To address the unclear microscopic mechanisms of water huff-n-puff in tight reservoirs under the influence of salinity, a visual physical model for microscopic flow in fracturing fluid displacement was constructed. It was clarified that the fluid displacement mechanism during fracturing fluid displacement is jointly governed by three mechanisms: pressure differential displacement, capillary force imbibition, and ion osmosis, which breaks through the traditional understanding only considering “fracturing fluid displacement and imbibition for energy supplementation.” Through multi-method and multi-scale approaches, the microscopic mobilization mechanism of fracturing fluid displacement under the influence of salinity was elucidated, and the main controlling factors were analyzed. The results indicate that reducing the salinity of injected water can enhance oil recovery, and the recovery factor increases with reservoir permeability, shut-in time, and injection pressure. Reservoir permeability and injected water salinity have a greater impact on the performance of water huff-n-puff, followed by shut-in time and injection pressure. Although low-salinity water flooding tends to induce viscous fingering, which reduces the sweep efficiency of the injected water, it enhances capillary imbibition, effectively stripping residual oil from low-permeability matrices and dead-end pores, thereby ultimately improving oil recovery. This study provides theoretical support for enhancing recovery in tight oil reservoirs using fracturing fluid displacement.
In recent years, the cosmetic and dermo-cosmetic industries have experienced a shift driven by the principles of green chemistry and the growing consumer demand for clean-beauty platforms. Conventional personal care formulations heavily rely on synthetic glycols, petroleum-derived penetration enhancers, and heavy chemical preservatives to stabilize active ingredients and optimize topical application. However, these ingredients are increasingly scrutinized due to their associated carbon footprints and processing inefficiencies. Consequently, the development of multi-functional, bio-based, and ecologically sustainable solvents has emerged as a primary frontier in modern cosmetic engineering. For the first time, this study focused on determining the physicochemical properties and direct cosmetic application potential of Natural Deep Eutectic Solvents (NADESs) based on 1,3-propanediol (PDO) and glycerin (GLY) paired with organic acids (citric, succinic, malic, and lactic) at a 6:1 molar ratio. Unlike traditional, highly viscous eutectic mixtures, the engineered NADESs successfully optimized liquid dynamic viscosities, overcoming a major barrier for topical application. The new NADESs and, for comparison, a physical mixture of their substrates were incorporated into aqueous serums and O/W emulsions. Accelerated stability trials (40 °C/4 °C, 3 months) combined with pH monitoring revealed that while liquid serums maintained exceptional stability, the emulsion formulations were highly dependent on the specific acid structures. All formulations of cosmetics demonstrated very good radical scavenging activity (up to 89% DPPH inhibition) and microbiological purity, complying with the ISO 17516:2014 standard. Furthermore, in vivo sensory evaluations visualized via heatmaps confirmed that NADESs effectively eliminated the characteristic “sticky effect” of polyols, significantly enhancing product ease of application on skin. This study provides the first systematic evidence that these NADESs offer seamless cold-process compounding, reduced homogenization times, and simplified single-pot operations. Consequently, this work establishes a novel, clean-beauty-compliant platform for advanced dermo-cosmetic manufacturing.
Safe drilling in hydrate-bearing sediments is essential for environmentally responsible natural gas hydrate development. Complex pressure variations, fluid migration, and mechanical disturbances during drilling may increase the risks of gas influx, lost circulation, pipe sticking, and wellbore instability. To improve diagnostic robustness under heterogeneous and noisy drilling conditions while reducing dependence on large-scale manually labeled datasets, this study develops an adaptively coupled data–physics dual-driven diagnostic framework based on a self-organizing map (SOM) and a competitive classifier. Unlike a conventional one-way SOM–classifier cascade, changes in the downstream classification loss are fed back to adjust the SOM neighborhood radius, thereby coupling unsupervised feature mapping with supervised risk classification. In addition, class-conditional pressure-window and torque–drag consistency penalties are linked to the predicted class probabilities so that physical information directly participates in the optimization of applicable fluid-related and pipe-sticking risk predictions. Risk categories without an explicitly available physical residual remain primarily data-driven. Experiments on a hybrid measured–simulated dataset show that the proposed model achieves a test-set accuracy of 97.67%, outperforming representative baseline models. When 20% Gaussian noise is added, the accuracy decreases by only 4.20 percentage points. A three-layer data acquisition–edge-computing–cloud-monitoring early-warning system is implemented through MATLAB/VC integration. In a pilot field trial, a representative well-kick risk was identified 12 min earlier than by a conventional threshold-based alarm, and the missed-alarm rate decreased from 15% to 3%. The proposed method provides an engineering-oriented framework for improving drilling safety and environmental risk control during natural gas hydrate development.
For the purpose of removing pharmaceutically active chemicals (PhACs) from wastewater, this study experimentally assesses the removal efficiency of four membrane filtration technologies: microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO) using four model pharmaceuticals, including nicotine (NCT), diclofenac sodium (DCF), 4-acetamidoantipyrine (4AAA), and ranitidine hydrochloride (RNT), spiked in a controlled synthetic wastewater matrix under identical operating conditions. Furthermore, this study investigates how steric and electrostatic interactions, along with membrane physicochemical characteristics would control the separation behavior. The influence of operating pressure is also evaluated to signify its role in controlling removal efficacy. The experimental results demonstrate limited removal efficiency for MF and UF membranes (rejection below 20% and 30%, respectively, across the tested operating pressures), particularly for low-molecular-weight PhACs, which is attributed to their relatively large transport pathways that permit dissolved solutes to penetrate with minimal retention. In contrast, NF and RO membranes achieved significantly higher rejection rates of more than 75% and more than 95%, respectively (one-way ANOVA, p < 0.05), for the tested PhACs. Generally, these results have ascertained the dominance of size, electrostatic, and charge mechanisms for NF and the effective removal of PhACs with RO due to strong solute transport inhibition through the dense barrier. Furthermore, an insignificant effect of operating pressure on the performance of MF and UF is noticed if compared to a stronger influence on NF and RO, where increased pressure initially enhances removal rate but may ultimately plateau as rejection mechanisms stabilize under polarization and fouling impacts.
To satisfy the industrial standards for phosphate products, phosphate ore must undergo beneficiation for the removal of gangue minerals such as carbonate minerals, silicate minerals, and clay minerals. Reverse flotation is commonly used to remove carbonate minerals from apatite. However, reverse flotation removes carbonate gangue minerals but fails to eliminate silicate and clay minerals. The use of pre-concentration (such as dense-medium cyclones and photoelectric sorting) in phosphate ore beneficiation enables the early rejection of gangue before the ore enters the fine-grinding and complex flotation circuits, thereby providing a range of technical and economic benefits. In addition, pre-concentration holds the potential to reject silicate and clay minerals in the beneficiation of phosphate ore. The phosphate ore investigated in this study was obtained from Hubei, China. It is characterized by well-defined gangue banding, which facilitates the liberation of some gangue minerals at relatively coarse comminution sizes (−15 + 0.5 mm). Based on these characteristics, photoelectric separation and dense-medium cyclone separation were employed as pre-concentration methods prior to reverse flotation, with the aim of achieving economically viable recoveries and marketable product grades. The results indicate that the combined dense-medium cyclone separation–reverse flotation process was the most effective for this phosphate ore, producing a final concentrate with a P2O5 grade of 31.08% and a recovery of 81.91%. Comparative evaluation reveals notable differences in gangue removal efficiency among the tested processes. While both reverse flotation and the combined photoelectric separation–reverse flotation process effectively removed dolomite, the dense-medium cyclone separation–reverse flotation process demonstrated superior overall performance by enabling the simultaneous removal of both silicate and dolomite impurities.
In recent years, forest fires have occurred frequently, and extremely high temperatures can easily cause plastic deformation of buried pipelines. To clarify the temperature-stress variation law of natural gas pipelines under wildfire action, this study, based on heat transfer theory and using the finite element method, constructs a numerical model of a buried pipeline and analyzes the thermo-mechanical sequential coupling behavior of the pipe–soil system. It elucidates the influence patterns of key factors such as burial depth, outer diameter, internal fluid pressure, soil thermal conductivity, and fire duration on the temperature-stress fields of the pipe and surrounding soil and investigates pipeline deformation under fire. The results show that burial depth is the most sensitive factor: when it increases from 0.2 m to 0.8 m, the maximum pipe temperature decreases from 291.4 °C to 32.4 °C, and the maximum von Mises stress decreases from 507 MPa to 236 MPa. Furthermore, increasing pipe outer diameter and soil thermal conductivity both exacerbate pipe temperature rise and stress accumulation. Meanwhile, the longer the duration, the more pronounced the soil heat storage lag. Additionally, when internal pressure increases from 2 MPa to 8 MPa, the pipe’s maximum stress increases by up to 10.8%. The analysis results can provide a theoretical basis for identifying high-risk pipeline sections and guiding route selection and protective measure optimization for pipelines crossing forested areas.
Water treatment plants are essential urban infrastructure with direct implications for public health and everyday life. Data-driven management has received growing attention in drinking water treatment, particularly for optimizing chemical dosing to improve operational efficiency, reduce costs, and ease operator workload. AI-based prediction of coagulant dosage has therefore become an active research topic. Existing studies, however, have focused mainly on model architecture, with less attention to data validity and cost control. In practice, many plants face data-quality problems, including inconsistent dosing records under similar water-quality conditions. Conventional data cleaning may also remove large portions of the dataset, which can weaken model reliability. This study proposes an artificial intelligence (AI) modeling framework for coagulation dosing that handles anomalous data, emphasizes data quality assurance, and combines cost-oriented feedforward prediction with feedback control. A genetic algorithm-optimized backpropagation (GA-BP) neural network was first evaluated on controlled laboratory data and full-scale plant data using the same core model architecture, allowing the effects of model configuration to be separated from those of data quality. Historical plant records were subsequently cleaned through expert-guided validation, approximate time-delay alignment, and turbidity-based classification of operating conditions. Settled-water turbidity was then used as a feedback signal to dynamically adjust subsequent coagulant dosage and assess the resulting chemical savings. Changes in the input structure produced only modest improvements in full-scale prediction performance (R2 = 0.53–0.72). In contrast, data cleaning and process-based data organization markedly improved predictive performance, with R2 values increasing to 0.927–0.969. Standalone AI models achieved only moderate dosage reductions, while their integration with real-time turbidity feedback provided the best cost-control performance. The model-based control strategy reduced average coagulant consumption by 10.37%, with a maximum reduction of 21.33% at a settled-water turbidity target of 1.9 nephelometric turbidity units (NTU). Across the evaluated feedback-control scenarios, manual dosing was up to 32.83% higher than the corresponding feedback-controlled dosage. Overall, AI models can fit coagulation-dosing data and predict coagulant dosage with sufficient accuracy, but data quality assurance remains the main factor determining model performance. Effective cost control also requires real-time turbidity-based feedback regulation rather than model outputs alone.
This study presents the first characterization of peel and leaf essential oils (EOs) from Citrus junos Sieb. ex Tanaka (yuzu) cultivated in Greece, evaluating seasonal maturation effects on their chemical profiles. EOs were extracted through hydrodistillation from samples harvested between September and November (stages H1–H3) and analyzed via GC-MS, identifying 32 and 38 volatile compounds in peel and leaf EOs, respectively. High yields were recorded for both peels (1.00–1.27%, peaking in H2) and leaves (0.28–0.39%, peaking in H1), exceeding values reported for East Asian and Mediterranean cultivars extracted through conventional methods (0.09–0.27% for peels; 0.04% for leaves). Peel EO matched the traditional yuzu chemotype, dominated by limonene (62.62–67.32%) and γ-terpinene (10.95–14.24%), but lacked β-phellandrene and contained higher amounts of thymol and sesquiterpenes. Early-stage peels (H1) were characterized by linoleic acid (2.26%), which cleared in subsequent months, indicating a marker for fruit immaturity. In contrast, leaf EO displayed a distinct profile dominated by β-phellandrene (27.92–29.60%), γ-terpinene (20.43–24.06%) and p-cymenene (8.95–10.86%) alongside 2,5-dimethoxy-p-cymene (2.92–3.54%). Late-stage harvests (H2 and H3) showed a more than three-fold increase in linalool (1.35% to 4.82%), enriching the oxygenated fraction and aroma quality. These results confirm that Mediterranean-grown yuzu maintains its aromatic identity while developing a high-yield regional profile, providing practical guidance for optimizing harvest timing across flavor, fragrance, and cosmetic applications.
The global energy transition is driving power systems toward lower-carbon electricity generation, requiring sustainability assessments that consider environmental burdens beyond direct carbon emissions. This study evaluates Mexico’s electricity system using life cycle assessment, resource analysis, and energy sustainability indices. The main novelty of this study is the development of four energy sustainability indices derived from EI99H damage results: the Index of Environmental Change per Energy Unit, Relative Environmental Change Index, Per Capita Environmental Impact, and Environmental Intensity Metric. These indices capture temporal environmental change, generation-related variation, population-related burden, and environmental impact per unit of electricity. Results show improvements in fuel oil, water, and biomass performance between 2013 and 2023, whereas natural gas and coal impacts increased. Mexico exhibits a lower per capita environmental burden than Germany and Spain, while France shows the lowest value, largely due to its nuclear-based electricity mix. Human Health damage is 55% higher than Ecosystem Quality, mainly due to fossil fuel combustion. Hydroelectric generation shows substantial water demand, while solar and wind have negligible requirements. Rising natural gas costs constrain competitiveness, whereas renewables maintain low operating costs.
Avocado fruits unsuitable for consumption are often used to produce oil, prized by food and cosmetic industries. This research assessed how press-extraction temperatures (20 °C and 80 °C) and cultivars affected avocado oil color, antioxidants, fatty acids composition, physico-chemical parameters, saponification value, nutritional and health-related indices and stability. Fruits and oils from cultivars Fuerte, Gween, Hass, Topa Topa, and Zutano were evaluated, and significant differences (p ≤ 0.05) among varieties as well as between extraction temperatures were detected for most traits. Fresh avocado pulp contained 13.6–15.7 g/100 g lipids; the extraction yield was 36.5% at 20 °C and 49.8% at 80 °C. In general, 20 °C better preserved chlorophyll (61.5 mg/kg) than 80 °C (43.8 mg/kg). The carotenoid β-cryptoxanthin was lower in the samples extracted at 80 °C. Gween oil had the highest tocopherol content (on average, 229.8 mg/kg); β-tocopherol (susceptible to extraction temperature) was the most abundant (49.10%), followed by α-tocopherol (38.15%) and γ-tocopherol (12.75%). The oils extracted at 80 °C showed higher density, viscosity and acidity but inferior refractive index, peroxide and iodine than those extracted at 20 °C. The oxidative stability index at 110 °C, measured by Rancimat, decreased in oils extracted at 80 °C compared to those extracted at 20 °C (9.1 vs. 10.4 h). Pressure-extracted avocado oil maintained excellent qualitative and technological properties at both extraction temperatures, thus suggesting its suitability for several food uses, including salad dressing and, thanks to its thermal stability, cooking.
Microcapsule powders containing omega-3 fatty acid oils were prepared using gum arabic through an emulsion and spray-drying process and β-cyclodextrin through a filtration/drying process. The prepared powder samples were characterized by OM, SEM, TGA, DSC, and GC analysis. In the gum arabic formulation, the EPA and DHA contents could not be determined after the spray-drying process by GC analysis. In the β-cyclodextrin formulation, GC analysis showed that the total EPA + DHA content changed from 661.41 mg/g in the original omega-3 fatty acid oil sample to 117.44 mg/g in the microcapsule powder sample. In the time-dependent peroxide value (PV) analysis, the oil recovered from the β-cyclodextrin powder showed a slightly higher mean PV than the process-matched original oil at Day 0, but lower mean PV values at Days 7 and 14. These results describe the preparation and characterization of omega-3 fatty acid oil-loaded microcapsule powder samples under the investigated conditions.
Against the backdrop of global warming, carbon dioxide capture technologies are advancing rapidly. The chemical absorption method using organic amines as absorbents is one of the most widely used technologies in industry. Mixed organic amine absorbents show great promise for practical application; however, few studies have investigated their corrosion behavior on 304 stainless steel. This paper studied the corrosion behavior of 304 stainless steel in mixed amine solutions through immersion coupon tests, electrochemical tests, and long-term corrosion tests. The results showed that in the mixed amine solution under CO2 saturated load, the corrosion rate of 304 stainless steel increased from 0.0016 mm/a to 0.0065 mm/a (at 30 wt% amine concentration) as the temperature rose from 40 to 60 °C. With increasing amine concentration in the range of 15 to 30 wt%, the corrosion rate first increased and then decreased, reaching a maximum value of 0.0065 mm/a. As the chloride ion concentration increased from 0 to 200 mg/L, the corrosion rate increased from 0.0065 mm/a to 0.0099 mm/a, while the susceptibility to pitting corrosion remained extremely low. Calculated Ea, ΔH‡, and ΔS‡ values jointly demonstrated that the corrosion process under the experimental conditions was predominantly controlled by the interfacial electrochemical charge transfer reaction. After 72 h of immersion, the surface of 304 stainless steel remained in a stable passive state, resulting in very low corrosion rates under all tested conditions and no visible corrosion on the coupon surfaces. Long-term corrosion tests indicated that corrosion predominantly occurred during the initial immersion stage, and the material exhibited good self-passivation performance during long-term service.
Developing stable plant-based probiotic beverages requires innovative formulation strategies. While chickpea aquafaba, a protein-containing by-product, shows promise for the development of functional foods, its specific application in probiotic matrices remains underexplored. This study evaluated a coconut-based probiotic beverage supplemented with aquafaba by monitoring viable cell counts, pH, sugar and organic acid profiles, and antioxidant activity during 30 days of refrigerated storage. A 22 full factorial experimental design was used to investigate the effects of aquafaba and sucrose concentrations on the viability of Lacticaseibacillus casei NRRL B-442. The selected formulation (30% aquafaba and 50 g/L sucrose) achieved the highest viable cell count after fermentation (9.88 ± 0.09 log CFU/mL), compared with 8.40 ± 0.07 log CFU/mL in the control (coconut). During 30 days of refrigerated storage, the formulation R4 maintained probiotic viability above the recommended functional threshold (>7.0 log CFU/mL), reaching 7.40 log CFU/mL at day 30, whereas the control (coconut) declined to 6.77 log CFU/mL. R4 formulation also exhibited a slower decline in pH (4.50 vs. 3.20 in the control (coconut) at day 30) and higher lactate concentration after fermentation (2.04 vs. 0.79 g/L). Carbohydrate profiling revealed sustained sucrose utilization during storage, while the control (coconut) showed early metabolic stabilization. Monte Carlo simulation estimated an 87.9% probability for R4 and 24.8% for the coconut control of meeting the predefined viability criterion of 7.0 log CFU/mL under the modeled storage conditions. R4 beverage maintained viable cell counts above the predefined criterion of 7.0 log CFU/mL throughout storage, unlike the control (coconut). These results emphasize that integrating formulation optimization with predictive microbiology and stochastic modeling is a key component for developing robust probiotic plant-based beverages. This approach provides a complementary framework for evaluating uncertainty in predicted probiotic viability under the evaluated storage conditions.
In recent years, power grids have grown increasingly complicated, and the rising penetration of new energy sources poses prominent risks to the secure and stable operation of power systems. As a critical technology for improving grid resilience and power supply reliability, mobile operation and maintenance bases are investigated in this paper, which proposes an optimal configuration method tailored to multi-scenario emergency power guarantee requirements of power systems. Monte Carlo sampling is adopted to simulate various fault scenarios, and a multi-index resilience evaluation system consisting of load loss rate, power shortage ratio and recovery indicators is established. On this basis, a pre-positioning optimization model is formulated to minimize the space–time scheduling cost of mobile operation and maintenance bases. To tackle the model complexity, nonlinear convergence factors and dynamic adaptive weight strategies are embedded into the conventional whale optimization algorithm, which improves the global search capability and convergence stability of the algorithm. Simulation results show that the proposed method outperforms the baseline case without mobile operation maintenance bases: system load curtailment drops from 1.27 MWh to 0.65 MWh, and the overall resilience index reaches 0.831, greatly boosting distribution network power recovery performance. In addition, the improved algorithm converges within 126 iterations. Compared with standard algorithms, it improves solving efficiency by 29.2% and cuts total scheduling cost by 15.8%, achieving a good trade-off between calculation precision and convergence speed.
The coupled effect of in situ temperature and stress complicates the permeability evolution of coal reservoirs, which restricts the exploration and evaluation of deep coalbed methane (CBM). Two high-rank coal samples were collected from the Sihe (SH) and Zhaozhuang (ZZ) mining areas, and multi-gradient coupled temperature–stress seepage experiments (20–50 °C, 8–32 MPa) as well as supporting triaxial mechanical tests were carried out to investigate the temperature and stress sensitivity of coal permeability. Combined with coal mechanical deformation characteristics, the transition depth mechanism of permeability evolution with burial depth was revealed. Experimental results indicate that coal permeability follows a negative exponential decay trend with increasing effective stress, and the evolution process can be divided into three stages: rapid attenuation, slow decline and stabilization. Temperature rise can weaken the stress attenuation degree of coal permeability under continuous effective stress loading and effectively reduce the stress sensitivity of coal reservoirs. Under constant confining pressure, permeability decreases linearly with rising temperature; the temperature-induced damage effect is prominent at low effective stress, while the regulatory effect of temperature is greatly weakened when fractures are compacted under high effective stress. An exponential function between permeability and burial depth was established based on coupled temperature–stress experimental data, and the critical burial depth of permeability transition depth in the study area was determined to be 550–600 m. The abrupt change interval of elastic modulus against confining pressure is consistent with the burial depth of permeability transition depth, which acts as the key mechanical factor dominating the nonlinear transition of reservoir permeability. This study provides experimental and theoretical support for the development of deep CBM in the study area. The results represent non-adsorbing gas (nitrogen) permeability under the investigated temperature–stress window (20–50 °C, 8–32 MPa) and should not be extrapolated to methane-bearing CBM reservoirs without adsorption–swelling corrections. The transition depth of approximately 550–600 m is a laboratory-derived estimate rather than a field-verified reservoir threshold.
This study evaluates six nuclear battery conversion pathways as long-duration power sources for applications where conventional recharging, replacement, or maintenance access is limited. The methodology combines a technology and materials review, theoretical maximum-output models, reduced COMSOL Multiphysics (version, 6.2) simulations, a normalized 100 W continuous-service comparison over 50 years, service-level sensitivity, radioactive-waste utilization pathways, and operating-temperature limits. The reviewed reference systems span approximately 100 nW to 10 MWe, reflecting substantially different technology scales and intended applications. The theoretical models produced radioisotope-based specific-output estimates from approximately 3.8 We/kg for the nickel-63 cantilever case to 85 We/kg for the promethium-147 betavoltaic case, while the conceptual uranium-235 fission pathway reached 31.2–44.2 kWe/kg. In the reduced simulations, geometry and boundary conditions were maintained while the nuclear source was varied, allowing the influence of source selection on each conversion pathway to be evaluated independently. Higher-energy or higher-thermal-power sources consistently increased the predicted response. Under the 100 W reference service, conventional battery storage provided the lowest service cost, while the Radioisotope Thermoelectric Generator ranked first among the evaluated nuclear references in the combined cost–mass and cost–volume indicators. Extending the fixed service analysis across six levels from 1 µW to 10 kW showed that the preferred technology changes with required power and design objective. These results provide a power-range-based framework for selecting nuclear battery technologies and identifying their most appropriate applications.
Natural rubber (NR) block production from cup lump conventionally requires multiple mechanical processing stages, resulting in high energy consumption and complex manufacturing operations. This study presents a modern process approach by using a Rotary Disc Granulator (RDG) to integrate size reduction and cleaning into a simplified two-step processing. The modern process was evaluated in terms of energy consumption, productivity, drying performance, molecular characteristics, rheological behavior, and final product properties, with comparison to the conventional process. The modern process reduced Specific Energy Consumption (SEC) from 89.64 ± 9.52 to 62.56 ± 4.40 kWh/ton, corresponding to an energy saving of approximately 30%, while maintaining comparable productivity and meeting Standard Thai Rubber (STR 20) quality requirements. Improved drying efficiency was achieved through enhanced heat transfer associated with the more uniform pellet morphology produced by the RDG. Molecular and rheological analyses further demonstrated higher molecular weight, lower long-chain branching, and improved thermal stability, indicating reduced mechanical degradation during processing. Despite the simplified process, the cured rubber exhibited comparable curing characteristics and mechanical properties to those produced by the conventional process. These findings demonstrate that process intensification using an RDG provides a practical and sustainable strategy for improving the energy efficiency of industrial natural rubber manufacturing while preserving product quality.
Mechanical sand-control screens are core completion components for maintaining sand retention and flow conductivity in oil, gas, geothermal, hydrate, and underground gas storage wells. This review summarizes recent progress in the plugging mechanisms, diagnostic indicators, and plugging-removal technologies of mechanical sand-control screens. The reviewed studies show that screen plugging is a multi-mechanism process controlled by external sand bridging, internal fines invasion, drilling/completion fluid residues, chemical scaling, organic deposition, and their coupled cementation effects. External plugging is mainly associated with slot- or pore-entrance bridging and filter-cake compaction, whereas internal plugging is controlled by fines retention in mesh layers, prepacked gravel, or tortuous porous media. Pressure drop, permeability damage/recovery, produced-sand particle-size distribution, and microstructural characterization are key indicators for evaluating plugging severity and treatment effectiveness. Hydraulic jetting, mechanical vibration, ultrasonic treatment, acidizing, oxidizing systems, thermochemical treatment, and physical–chemical combined methods are compared in terms of mechanisms and applicability. The analysis indicates that single treatments are usually insufficient for strongly cemented multicomponent plugging; a sequential strategy of chemical weakening followed by physical stripping and flowback is more suitable for complex field conditions. Future work should focus on green and selective chemical systems, downhole diagnosis-guided treatment selection, and integrated sand-control designs combining plugging prevention, monitoring, and removal.
In remote regions with abundant hydropower resources, upstream and downstream river basin virtual power plants (RBVPPs) operated by different entities are hydraulically coupled. Independent scheduling may therefore lead to inefficient water allocation and profit losses because downstream inflow depends on upstream reservoir releases. This study proposes a multi-RBVPP scheduling strategy based on a bidirectional water compensation contract. The independent-operation outflow schedule serves as the contractual baseline, and both increases and decreases in upstream releases are compensated according to their time-dependent effects on the downstream inflow. These effects are quantified using matrices for hydraulic connectivity, flow distribution coefficients, and water travel time. In the Stackelberg framework, the downstream RBVPP determines the time-varying compensation prices and electricity sales plan, whereas the upstream RBVPP adjusts reservoir releases and generation schedules subject to its own operational constraints. Only boundary outflow information is exchanged, preserving the privacy of internal operations. Conditional value-at-risk (CVaR) and scenario-based stochastic optimization are used to address uncertainties in renewable generation and hydrological conditions. The results for wet, normal, and dry conditions show that the proposed framework increases the profits of both RBVPPs and improves the coordinated use of basin water resources.
This paper presents a comprehensive review of the applications and advances of numerical simulation in hydrothermal geothermal systems. It begins by outlining the global resource potential and basic characteristics of these systems while also identifying the limitations of conventional static resource assessment methods. Subsequently, it elaborates on how numerical simulation, based on thermal–hydraulic–mechanical–chemical (THMC) coupling theory, serves as an essential dynamic prediction tool for resource potential assessment, development optimization, long-term evolution forecasting, and environmental risk management. Furthermore, this review introduces commonly used simulation software and multi-field coupling frameworks and analyzes their specific applications through representative cases, including sedimentary basins, uplifted mountain systems, and high-temperature geothermal systems. Finally, current technical challenges are summarized, and future perspectives are discussed, highlighting the integration of big data and artificial intelligence and the development of digital twin technologies.
