where there are panels that were previously accounted for by the deterministic model as minerals and are not actually exploitable. Full article

Periodic train timetables provide passengers with easily accessible rail transport services. However, in networked railway operations, some long-distance cross-line trains have high operational priority and pose difficulties for scheduling local services. In this paper, we address the minimal-cycle-length periodic train timetabling problem with high-priority cross-line operations and complex local train types. We propose a special set of constraints to accommodate the prespecified operational times of cross-line operations with regard to system robustness. As the cycle length is regarded as a decision variable, the formulation is nonlinear. To solve the problem, we exploit the connection between cycle length and consumed capacity of periodic timetables and propose high-level cycle-capacity and binary search-guided iterative solution frameworks, which implicitly enumerate the periodic train timetabling problems. Using the real-world operational data of the Guangzhou–Zhuhai Intercity Rail Line, we explore the solution performance of the proposed solution approaches and the straight linearization of the problem, and we also compare the practices of fixing prespecified operational times and our proposed constraints for the cross-line services. The results demonstrate that our proposed method can efficiently achieve flexible while recoverable operational times for the cross-line services and the proposed implicit enumeration algorithms significantly outperform the direct linearization, which increases the search space significantly due to the considerable dimensionality of the periodic decision variables involved. Numerical computations also suggest that our proposed constraints provide a type of approach for balancing the operational convenience and stability margins available in the periodic timetable with the presence of cross-line operations. Full article

High-concentration polyvinyl alcohol (PVA) wastewater from 3D printing applications presents significant treatment challenges due to PVA’s water solubility, chemical stability, and resistance to biodegradation. In this study, we investigated the enhanced removal of high-concentration PVA (3–7 g/L) using a modified ferrite process with H₂O₂ pre-oxidation, while simultaneously exploring the formation and properties of magnetic precipitates. The effects of PVA concentration, reaction conditions, and thermal treatment (100 °C and 650 °C) on the magnetic precipitates were studied through XRD, TEM, FTIR, and magnetic measurements. Results showed that H₂O₂ pre-oxidation effectively maintained the system pH and improved PVA removal efficiency, achieving a COD reduction of 83% after two-stage treatment for 7 g/L PVA solution. XRD and TEM analyses revealed that precipitates formed at 100 °C consisted of dispersed Fe₃O₄ nanoparticles within PVA fibrous networks, while calcination at 650 °C led to the formation of rod-like structures and agglomerated particles. The magnetic properties varied significantly with treatment conditions, exhibiting the highest saturation magnetization of 10.30 emu/g for sample calcinated at 100 °C. This study demonstrates the potential of the modified ferrite process for treating high-concentration PVA wastewater while producing recoverable magnetic nanoparticles, providing a dual-function approach to address environmental challenges posed by the 3D printing industry. Full article

Due to urban expansion and limited heat sources, the heating capacity of heat supply stations is inadequate to meet the growing heat demand. In current heat supply stations, heat from the primary heat network is generally conveyed to the secondary heat network solely via plate heat exchangers, resulting in the return water temperature of the primary heat network being as high as 50 °C, with a substantial amount of recoverable waste heat resources. In this paper, a case study of a heat supply station with insufficient heating capacity in Beijing is conducted to propose supplemental heating systems using vapor-compression heat pumps and absorption heat pumps to further extract waste heat from the primary heat network. Through the TRNSYS platform, simulation models for both systems were developed. Then, based on the bilevel optimization method, the design scheme and operational strategy were co-optimized with the objective of minimizing the lifecycle cost. The performance of the two systems was compared from the perspectives of energy consumption, economy, additional footprint, and regional applicability. The results indicate that the energy consumption of the vapor-compression heat pump supplemental heating system (VCSHS) is 0.85% higher than that of the absorption heat pump supplemental heating system (ASHS), with supplementary heat of 3500 kW. The initial cost of the VCSHS is approximately 1 million CNY lower than that of the ASHS, while the operational costs of both systems are nearly identical, making the VCSHS more cost-effective overall. Additionally, the footprint of new equipment in the VCSHS is nearly 30% smaller than that in the ASHS. Compared with cold regions, it is more economical to adopt ASHSs in severe cold regions due to their lower heat price. Full article

China has vast amounts of deep coalbed methane resources but is still in the early stage of deep coalbed methane development; thus, it lacks mature gas exploitation and development theories and technologies, particularly effective methods for evaluating final recoverable reserves. This paper intends to develop a method that can rapidly and accurately predict deep coalbed methane EUR before well spacing to guide the formulation of rational exploitation schemes and full exploitation of geological resources, thus lowering costs and enhancing efficiency. Taking deep coalbed methane in the Daning–Jixian block of the Ordos Basin as the research object, this paper first uses the production decline method to evaluate the EUR of brought-in wells and analyzes the influence of geological conditions and engineering parameters on the EUR. Second, the ADASYN method is used to process the unevenly distributed samples to solve the small number and poor representativeness of the machine learning model samples. After this, the BP neural network, support vector machine, and Gaussian process regression are used to build EUR evaluation models, and the models are compared and the best is selected. Lastly, the selected EUR evaluation model is applied to analyze the influence weights of geological conditions and engineering parameters on EUR. According to the research results, the MAPEs of the BP neural network, support vector machine, and Gaussian process regression models reach 7.03%, 7.23%, and 1.28%, respectively, after ADASYNA oversampling. However, the Gaussian process regression model may bear the risk of overfitting. The model comparison results show that the support vector machine model is superior to the BP neural network model and the Gaussian process regression model. Therefore, the support vector machine is favorably selected to predict EUR in this paper. Feature importance analysis results indicate that engineering parameters (including clusters, horizontal length, fracturing liquid, and proppant) are the major factors influencing the EUR prediction results. This paper establishes a model for predicting the EUR of deep coalbed methane, which provides a reference for the future formulation of well spacing schemes in the surveyed region. Full article

Resource-exhausted cities are cities where the ratio of exploited reserves to recoverable reserves exceeds 70%. Long-term energy extraction and consumption lead to weak economic growth, idle industrial land, and ecological imbalances. It is imperative to explore sustainable development paths that are green and low-carbon. The spatial characteristics of cities and the structure of energy networks are crucial foundations for low-carbon development and energy security in cities. The main research content includes three aspects: (1) The first involves the identification of the distribution characteristics of typical resource- exhausted cities worldwide. This mainly includes coal, oil, metallurgy, forestry, and non-metallic minerals. Among them, coal resource-exhausted cities are the most numerous, mainly distributed in China, Australia, the United States, etc. (2) The second includes an analysis of the spatial characteristics of resource-exhausted cities in China. This involves taking 24 resource-exhausted prefecture-level cities in China as the research objects, integrating geographic data such as Points of Interest (POIs), and using machine learning for accurate quantitative identification and spatial delineation of urban functions. The production space and ecological space of cities show an aggregated distribution pattern, while the living space is randomly distributed. (3) The third is based on urban energy consumption data, utilizing the modified gravity model and social network analysis (SNA), and analyzing the centrality/relevance, relationship density and frequency, and accessibility. The average degree of centrality of the 17 coal-related industries is 5.529, demonstrating the energy network structure of resource-exhausted cities. This paper provides data foundations and technical methods for achieving urban energy renewal, ecosystem stability, and optimized spatial structures. Full article

Shale gas, a significant recoverable natural gas resource trapped in shale formations, represents a significant energy reservoir. Although China has significant recoverable shale gas reserves, the challenge of controlling drilling costs remains a critical barrier to efficient development. This study presents a novel stacked ensemble learning model that integrates support vector machine (SVM) and long short-term memory (LSTM) networks to improve the accuracy of shale gas drilling cost prediction. The methodology consists of three main phases. First, we constructed a comprehensive, multidimensional spatiotemporal dataset of shale gas drilling costs. Second, we used Gradient Boosting Decision Tree (GBDT) modelling to rank the importance of various factors influencing drilling costs. Finally, we developed a stacked ensemble learning model combining SVM and LSTM architectures to achieve superior cost prediction accuracy. Experimental results demonstrate the effectiveness of the model, with the coefficient of determination (R²) improving from 0.25189/0.33834 (traditional SVM/LSTM models) to 0.55934. Model validation using selected well investment data from the Changning Block shows promising performance, achieving a Mean Absolute Percentage Error (MAPE) of 6.41%, with optimal prediction accuracy in the medium investment range (60–70 million yuan). This innovative approach provides a reliable tool for predicting shale gas drilling costs and offers new methodological perspectives for cost reduction strategies. The results contribute significantly to the sustainable development of shale gas resources and provide valuable insights for industry practitioners and researchers in the fields of energy economics and resource management. Full article

Heat integration has been widely and successfully practiced for recovering thermal energy in process plants for decades. It is usually implemented through synthesizing heat exchanger networks (HENs). It is recognized that mechanical energy, another form of energy that involves pressure-driven transport of compressible fluids, can be recovered through synthesizing work exchanger networks (WENs). One type of WEN employs piston-type work exchangers, which demonstrates techno-economic attractiveness. A thermodynamic-model-based energy recovery targeting method was developed to predict the maximum amount of mechanical energy feasibly recoverable by piston-type work exchangers prior to WEN configuration generation. In this work, a heat-integrated WEN synthesis methodology embedded by the thermodynamic model is introduced, by which the maximum mechanical energy, together with thermal energy, can be cost-effectively recovered. The methodology is systematic and general, and its efficacy is demonstrated through two case studies that highlight how the proposed methodology leads to designs simpler than those reported by other researchers while also having a lower total annualized cost (TAC). Full article

As a non-renewable resource, oil faces increasing demand, and the remaining oil recovery rates in existing oil fields still require improvement. The primary objective of this study is to investigate the impact of pore structure parameters on the distribution and recovery of residual oil after polymer flooding by constructing a digital pore network model. Using this model, the study visualizes the post-flooding state of the model with 3DMAX-9.0 software and employs a range of simulation methods, including a detailed analysis of the pore size, coordination number, pore–throat ratio, and wettability, to quantitatively assess how these parameters affect the residual oil distribution and recovery. The research shows that the change in the distribution of pore sizes leads to a decrease in cluster-shaped residual oil and an increase in columnar residual oil. An increase in the coordination number increases the core permeability and reduces the residual oil; for example, when the coordination number increases from 4.3 to 6, the polymer flooding recovery rate increases from 24.57% to 30.44%. An increase in the pore–throat ratio reduces the permeability and causes more residual oil to remain in the throat; for example, when the pore–throat ratio increases from 3.2 to 6.3, the total recovery rate decreases from 74.34% to 63.72%. When the wettability changes from oil-wet to water-wet, the type of residual oil gradually changes from the difficult-to-drive-out columnar and film-shaped to the more easily recoverable cluster-shaped; for example, when the proportion of water-wet throats increases from 0.1:0.9 to 0.6:0.4, the water flooding recovery rate increases from 35.63% to 51.35%. Both qualitative and quantitative results suggest that the digital pore network model developed in this study effectively predicts the residual oil distribution under different pore structures and provides a crucial basis for optimizing residual oil recovery strategies. Full article

The widespread use of Unmanned Underwater Vehicles (UUVs) in seafloor observatory networks highlights the need for docking stations to facilitate rapid recharging and effective data transfer. Floating docks are promising due to their flexibility, ease of deployment, and recoverability. To enhance understanding and optimize UUV docking with floating docks, we employ dynamic fluid body interaction (DFBI) to construct a seabed moored suspended dock (SMSD) model that features a guiding funnel, a suspended body, and a catenary of a mooring chain. This model simulates SMSD equilibrium stabilization in various ocean currents. Then, a UUV docking model with contact coupling is developed from the SMSD model to simulate the dynamic contact response during docking. The accuracy of the docking model was validated using previous experimental data. Through investigation of the UUV docking response results, sensitivity studies relating to volume, moment of inertia, mass, and catenary stiffness were conducted, thereby guiding SMSD optimization. Finally, sea tests demonstrated that the SMSD maintained stability before docking. During docking, the SMSD’s rotation facilitated smooth UUV entry. After the UUV docked, the SMSD was restored to its original azimuth, confirming its adaptability, stability, and reliability. Full article

In order to improve the effect of injection–production coupling development to improve crude oil recovery in complex fault-block reservoirs, we carried out a physical simulation experiment based on a sandpack model of transforming water-driven development into injection–production coupling development and quantitatively evaluated the influence of rounds of injection pressure coupling on the crude oil mobilization in reservoirs with different permeability levels and on oil recovery. Meanwhile, the characteristics of residual oil were studied via a numerical simulation method. The mechanism of increased oil production via injection–production coupling development was revealed by analyzing the water and oil contents, formation pressure, and streamline fields through the establishment of mechanism models. The results of the physical experiment show that injection–production coupling can improve the recovery effect of medium- and low-permeability reservoirs by 55.66%. With an increase in the injection pressure, the oil recovery percentage of the low-permeability sandpack model at 20 MPa is 100%, and this study finds that injection–production coupling is the main way to develop the recoverable oil in a low-permeability reservoir. The numerical simulation results show that among the four remaining oil distribution types (interwell-enriched, low-permeability zone-enriched, well network imperfection, and mismatch between injection and production), the interwell-enriched type of the remaining oil reserves accounts for the highest proportion (48.52%). The simulation results of the mechanism model show that water-driven development easily leads to streamline solidification, resulting in ineffective circulation of the injected water. Compared with conventional water-driven development, the pressure propagation range is significantly increased in injection–production coupling development. The reservoir streamline distribution is more continuous and uniform, and the flooding wave is wider in volume and range. This research provides a theoretical basis for the injection–production coupling technology policy in complex fault-block reservoirs. Full article