Multistage hydrocarbon remigration is common in foreland-basin thrust belts, but its timing and pathways remain poorly constrained. We investigated the Lower Cretaceous Yageliemu and Bashijiqike reservoirs in the Kela-2 gas field, Kuqa foreland basin. Molecular geochemical analyses of reservoir extracts and oil inclusions, combined with fluid-inclusion observations, were used to reconstruct the hydrocarbon charging and remigration history. Reservoir extracts and oil inclusions from both reservoirs show consistent molecular signatures, indicating a common origin from the Triassic Huangshanjie lacustrine source rocks. Fluid-inclusion thermometry combined with burial-history reconstruction indicates that lacustrine oil charged the deeper K1y reservoir earlier than the shallower K1bs reservoir, supporting a later phase of upward cross-stratal remigration associated with progressive fault reactivation. From approximately 5.3 Ma onward, large-scale coal-derived gas charging increasingly modified the earlier oil accumulations, and after ca. 2.5 Ma of sustained gas charging, the fully developed Kela-2 anticlinal trap became the dominant accumulation process.
Reducing sodium chloride in meat products is essential for improving their nutritional profile but often impairs technological and sensory quality. This study evaluated the effects of sodium reduction on pork meat products formulated with a fixed potato starch–tara gum system. Model products with varying NaCl levels (0.57%, 1.14%, 1.71%, and 2.28%) were analysed for physicochemical properties, texture, and sensory attributes during storage. Multivariate analysis (PCA, correlation analysis) and a composite quality index were applied. Compared with the control, a 50% reduction in NaCl content (to 1.14%) resulted in increased cooking loss (from 2.66% to 3.22%), increased free water content from 0.73% to 2.23% after 20 days of storage, and reduced hardness (from 20.84 to 16.28 N after 20 days of storage), indicating a weakening of the protein matrix. The tested potato starch–tara gum formulation maintained acceptable water retention and structural properties across the evaluated NaCl levels, although progressive NaCl reduction was associated with deterioration of several quality parameters. Strong correlations between sodium content, hardness, and water-related parameters were observed. While the lowest NaCl level resulted in quality deterioration, the 1.14% variant maintained acceptable properties, demonstrating effective sodium reduction without compromising quality.
The cold supply chain (CSC) reverse logistics segment remains underexplored in the risk assessment literature. Existing frameworks apply traditional failure mode and effects analysis (FMEA) with a three-criterion structure (severity, occurrence, detectability), which inadequately captures the time-sensitive and cost-intensive risk environment of CSC reverse logistics. This study proposes an integrated framework combining hazard analysis and critical control points (HACCP)-based node partitioning, an extended five-criterion FMEA incorporating timeliness and economic cost, triangular interval-valued fuzzy number (TIVFN) aggregation via the Aczél–Alsina operator, and cumulative prospect theory (CPT) to prioritize risk modes. Applied to a food-sector reverse CSC, 16 failure modes were identified across six HACCP-based process nodes. Severity and economic cost jointly account for 47.5% of total criterion weight. Monitoring and data management (node P6) consistently emerge as the dominant risk cluster, with data completeness (FM14), sensor accuracy (FM15), and early-warning functionality (FM16) occupying the top three positions across all weight scenarios. Relative to conventional models and the extended RPN, the TIVFN-CPT model assigns FM16 a substantially higher priority, demonstrating that CPT captures asymmetric, loss-averse risk perception that conventional methods fail to encode. Sensitivity analysis across five weight scenarios confirms the structural robustness of the rankings. By extending FMEA to a five-criterion behavioral framework and introducing TIVFNs as an uncertainty-preserving linguistic scale, this study indicates that information quality constitutes a risk factor of comparable priority to physical temperature maintenance, supporting a tiered resource allocation strategy that prioritizes investment at node P6 and targeted upgrades at nodes P1 and P4.
To address the continuous accumulation of carbon monoxide (CO) in the upper corner of working faces in shallow-buried easily spontaneous combustion coal seams, this study takes the 1113 fully mechanized mining face of Yidong Coal Mine in Shaanxi Province as the engineering background and investigates the CO migration characteristics in the upper corner and catalytic oxidation removal technology. First, a CFD model of gas migration in the goaf was established. The reliability of the model was verified by comparing the simulated oxygen concentration distribution characteristics and the range of the spontaneous combustion “three zones” with field monitoring results. Based on the validated model, the distribution and migration characteristics of CO concentration in the goaf were analyzed, revealing the migration process of CO from the goaf to the return air corner and its accumulation characteristics. Independent field monitoring further confirmed the occurrence of persistent CO accumulation and over-limit concentrations in the upper corner, supporting the engineering significance of the CO migration pathway predicted by the CFD model. To overcome the limitations of traditional prevention measures in removing already-formed CO, catalytic oxidation experiments were conducted to investigate the variation characteristics of gas components during CO oxidation under ambient temperature conditions. The effects of oxygen concentration, CO concentration, and catalyst dosage on CO removal performance were also studied. The results showed that the catalyst could achieve efficient CO oxidation removal and demonstrated high activity for low CO concentrations, with 100 ppm and 200 ppm CO being completely eliminated in laboratory tests. In field applications, the upper corner CO concentration was reduced from 80 to 142 ppm to 0–16 ppm. The research results provide a theoretical basis and technical reference for CO control in the upper corner of shallow-buried easily spontaneous combustion coal seams.
Agricultural drip fertigation is a highly coupled dynamic process in which precision resource management depends on the coordinated performance of the entire equipment chain. Against the backdrop of global water scarcity and excessive fertilizer application, improving the full-process precision of mixing, injection, sensing, control, distribution, and terminal delivery has become a prerequisite for the wider adoption of fertigation. This review evaluates advanced process-monitoring technologies and closed-loop control architectures within modern cyber-physical fertigation systems, covering fertilizer solution preparation and mixing, injection devices, liquid- and solid-phase state sensing, intelligent control algorithms, and pipeline distribution with terminal emitters. Online mixing has evolved from gravity-based batch pre-mixing toward continuous metered injection with vortex-guided static mixing, electrical conductivity (EC) sensing with drift compensation and granular mass flow detection form the perceptual basis of closed-loop regulation, control has advanced from proportional–integral–derivative (PID) controllers through variable-universe fuzzy logic to artificial neural network (ANN) hybrids with metaheuristic optimization, and pipeline pressure regulation together with emitter anti-clogging strategies determine long-term distribution uniformity. A quantitative analysis shows that the attainable precision of the sensing–decision–execution chain is bounded by the coupling among sensor accuracy, process delays, control performance, and actuator response rather than by any single device. The review identifies five unresolved gaps—sensor reliability, multi-season field validation, interoperability, low-cost automation, and fertilizer-type adaptability—and recommends that future research prioritize low-cost Internet of Things (IoT) sensor arrays on low-power wide-area networks, edge–cloud collaborative control, and foundation-model-driven autonomous decision-making, co-designed as one coupled specification.
Hyperthermophilic composting (HTC) is a promising approach for rapidly stabilizing organic waste and valorizing municipal biological residues; however, the microbial mechanisms underlying biomass conversion under hyperthermophilic conditions remain insufficiently understood. In this study, a pilot-scale HTC process was applied to anaerobically digested sludge, and temporal changes in bacterial communities and predicted functional genes were investigated. The process rapidly exceeded 90 °C within 2 days without external heating and maintained an average of approximately 77 ± 11 °C, demonstrating stable hyperthermophilic conditions. Moisture content concurrently decreased from 46.7% to 31.8% during the 40-day process. Bacillota and Actinomycetota dominated at the phylum level, while thermophilic genera including Thermobifida, Planifilum, Compostibacillus, and Oceanobacillus prevailed at the genus level. Functional prediction indicated increasing abundance of genes associated with carbohydrate metabolism and lignocellulose degradation throughout the process. Predicted gene profiles related to fengycin biosynthesis and oxidative stress responses suggested enhanced microbial adaptation to extreme conditions and potential suppression of pathogenic microorganisms. These findings provide integrated insights into microbial dynamics under hyperthermophilic conditions and highlight the potential of HTC as an efficient sludge stabilization strategy.
Self-Supervised Learning (SSL) has emerged as an effective paradigm for reducing the dependence of Human Activity Recognition (HAR) models on labeled data. To address the inadequate exploitation of IMU spatio-temporal correlations during pre-training and the limited generalization caused by simplistic fine-tuning strategies, a novel SSL framework for IMU-based HAR is proposed. The framework employs the Transformer and Depthwise Separable Convolution (DSC) to jointly capture global temporal dependencies and local spatial features, which are adaptively fused into discriminative spatio-temporal representations. These representations are subsequently enhanced through spatio-temporal feature extraction and multi-dimensional feature aggregation for downstream HAR. Furthermore, an IMU-based data acquisition platform was developed to construct the CQXY dataset. The proposed method was validated through comprehensive evaluations on four public datasets (UCI, Motion, HHAR, and Shoaib) and one self-collected dataset (CQXY). Experimental results show that, on the public datasets, the proposed method improves classification accuracy, F1-score, and Cohen’s kappa coefficient by an average of 13.11%, 14.24%, and 16.70%, respectively, compared with the baseline models. Similarly, on the self-collected dataset, the corresponding improvements reach 8.87%, 11.07%, and 10.81%. These results confirm the generalization of the proposed approach across datasets of different scales and domain.
The increasing penetration of distributed energy resources has strengthened the operational coupling between transmission and distribution grids, intensified voltage fluctuations, and reduced static voltage stability margins under bidirectional power-flow conditions. In transmission–distribution coordinated operation, conventional scheduling methods have difficulty simultaneously addressing boundary power interaction, renewable generation forecasting errors, real-time corrective control, and static voltage stability constraints. To address these issues, this paper proposes a transmission–distribution coordinated multi-time-scale rolling optimal control method considering static voltage stability. First, based on local network equivalencing, a static voltage stability index (SVSI) calculation method is developed for scenarios with bidirectional power-flow variations, enabling fast online assessment of static voltage stability boundaries. Second, the transmission–distribution boundary exchange power is selected as the key coordination variable, and a multi-time-scale rolling optimization framework is established to coordinate main-grid operational requirements with distribution-side flexible resources. At the long time scale, a baseline scheduling plan is generated by considering economic operation, renewable energy accommodation, and transmission–distribution boundary power exchange. At the short time scale, model predictive control (MPC) is adopted to perform closed-loop correction of control variables using rolling forecast information and real-time measurements, thereby achieving rolling coordinated control of distributed generation, energy storage systems, flexible regulation resources, and boundary exchange power. Furthermore, the SVSI is embedded into the rolling optimization model as a security constraint, forming a multi-objective coordinated control method that jointly considers economic performance, renewable energy accommodation capability, transmission–distribution interaction, and static voltage security margin. Case study results show that the proposed method can effectively improve renewable energy accommodation and reduce network losses while enhancing the static voltage stability margin. In addition, it improves the adaptability of the coordinated transmission–distribution system to operating-condition variations, forecasting deviations, and boundary power fluctuations.
The integration of zeolitic imidazolate framework-8 (ZIF-8) into polymer matrices has created a versatile class of nanocomposites with potential applications in gas separation, water purification, food packaging, sensing, catalysis, energy systems, and environmental remediation. However, their performance is governed by complex and strongly coupled variables, including ZIF-8 particle size, morphology, defect density, surface chemistry, filler loading, polymer compatibility, interfacial adhesion, dispersion state, and processing conditions. To organize this complexity, the review introduces a hierarchical design framework that distinguishes controllable synthesis and processing inputs, experimentally measurable intermediate material states, and condition-dependent performance outputs, thereby providing a structured basis for machine-learning-ready data representation. Conventional trial-and-error approaches are therefore often inefficient and provide limited capacity to identify transferable structure–processing–property relationships. This review examines the emerging role of machine learning (ML) in the rational design and optimization of ZIF-8/polymer nanocomposites for sustainable applications. Particular attention is given to the construction of material descriptors, selection of predictive algorithms, interpretation of feature importance, optimization of synthesis and processing parameters, and prediction of mechanical, thermal, barrier, transport, adsorption, catalytic, and antimicrobial properties. The review further discusses how supervised learning, explainable artificial intelligence, active learning, Bayesian optimization, transfer learning, and physics-informed models can support material screening and multi-objective optimization across performance, cost, energy consumption, environmental impact, and end-of-life considerations. Current limitations, including small and heterogeneous datasets, inconsistent reporting, insufficient negative results, limited model interpretability, and weak experimental validation, are critically evaluated. A future framework is proposed that integrates standardized databases, high-throughput experimentation, multiscale characterization, life-cycle indicators, uncertainty quantification, and closed-loop machine learning. Such an approach could accelerate the transition from empirical formulation toward data-driven, interpretable, and sustainability-oriented design of ZIF-8/polymer nanocomposites.
Centella asiatica (L.) Urban extracts are widely used in food, cosmetic, and pharmaceutical products. Conventional solvent extraction requires organic solvents and often produces dark green extracts due to chlorophyll co-extraction, limiting their industrial applications. This study developed an environmentally friendly aqueous two-phase system (ATPS) for the simultaneous extraction of triterpenoids of Centella asiatica. Response surface methodology with a central composite design was employed to optimize ethanol concentration, ammonium sulfate concentration, and temperature. A rapid and validated HPLC method for quantification of four triterpenoids—madecassoside, asiaticoside, madecassic acid, and asiatic acid—was developed and validated on a Poroshell 120 SB-C18 column (3.0 × 150 mm, 2.7 microns) at 40 °C. The developed method for all four triterpenoids was rapid (14 min run time) with acceptable system suitability, specificity, linearity (R > 0.9995), accuracy (98.42–101.52%), precision (0.16–1.36%), LOD (0.04–0.64 µg/mL), and LOQ (0.13–1.93 µg/mL). The ANOVA data from the central composite design of response surface methodology for total triterpenoid content were fitted with quadratic models, yielding acceptable R2 (>0.97), adjusted R2 (>0.95), and predicted R2 (>0.80). The optimum extraction conditions were 18.29% w/w ethanol, 33.93% w/w ammonium sulfate, and 60.04 °C, yielding a total triterpenoid content of 30.53 mg g−1 dry weight. The optimized ATPS produced higher yields of madecassoside (13.15 mg g−1), asiaticoside (9.02 mg g−1), madecassic acid (5.26 mg g−1), and asiatic acid (3.10 mg g−1) than conventional ethanol extraction while simultaneously reducing chlorophyll-derived coloration. This ATPS extraction strategy provides a sustainable approach for producing suitable Centella asiatica extracts for cosmeceutical and pharmaceutical applications.
To address the challenge of efficient catalytic elimination of carbon monoxide (CO) generated from spontaneous coal combustion in goaf and blasting operations in underground coal mines under low-velocity laminar flow conditions, we propose a perforated spiral-insert catalytic tube. The design inserts a spiral vane with surface micro-holes into a straight tube; the vane surface and internal pore walls are coated with a CuMnOx catalyst. A porous medium equivalent model describes the flow and catalytic reaction characteristics in the perforated region. A three-dimensional Computational Fluid Dynamics (CFD) model coupling flow, mass transfer, and surface catalytic reactions is developed. After grid independence verification, three sets of L9 orthogonal experiments systematically investigate the effects of inlet velocity, helix pitch, vane height, opening ratio, and pore diameter on CO conversion and flow resistance. Range analysis, variance analysis, and the comprehensive performance factor are used for multi-objective optimization. PEC results show that inlet velocity is the primary factor affecting both conversion and comprehensive performance, and its dominance is independent of the number of vanes. At a low velocity of 0.2 m/s, the four-vane configuration achieves a maximum conversion of 51.07%. For a balanced trade-off between conversion and flow resistance, four vanes with a high opening ratio, large pore diameter, and large helix pitch yield the best comprehensive performance. If low resistance is the primary goal, two vanes with a high opening ratio achieve a resistance of only 0.29 Pa and a per-unit-resistance conversion efficiency of 97.72 Pa−1. A further predicted optimal combination is validated by simulation, achieving a conversion of 64.96%, confirming the effectiveness of the parameter optimization. Under low-velocity conditions, the flow resistance of this design is only about 0.3–1.3 Pa, allowing passive operation using the natural negative pressure of the extraction pipeline. The design offers modular replaceability of the catalyst insert and operates without external power input beyond the natural negative pressure of the pipeline under the simulated low-velocity conditions, providing a theoretical basis and parameter optimization method for in situ CO catalytic elimination in coal mines.
Ultra-deep fractured carbonate reservoirs suffer severe fluid loss and formation damage, requiring accurate characterization of natural fracture parameters and contamination behavior. Current field evaluation methods rely solely on logging data, which cannot obtain parameters such as the number and width of fractures. Taking the Shunbei Block as an example, this study integrates laser particle size analysis, HTHP flooding experiments, and a fracture loss model incorporating dynamic mud cake growth to clarify single-fracture loss behavior for fractures of different widths. Using fracture parameters interpreted from field logging, together with loss data and simulation results, we developed a method to determine fracture parameters and characterize contamination. Experiments reveal that effective plugging becomes difficult when fracture width exceeds 138 μm. Simulations indicate that cumulative loss volume increases with fracture width. The field loss volumes of 40–6600 m3 correspond to single-fracture widths of 0.9–6.2 mm. Based on the simulated width–loss volume relationship, correction coefficients for fracture count and width for two wells were calibrated. The method was applied to 9 wells, yielding an R2 of 0.86 between predicted and measured loss volumes. The method effectively identifies fracture parameters and assesses contamination, supporting lost circulation control in fractured carbonate reservoirs.
Oil-pool fires involving liquid hydrocarbons are difficult to suppress and pose substantial economic risks. This study characterizes three widely used Class B fire suppressants through physical property measurements, cold oil-surface spreading tests, and burning oil-pool fire experiments, coupled with transient computational fluid dynamics (CFD) simulations in ANSYS Fluent. The results show that 3% aqueous film-forming foam (3% AFFF) and 3% alcohol-resistant aqueous film-forming foam (3% AFFF-AR) spread spontaneously on oil surfaces, whereas 3% fluoroprotein foam (3% FP) has a negative spreading coefficient and covers oil surfaces only through gravitational accumulation. Although 3% AFFF exhibits the fastest cold-state spreading, 3% AFFF-AR achieves the shortest fire-extinguishing time under combustion conditions because of its superior thermal stability and longer drainage time. In contrast, 3% FP shows the poorest fire-extinguishing performance. The CFD simulations capture the main temporal evolution of foam spreading and agree reasonably with experimental observations, particularly in the temporal consistency between simulated foam front propagation under the simplified elevated-temperature boundary condition and measured extinguishment times. Within the scope of the present simplified model and small-scale experiments, the results suggest that a positive spreading coefficient serves as a necessary but insufficient thermodynamic condition for effective foam spreading. Under high-temperature conditions, foam thermal stability critically influences the sustainability of spreading by resisting thermal degradation and bubble rupture, while drainage behavior and spreading kinetics modulate the coverage rate; collectively, these factors determine the overall fire suppression effectiveness under the tested conditions. These findings provide preliminary support for optimizing firefighting foam performance, pending validation under larger-scale and more fully resolved combustion scenarios.
Fly ash from municipal solid waste incineration is a hazardous waste whose safe disposal and resource recovery are obstructed by a fundamental trilemma: no single treatment simultaneously immobilizes heavy metals permanently, destroys persistent organic pollutants, and valorizes the residual matrix. Here we resolve this trilemma through a speciation-driven cascade strategy, a design principle in which chemical, thermal, and physical unit operations are precisely matched to the distinct chemical fractions and volatilities of each metal. We systematically review the speciation and occurrence of heavy metals in fly ash and propose a classification of multi-coupled technologies based on how they manipulate the acid-soluble, reducible, and residual fractions. We distill the four recurring interaction mechanisms (i.e., phase-transfer promotion, kinetics alteration, secondary-pollutant co-control, and resource–recovery balancing) that enable synergistic performance, demonstrate that the cascade framework explains why certain couplings succeed while others fail, and derive general rules for process integration. Bottlenecks in multi-variable regulation, secondary pollution, long-term stability evaluation, and engineering scale-up are dissected. We then chart a roadmap toward intelligent, low-carbon, and zero-waste valorization via selective recovery and all-component utilization. This review reframes fly ash treatment as a tunable sequence of speciation-modifying steps and provides a structured framework for hazardous-waste-to-resource conversion across diverse industrial residues.
The front-end wide-temperature-range gas-mixing flow system of a high-altitude test chamber is a key component for simulating inlet conditions over a wide temperature range, and the temperature uniformity of the mixed gas as well as the positional stability of the downstream interface directly affect the accuracy of the test boundary conditions. To address the nonuniform temperature field, thermal deformation of the main pipeline, and local thermal stress concentration arising from the combined effects of large temperature differences, long-distance piping, and high-pressure operating conditions, this study establishes a multiphysics coupling analysis framework based on ANSYS Fluent 2021 R1, CAESAR II, and ANSYS Mechanical, and systematically investigates the temperature distribution in the mixing section, thermal boundary transfer, the global thermal response of the piping system, locally refined models, and the compensating effect of expansion joints. The fluid–thermal results show that the temperature distribution of the mixed gas in the right-side section of the pipeline varies among the representative operating conditions, under which the branch mass-flow allocation, total flow rate, inlet total pressure, and inlet total temperature change simultaneously. To enable quantitative comparison, a temperature nonuniformity coefficient γ (the standard deviation of cross-sectional temperature divided by mean temperature) is introduced. For each temperature combination, Cases 1 to 3 represent operating conditions dominated by the 40 °C branch, whereas Cases 4 and 5 represent conditions dominated by the −70 °C branch in the 40 °C/−70 °C group and by the 550 °C branch in the 40 °C/550 °C group. Under the selected 40 °C/−70 °C cases, the γ values at the downstream monitoring section decrease from 8.72% in Case 1 to 3.86% in Case 3, confirming progressively smoother temperature distributions, whereas γ increases from 6.31% in Case 4 to 9.87% in Case 5, indicating stronger thermal stratification. Under the selected 40 °C/550 °C cases, γ reaches its minimum of 2.87% in Case 2, while the comparison between Case 4 (γ = 9.54%) and Case 5 (γ = 5.78%) shows that the increase in flow-rate difference in the dominant branch is associated with improved temperature uniformity. Thermo-structural coupling analysis further indicates that the main pipeline is the key component governing the overall thermal deformation of the system, that axial thermal expansion dominates under high-temperature conditions, and that local high stresses are mainly concentrated at fixed supports and in adjacent regions with abrupt geometric constraint changes. Further analysis demonstrates that expansion joints modeled using equivalent stiffness can reduce the Z-direction displacement at key locations of the main pipeline by 10.94, 16.46, 9.58, and 10.50 mm under four typical operating conditions, respectively, while shifting the critical region from the main pipeline support area to the vicinity of the compensating components. These results can provide a reference for controlling interface thermal displacement, designing the main pipeline structure, and arranging expansion joints in front-end gas-mixing systems.
Reliable estimation of liquid accumulation in gas-well tubing is important for characterizing liquid-loading conditions and supporting engineering assessment. Traditional mechanistic approaches commonly depend on an extensive set of wellbore descriptors and empirical parameters, while also requiring complicated solution procedures. This work addresses these constraints through a data-driven predictive framework that couples ensemble feature selection with ant-colony-optimized support vector regression (ACO-SVR). A majority-voting scheme was applied to 107 production-test records collected from a gas field. The scheme combined linear regression, grey relational analysis, random-forest mean decrease in impurity, the Pearson correlation coefficient, and SHAP attribution, and selected seven dominant factors from 11 candidate variables: casing pressure, tubing pressure, tubing depth, reservoir mid-depth, daily gas production, daily water production, and wellhead temperature. Ant colony optimization subsequently determined the SVR hyperparameters. Evaluation with 32 held-out well samples produced a root-mean-square error of 165.73 m, a mean absolute error of 103.26 m, a coefficient of determination of 0.94, and a mean relative error of 2.11%. Repeated five-fold cross-validation further yielded an average R2 of 0.91±0.04 and an RMSE of 181.6±24.8 m, indicating moderate variability across alternative data partitions. Relative to the untuned SVR, ACO-SVR lowered the root-mean-square error and mean absolute error by approximately 27.0% and 31.1%, respectively. Its mean relative error was also 3.66 percentage points below that of the PLATA model. The resulting framework provides accurate prediction of tubing liquid accumulation height from a small sample and offers quantitative information for liquid-loading assessment under the investigated operating conditions.
Developing portable and reliable systems for online monitoring of antibiotic degradation in water is essential for studying, optimizing, and improving their efficiency. In this work, we introduce an impedimetric sensor-based system for online monitoring of the photocatalytic degradation of levofloxacin (LVX). The sensor consists of graphite pencil leads (GPL) modified with molecularly imprinted polymers (MIP) to ensure specificity and enhance sensitivity. The sensor was evaluated in a concentration range of 0 to 40 mg/L of LVX, achieving a detection limit of 1.3 mg/L, and subsequently tested in a photocatalytic degradation process. All measurements were validated using high-performance liquid chromatography (HPLC). Based on the results, the proposed system is a promising alternative to conventional analytical methods for online monitoring of degradation processes. This approach facilitates the optimization of degradation mechanisms, minimizing resource consumption and reducing analysis and operation times, especially in resource-limited environments.
Petrophysical cutoffs and reservoir-grading criteria were investigated for shale oil in the first member of the Qingshankou Formation in the Qijia–Gulong Sag, Songliao Basin. A total of 186 shale core samples were analyzed using scanning electron microscopy, contact-angle measurements, and high-pressure mercury intrusion. The connected pore-throat system was characterized from mercury-intrusion data, and the pore-throat limits associated with hydrocarbon storage and effective flow were determined using the water-film-thickness method and permeability-contribution analysis, respectively. Mercury-intrusion-derived permeability was further calibrated against measured permeability for reservoir evaluation. The shale samples are predominantly water-wet, with contact angles mainly concentrated at 20–25°. Considering the actual formation-pressure range, the storage-related pore-throat cutoff is approximately 2.95–3.60 nm, whereas the minimum effective-flow pore-throat radius is approximately 7 nm, indicating that hydrocarbon storage and effective flow correspond to different pore-throat conditions. MICP-curve characteristics and fractal analysis further distinguish four reservoir types, showing progressively reduced pore-throat size, connectivity, and permeability from Type I to Type IV. Based on corrected permeability and average pore-throat radius, Grade I reservoirs are defined by >0.30 mD and >300 nm, Grade II by 0.09–0.30 mD and 30–300 nm, Grade III by 0.04–0.09 mD and 7–30 nm, and Grade IV by <0.04 mD and <7 nm. The results distinguish the lower pore-throat condition for hydrocarbon storage from that required for effective flow and establish a quantitative relationship between microscopic pore-throat characteristics and reservoir quality, providing practical criteria for shale-reservoir evaluation in the Qijia–Gulong Sag.
Acidic wetting–drying cycles can progressively deteriorate sandstone used in rock engineering, but the link between macroscopic mechanical degradation and three-dimensional pore-crack evolution remains unclear. The objective of this work was to clarify the deterioration mechanism of sandstone under repeated acidic wetting–drying action. Sandstone specimens were subjected to 0, 5, 10, 20, or 30 wetting–drying cycles in a H2SO4 solution with an initial pH of 2.00. Triaxial shear testing, micro-CT reconstruction, scanning electron microscopy, and a correlation analysis were combined to characterize changes in the mechanical behavior and microstructure. With an increasing cycle number, the cohesion and internal friction angle decreased by 40.85% and 11.27%, respectively, while the pore network became progressively enlarged, connected, and structurally complex. The pore fractal dimension increased from 2.18 to 2.49. Scanning electron microscopy images showed enlarged pores and cracks, looser particle contacts, and local damage along particle boundaries. Increasing the confining pressure improved the peak resistance and restricted crack development. A correlation analysis indicated that mechanical degradation was closely associated with the evolution of the pore and crack structure. These results establish a multiscale relationship between the loss of mechanical performance and microstructural deterioration, providing a basis for evaluating sandstone stability in acidic environments.
Recent studies have demonstrated that installation-induced soil disturbance, advancement ratio, and installation rate can substantially affect the subsequent axial response of helical piles. However, systematic experimental comparisons that simultaneously relate installation torque, axial thrust, and post-installation uplift capacity across multiple geometric and operational variables in clay remain limited. This study investigated the coupled effects of helix pitch, advancement ratio, penetration speed, and shaft style on the installation and uplift behavior of helical anchors in prepared clay. Laboratory model tests were conducted using grouped anchor configurations, and installation torque, installation thrust, and uplift load–depth responses were recorded. An area method was introduced to quantify accumulated installation resistance from torque–depth and thrust–depth curves. Within the tested fixed-advancement-ratio pitch series, the tested pitch–speed combinations produced comparatively moderate variations in installation resistance and uplift capacity when the helix plate diameter was fixed. The installation condition represented by the advancement ratio and corresponding penetration speed influenced penetration–rotation compatibility: lower advancement ratios increased repeated shearing and torque, whereas higher advancement ratios reduced torque but increased thrust. Increasing penetration speed markedly increased accumulated torque but reduced ultimate uplift capacity from 478 N to 315 N. The shaft-style tests showed differences in plugging, suction, and local penetration resistance, with limited variation in helical-anchor uplift capacity. These findings indicate that installation parameters and geometric configuration should be considered together when optimizing helical-anchor installation in clay. The findings provide model-scale references for selecting helical-anchor geometry and installation procedures in prepared kaolin clay under conditions comparable to those tested.
