Search Results (333)

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

The characterization of solute transport dynamics in both the injection wellbore and the aquifer is essential for parameter estimation in the divergent flow tracer test. However, many previous studies usually ignore the transport dynamics in the wellbore and represent it with simple injection modes. In this study, a new model was developed by considering the transport dynamic in the injection wellbore. A semi-analytical solution of the new model was derived and validated to better analyze the effects of the wellbore and aquifer on the anomalous transport dynamic and mass exchange in the injection wellbore–aquifer system. The results show that the injection wellbore has a significant effect on solute transport in the aquifer. As the Peclet number (${Pe}_1$) in the wellbore increases, the value of peak concentration of the breakthrough curves (BTCs) in the aquifer increases and the value of late-time tailing of the BTC decreases. Particularly, the simulated BTC by the new model reduces to the traditional model when the ${Pe}_1$ is large enough. The aquifer inversely influences the sub-diffusion in the wellbore such that an increase in porosity in the aquifer leads to a stronger sub-diffusion in the wellbore, while a decrease in dispersivity in the aquifer leads to weaker sub-diffusion in the wellbore. These findings shed light on the quantification of sub-diffusion in the wellbore–aquifer system. Full article

Understanding the spatiotemporal variability of nanoplastics (NPs) in porous media is vital for environmental risk assessment, yet quantitative in-media analysis of NP distributions during transport remains limited. To address this, we innovatively applied low-field nuclear magnetic resonance (LF-NMR) as a non-invasive approach to dynamically monitor magnetic polystyrene nanoplastic (MPSNP) transport in saturated quartz sand. By establishing the relationship between LF-NMR transverse relaxation rate [1/T_2,I − 1/T_2,0] and MPSNP concentrations, we reconstructed spatiotemporal concentration profiles via T₂ inversion. This methodology enabled systematic evaluation of the effects of ionic strength (IS), flow velocity, initial concentration, and flow direction. Three mathematical models were further applied to analyze MPSNP transport behavior. Results revealed IS as the dominant factor; increasing IS (0.001 to 1 mM) dropped mass recovery from 85.7% to 0%, the migration front no longer advanced at IS > 5 mM. Lower flow rates, higher initial concentrations, and horizontal flow also enhanced retention. The two types of two-site kinetic models provide a better fit for the features of the breakthrough curves. This novel use of LF-NMR demonstrates its robust capability to resolve spatial transport heterogeneity, underscoring that flow velocity, flow direction, and ionic strength are critical regulatory parameters that should be carefully accounted for when evaluating nanoplastic transport in porous media. Full article

The separation of synthesised ammonia from unreacted nitrogen and hydrogen is a crucial step in producing high-purity ammonia and enabling the efficient recycling of unreacted gases in the ammonia synthesis process. The separation of ammonia from nitrogen and hydrogen was studied using zeolite 13X. Experiments were performed using a custom-designed experimental apparatus developed specifically for this study. Adsorption isotherm data for ammonia, hydrogen, and nitrogen were obtained over a temperature range of 293–313 K and pressures up to 5 bar. The results show that the adsorption capacity of zeolite 13X for ammonia is significantly higher than for nitrogen and hydrogen. This indicates a substantially stronger affinity toward ammonia molecules, enabling highly selective adsorption. The experimental isotherm data were successfully fitted using the Sips model, which accurately described the adsorption behaviour of the gases and showed good agreement with the measured data. The adsorption performance of zeolite 13X was further evaluated through a series of dynamic breakthrough experiments under varying pressures and gas compositions. The results confirmed the high selectivity of zeolite 13X for ammonia, with negligible adsorption of nitrogen and hydrogen. Ammonia breakthrough time was found to increase with system pressure, reflecting enhanced adsorption capacity. These findings highlight zeolite 13X as an effective and reusable adsorbent for selective ammonia separation in multi-component gas streams, with promising potential for industrial applications. Full article

Hydrothermally treated chitosan/polyvinyl alcohol beads (H-CS/PVA) were used as filler material in a fixed-bed column for continuous Cr (VI) removal. The effects of main operational parameters, namely bed height, initial concentration and flow rate, were evaluated in the respective ranges of 2–6 cm, 20–60 mg/L and 2.5–7.5 mL/min. Maximum removal efficiency and adsorption capacity were calculated as 64.2% and 15.53 mg/g, respectively. The corresponding breakthrough curves were analyzed by Yoon–Nelson, Adams–Bohart, Thomas and BDST (Bed Depth–Service Time) models, out of which the highest consistency was achieved with the Yoon–Nelson model for all studied conditions. The adsorbent maintained strong reusability, showing minimal loss (~2.5%) in desorption efficiency across three successive regeneration cycles with 0.1 M NaOH as the eluent. SEM and SEM–EDX analyses confirmed the presence of chromium on the H-CS/PVA surface at an elemental fraction of 1.03% (w.). Furthermore, FTIR and XPS analyses verified the role of amine and hydroxyl functionalities in the complexation and adsorption of Cr (VI). Overall, a column system operated under optimal conditions (H_bed: 6 cm, C₀: 40 mg/L, and column diameter: 2.5 cm) and regenerated three times can efficiently treat 20 L of Cr (VI)-contaminated wastewater, resulting in an associated environmental impact of 0.896 kg CO₂-eq. Full article

Breakthrough pressure (BP) is a key parameter for evaluating the sealing capacity of shale caprocks, whereas direct laboratory measurements are time-consuming and costly, limiting their use in continuous regional assessment. This study develops a conventional-log-based workflow for BP prediction in the Quan-4 Member (K1q4) caprock underlying the Qingshankou shale oil interval in the Gulong Sag, Songliao Basin. Four routinely available logs—gamma ray (GR), acoustic interval transit time (AC), bulk density (DEN), and compensated neutron log (CNL)—were integrated with core-measured BP data. A GR-AC multiple-regression baseline and five machine-learning algorithms, including stochastic gradient descent (SGD), extremely randomized trees (ERT), Random Forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), were compared under a unified workflow. The training set was used for normalization, model fitting, grid search, and internal five-fold cross-validation, whereas the held-out test set and external prediction wells were kept separate for performance evaluation. The results show that BP generally increases with GR and DEN and decreases with AC and CNL, indicating that clay content, compaction, and pore connectivity jointly control the logging response of caprock sealing capacity. Among the evaluated models, AdaBoost achieved the best overall performance, with RMSE, MAE, and R² values of 1.33 MPa, 0.97 MPa, and 0.89 on the held-out test set, and 1.19 MPa, 0.92 MPa, and 0.94 in external prediction wells. Train–test diagnostics, learning curves, and SHAP analysis indicate stable performance and physically plausible feature contributions within the present dataset. The proposed workflow can therefore provide a practical supplement to laboratory BP measurements for caprock evaluation in the study area, although broader application still requires well-level independent validation and explicit prediction-uncertainty quantification. Full article

With Transformers achieving breakthrough results in natural language processing and computer vision, researchers have attempted to leverage their powerful modeling capabilities in 3D point cloud processing. However, the inherent unordered and unstructured nature of point cloud data poses significant challenges to directly applying Transformer architectures. This research proposes a novel point cloud processing method by introducing point cloud serialization and a serialization-based attention mechanism to enhance the performance of the PointNeXt model in semantic segmentation tasks. Traditional point cloud processing methods typically treat point clouds as unstructured data collections, resulting in low computational efficiency and scalability limitations. Our proposed approach breaks through the constraints of point cloud data’s unordered nature by serializing point clouds into a structured format. We employ spatial filling curves (such as Z-order and Hilbert curves) to sort point clouds, enabling efficient grouping of points into non-overlapping patches and applying more efficient attention mechanisms on these patches. Based on the serialization point cloud, we incorporate the segment attention mechanism from Point Transformer V3 (PTv3), which leverages the ordered characteristics of Serialization. By designing segment interactions (such as sequential shifting and sequential random mixing), we expand the model’s receptive field while maintaining computational efficiency. Full article

Fault-controlled reservoirs are characterized by strong heterogeneity and diverse flow types. Existing water-coning calculation methods cannot accurately describe the complex oil–water distribution within reservoirs exhibiting a distinct “core–damage zone” architecture. To address this limitation, the main goal of this study is to develop a zonal water-coning calculation framework tailored to these highly heterogeneous structures. Methodologically, the Forchheimer equation is utilized to describe the entire reservoir system, with region-specific simplifications applied based on dominant flow mechanisms: in the high-velocity core zone, the viscous term is ignored; in the low-velocity damage zone, the inertial term is neglected; and the transition zone employs the complete Forchheimer formulation. The results indicate that the water-coning curves in the core and transition zones are significantly steeper as the radial distance decreases compared to the damage zone. Specifically, in a field application at the Fuman Oilfield, the calculated theoretical critical production rate of the core zone (5.39 × 10⁻² m³/s) is three orders of magnitude higher than that of the damage zone (1.45 × 10⁻⁵ m³/s). In conclusion, this massive zonal disparity demonstrates the severe bottleneck effect of the high-permeability core under a unified wellbore pressure drawdown, theoretically validating the necessity of deploying segmented completions and targeted water-control strategies to prevent premature water breakthrough. Full article

Adsorption is a widely employed separation technique valued for its low energy requirements and its applicability to diverse processes, including air separation, water purification, chromatographic analysis, wastewater treatment, and protein immobilization on biomaterials. Industrial adsorption–desorption cycles are typically carried out in parallel packed-bed columns. The accurate design and optimization of these columns rely on experimental breakthrough curves. These curves provide essential information on adsorption capacity and mass-transfer kinetics. In this study, five modelling approaches, based on instantaneous adsorption, non-instantaneous adsorption, Fickian diffusion, and anomalous diffusion, were evaluated for their ability to predict breakthrough behaviour during the adsorption of butanol on activated carbon. The first four models were formulated using conventional partial differential equations of varying complexity, whereas the fifth model incorporated anomalous diffusion through fractional-order differential equations. The results indicate that model performance depended strongly on the adsorbent type: certain models provided superior predictions for one activated carbon, while different models were more accurate for the other. Full article

Water contamination by heavy metals remains a major global challenge, requiring efficient, sustainable, and low-cost remediation materials. Chitosan and cellulose are recognized as effective biosorbents due to their high affinity toward metal ions, biodegradability, and availability. However, their individual limitations motivate the design of composite with enhanced properties. In this study, chitosan/cellulose composite hydrogel beads crosslinked with glutaraldehyde (CHB-CF-GLA) were synthesized and evaluated for Cu(II) removal under batch and dynamic conditions. The composite was characterized by FESEM-EDS, ATR-FTIR, XRD, swelling analysis, and determination of pH_pzc to elucidate its structural and physicochemical features. Batch experiments optimized pH, initial Cu(II) concentration, and adsorption capacity, while non-linear kinetic and isotherm models described the adsorption mechanism. The adsorbent exhibited good stability and reusability over multiple cycles. Fixed-bed column studies demonstrated that increasing bed height prolonged breakthrough and exhaustion times, while higher influent concentrations and flow rates led to earlier column saturation. The experimental breakthrough curves were well described by the Thomas and Yoon–Nelson models, whereas the Adams–Bohart model showed limited applicability. COMSOL Multiphysics 3.5 simulations validated the experimental data and predicted column performance. Overall, CHB-CF-GLA is an efficient and functional adsorbent with strong potential for continuous Cu(II) removal in water treatment applications. Full article

Background and Objectives: Optimal pain control after radius fracture surgery is critical for recovery and reducing opioid exposure. While brachial plexus block (BPB) offers analgesic benefits, its additive effect alongside general anesthesia remains underexplored. Materials and Methods: We conducted a retrospective cohort study of adults undergoing open reduction and internal fixation for radius fractures under general anesthesia between July 2020 and September 2025. Patients receiving preoperative ultrasound-guided BPB were matched 1:1 to those without BPB using propensity score matching. Pain scores, hemodynamic changes, and anesthesia-to-incision time were compared using paired t-tests or Wilcoxon signed-rank tests. Perioperative opioid consumption and breakthrough morphine use were analyzed using conditional logistic regression, Kaplan–Meier survival analysis, and stratified Cox regression. Results: Among 707 eligible patients, 205 who received BPB were matched to 205 controls. In the matched cohort (n = 410), BPB was associated with lower intraoperative fentanyl use [16.1% vs. 43.4%; odds ratio (OR) = 0.23; 95% confidence interval (CI): 0.13–0.40; p < 0.001], reduced rescue analgesic use in the postanesthesia care unit (10.2% vs. 53.2%; OR = 0.06; 95% CI: 0.03–0.16; p < 0.001), and decreased total opioid use (mean morphine milligram equivalents: 2.3 ± 3.4 vs. 6.7 ± 5.6; p < 0.001). Pain scores were lower (visual analogue scale: 2.9 ± 1.4 vs. 3.9 ± 1.9; p < 0.001). Breakthrough morphine use was delayed and less frequent in the BPB group (19.0% vs. 28.3%; hazard ratio = 0.47; 95% CI: 0.29–0.78; p = 0.003). BPB attenuated hemodynamic responses (mean arterial pressure area under curve: 707.2 ± 512.5 vs. 979.3 ± 636.5; p < 0.001). Conclusions: Preoperative BPB improves perioperative analgesia, lowers opioid use, and stabilizes hemodynamics in radius fracture surgery. Full article

Understanding the effects of mixing-driven precipitation on solute transport behavior is critical for reactive transport predictions, yet its complexity, arising from the interplay of flow dynamics, solute transport, and geochemical reactions, remains a significant challenge. In particular, mineral precipitation modifies the hydraulic properties of porous media. The impact of this process on the solute transport behavior remains largely unexplored and is crucial for accurate reactive transport predictions. This study presents a controlled laboratory investigation of mixing-driven calcite precipitation (MDP) in an intermediate-scale Hele-Shaw cell, simulating a coarse-sand porous medium. The experiment allowed for direct visualization of the spatiotemporal evolution of precipitation while continuously monitoring hydraulic properties. Self-organized heterogeneities in the precipitate structure were observed, with calcite layers forming symmetric patterns aligned with the main flow, contrasting with the asymmetry predicted by a semi-analytical model under idealized conditions. Tracer tests conducted before and after precipitation demonstrated significant impacts on solute transport, including the emergence of strong anomalous transport features, such as earlier solute arrival, a distinct double peak, and pronounced tailing. These findings highlight the critical role of precipitation-induced heterogeneities in shaping transport behavior, emphasizing the need to integrate these dynamics into reactive transport models for improved predictive accuracy. Full article

Adsorption on activated carbon (AC) can be used in the wastewater sector to improve treated effluent quality. This study evaluates the performance of a biobased AC relative to that of a coal-based AC, which is more widely used industrially despite its higher environmental impact. These adsorption performances are influenced by both the complexity of the water matrix and the physicochemical characteristics of AC. For these reasons, the adsorption of two representative pharmaceuticals (carbamazepine and sulfamethoxazole) in three different matrices, deionized water (DW), synthetic water with ions (SW), and wastewater secondary effluents (SEs), by two ACs (from wood and from coal), was investigated. The effects of the matrix and competition adsorption between both pharmaceuticals were studied. Adsorption kinetics and isotherms in the three matrices and breakthrough curves in a fixed-bed study were performed. Complexification of the matrix leads to a decrease in adsorption, especially in SE, where dissolved organic matter inhibits the adsorption rate and diffusion of the pharmaceuticals. Competition between pharmaceuticals was observed in SW in batch and fixed-bed studies, and showed that CBZ adsorption is favored over SMX because of the higher affinity of CBZ. Performances of biobased AC are slightly lower, but remain promising for a sustainable treatment process. Full article

With the deep integration of the modern automotive industry and artificial intelligence technologies, connected and automated vehicles (CAVs) have emerged as a key breakthrough for improving traffic safety and operational efficiency. This study proposes a distributed multi-vehicle cooperative trajectory planning and control framework for ramp merging and diverging scenarios, integrating Deep Neural Networks (DNNs) with Model Predictive Control (MPC). The methodology consists of three key components: First, a distributed cooperative architecture based on dynamic topology is constructed to effectively reduce communication loads; second, a feature point-based Cubic Bézier Curve trajectory generation method is proposed, enabling flexible path planning with reduced reliance on high-precision maps; finally, a DNN-accelerated MPC solving strategy (NN-MPC) is designed. This strategy employs an offline-trained deep neural network to approximate the online optimization process, supplemented by a terminal Safety Check mechanism and a dynamic surrounding vehicle selection algorithm. Experimental results demonstrate that the proposed method successfully reproduces the planning capability of offline high-precision MPC in ramp merging and diverging scenarios while reducing computation time to the millisecond level. It effectively overcomes the myopic decision-making problem of traditional real-time algorithms, achieving smoother conflict resolution and higher traffic efficiency. Notably, quantitative validation confirms that this cooperative framework achieves an approximate 30% reduction in average travel delay compared to the non-cooperative baseline. This study confirms the engineering advantages of the hybrid architecture under dynamic high-density traffic flows, significantly enhancing the system’s real-time response capability while balancing the safety and riding comfort of cooperative driving. Full article

Recent laboratory experiments in an intermediate-scale Hele-Shaw cell, designed to represent a coarse sand aquifer, demonstrate that mixing-induced calcite precipitation leads to the formation of a self-organized, heterogeneous porous medium. This morphology, characterized by elongated carbonate structures and internal preferential flow channels, induces strong anomalous transport features, including early solute arrival, distinct double-peak breakthrough curves, and pronounced tailing. In this article, we investigate the link between this precipitation-induced heterogeneity and solute transport by implementing varying permeability scenarios, derived from experimental image analysis, into a transport model. Our analysis reveals that while a standard dual-permeability approach, which simply delineates the total precipitated area, captures the flow diversion responsible for the emergence of the double peak, it fails to reproduce the transition between peaks and the late-time tailing. To address this, we introduce a novel triple-permeability model that incorporates internal preferential flow channels within the high-precipitation zones. By resolving the internal structure of these zones, the triple-permeability model accurately captures the dual-peak transition and tailing behavior. These findings provide critical insights for applications such as geological carbon sequestration and enhanced oil recovery. Although determining exact internal structures in field settings is challenging, our results demonstrate that effective transport models must account for the internal heterogeneity of high-precipitation zones, rather than treating them as uniform barriers, to accurately predict the channeling effects that govern injectivity and long-term storage security. Full article