Search Results (111)

Background: Online hemodiafiltration (HDF) represents the most advanced form of kidney replacement therapy, combining diffusive and convective transport to enhance the removal of uremic toxins across a wide molecular spectrum. Achieving high convective volumes is a key determinant of treatment efficacy and has been associated with improved survival. Beyond small solutes, HDF targets middle molecules and protein-bound uremic toxins (PBUTs), including β₂-microglobulin, inflammatory cytokines, and other large uremic compounds implicated in cardiovascular and systemic complications. Aims: This narrative review examines advances in dialysis membrane materials, dialyzer design, and monitoring technologies that optimize mass transfer in HDF. It focuses on the interplay between membrane permeability, hemocompatibility, and convective dose delivery, and discusses how these engineering developments translate into clinical performance. Key mechanisms: Recent progress in synthetic polymer membranes, particularly polysulfone- and polyethersulfone-based systems, and hollow-fiber manufacturing has enabled improved control of pore size distribution, hydraulic permeability, and sieving characteristics. These developments enhance the clearance of middle molecules and selected PBUTs while preserving essential proteins such as albumin. Mechanistic insights into internal filtration, protein polarization, and Donnan effects highlight the complex transport processes occurring within the dialyzer and their interaction with automated HDF systems. Expanded hemodialysis and high-volume HDF approaches further increase the removal of larger solutes but require careful management to limit albumin loss and maintain hemocompatibility. Clinical implications: Optimized membrane design, combined with advanced HDF machine algorithms, allows delivery of high convective volumes under safe and stable conditions, improving removal of β₂-microglobulin, cytokines, and other clinically relevant toxins associated with inflammation and cardiovascular risk. However, treatment must remain individualized, considering electrolyte balance, albumin preservation, and patient-specific factors such as inflammation and nutritional status. Mechanistic modeling supports understanding of transport phenomena but must be interpreted cautiously when translated into clinical practice. Conclusions: Advances in membrane science, dialyzer engineering, and monitoring technologies have strengthened the role of HDF as a precision-based renal replacement therapy. Continued innovation aimed at optimizing middle-molecule and PBUT clearance while preserving albumin and treatment stability is essential to improve patient outcomes and support the broader implementation of HDF as a mainstream dialysis modality. Full article

Early leak detection in water distribution networks is essential to minimize losses and improve operational efficiency. This systematic review analyzes 53 studies published between 2018 and 2025 that employed machine learning, deep learning, and hybrid approaches. The results show that pressure is the most widely used and most sensitive input variable for identifying hydraulic anomalies. Most datasets originate from EPANET-generated simulations, while experimental and field data are less common due to their high costs and operational complexity. Machine learning models, particularly SVMs, achieve accuracies between 94 and 100%, demonstrating stability with noisy data and low computational cost, while in deep learning, CNNs are most effective for multiclass classification and localization, typically reaching 95–99% accuracy. Hybrid approaches that combine automatic feature extraction (e.g., CNNs or autoencoders) with conventional classifiers (such as SVMs or LSSVMs) yield the best results, surpassing 97% accuracy and achieving localization errors below 0.2 m. Based on these findings, a theoretical model is proposed using a hybrid CNN + SVM approach to enhance accuracy, robustness, and adaptability in real-time monitoring systems. Full article

Concrete arch dams represent a predominant dam type in water conservancy and hydropower projects in China. The control of concrete placement progress during construction directly impacts project quality and construction efficiency. Traditional manual monitoring methods, characterized by delayed response and strong subjectivity, struggle to meet the demands of modern intelligent construction management. This study introduces machine vision technology to monitor the concrete placement process and establishes an intelligent analysis system for construction scenes based on deep learning. By comparing the performance of U-Net and DeepLabV3+ semantic segmentation models in complex construction environments, the U-Net model, achieving an IoU of 89%, was selected to identify vibrated and non-vibrated concrete areas, thereby optimizing the concrete image segmentation algorithm. A comprehensive real-time analysis method for placement progress was developed, enabling automatic ternary classification and progress calculation for key construction stages, including concrete unloading, spreading, and vibration. In a continuous placement case study of Monolith No. 3 at a project site, the model’s segmentation results showed only an 8.2% error compared with manual annotations, confirming the method’s real-time capability and reliability. The research outcomes provide robust data support for intelligent construction management and hold significant practical value for enhancing the quality and efficiency of hydraulic engineering construction. Full article

This work presents a hierarchical deep reinforcement learning (DRL) framework based on Soft Actor–Critic (SAC) for the autonomy of rock-breaking machinery in surface mining comminution processes. The proposed approach explicitly integrates mobile navigation and hydraulic manipulation as coupled subprocesses within a unified decision-making architecture, designed to operate under the unstructured and highly uncertain conditions characteristic of open-pit mining operations. The system employs a hysteresis-based switching mechanism between specialized SAC subagents, incorporating automatic entropy tuning to balance exploration and exploitation, twin critics to mitigate value overestimation, and curriculum learning to manage the progressive complexity of the task. Two coupled subsystems are considered, namely: (i) a tracked mobile machine with a differential drive, whose continuous control enables safe navigation, and (ii) a hydraulic manipulator equipped with an impact hammer, responsible for the fragmentation and dismantling of rock piles through continuous joint torque actuation. Environmental perception is modeled using processed perceptual variables obtained from point clouds generated by an overhead depth camera, complemented with state variables of the machinery. System performance is evaluated in unstructured and uncertain simulated environments using process-oriented metrics, including operational safety, task effectiveness, control smoothness, and energy consumption. The results show that the proposed framework yields robust, stable policies that achieve superior overall process performance compared to equivalent hierarchical configurations and ablation variants, thereby supporting its potential applicability to DRL-based mining automation systems. Full article

Tunnel boring machine (TBM) hydraulic cylinders operate under pronounced start–stop shocks and load uncertainties, making it difficult to simultaneously achieve smooth start-up and high-precision tracking. This paper proposes a two-phase switching adaptive sliding mode control (ASMC) strategy for TBM hydraulic actuation. Phase I targets a soft start by introducing smooth gating and a ramped start-up mechanism into the sliding surface and equivalent control, thereby suppressing pressure spikes and displacement overshoot induced by oil compressibility and load transients. Phase II targets precise tracking, combining adaptive laws with a forgetting factor design to maintain robustness while reducing chattering and steady-state error. We construct a state-space model that incorporates oil compressibility, internal/external leakage, and pump/valve dynamics, and provide a Lyapunov-based stability analysis proving bounded stability and error convergence under external disturbances. Comparative simulations under representative TBM conditions show that, relative to conventional PID Controller and single ASMC Controller, the proposed method markedly reduces start-up pressure/velocity peaks, overshoot, and settling time, while preserving tracking accuracy and robustness over wide load variations. The results indicate that the strategy can achieve the unity of smooth start and high-precision trajectory of TBM hydraulic cylinder without additional sensing configuration, offering a practical path for high-performance control of TBM hydraulic actuators in complex operating environments. Full article

Localising leaks in pressurised water distribution networks (WDNs) is crucial for reducing water loss but remains challenging because of model uncertainties and limited sensor data. Nevertheless, many state-of-the-art methods rely on idealised assumptions that are perfectly known, like time-invariant demands, noise-free pressure sensors, a single, stationary leak, and a known leak-free baseline. These assumptions rarely hold in practice, creating a gap between expected performance and field reality. This article provides a comprehensive review of current leak localisation techniques based on sensor data and hydraulic or data-driven models. This study critically examines how recent studies have addressed these unrealistic assumptions. Advanced methods incorporate demand uncertainty and sensor noise into leak detection algorithms to improve robustness, estimate unknown demand variations using physics-informed machine learning, and employ Bayesian inference to locate multiple simultaneous leaks. The analysis indicates that accounting for such real-world complexities markedly improves localisation accuracy; for instance, even minor demand estimation errors or sensor noise can dramatically degrade performance if not addressed. Finally, bridging the gap between the models and reality is essential for the practical deployment of water utilities. Thus, this review recommends that future studies integrate uncertainty quantification, adaptive modelling, and enhanced sensing into leak localisation frameworks, thereby guiding the development of more resilient and field-ready leak management solutions. Full article

In the modern industrial context, many manufacturers design universal testing machines (UTMs) equipped with servo-hydraulic or electromechanical linear actuators, which offer excellent control capabilities and high-quality force signal measurement, at the expense of high costs due to the need for hydraulic power units or dedicated electrical networks. The complexity of these systems discourages manufacturers of mechanical components, especially the ones produced through additive manufacturing (AM), from investing in machines for the determination of mechanical properties according to international standards, settling instead for information derived from technical datasheets of the base material (filament or powders), which rarely include information about fatigue life. Within this context, the Fast Fatigue Machine (FFM), designed by KnoWow srl and ItalSigma srl, makes mechanical characterization of materials a process accessible to any organization that may require it. This was made possible by designing a pneumatic benchtop testing machine with a built-in setup for Thermographic Methods (TMs) usage. The aim of this work is to validate pneumatic actuators as a viable alternative to servo-hydraulic systems, demonstrating their effectiveness and reliability. Frequency analysis on both sinusoidal waveforms, root mean square error (RMSE) evaluation, and percentage total harmonic distortion (THD%) calculations showed that, while the servo-hydraulic system closely follows the load signal with a THD of around 5%, regardless of the applied load intensity, the pneumatic system exhibits higher distortion (THD of approximately 9%, strongly dependent on the load levels) and a high-frequency harmonic component, which, however, does not affect the overall results. Life cycle assessment (LCA) analysis confirmed the convenience of the pneumatic system and TMs in material testing and fatigue characterization. Full article

Electro-hydraulic servo fatigue testing machines are susceptible to concurrent degradation and failure of multiple components during high-frequency, high-load, and long-duration cyclic operations, posing significant challenges for online health monitoring. To address this, this paper proposes a multi-state recognition method based on spatiotemporal feature fusion and bidirectional cross-attention. The method employs a Bidirectional Temporal Convolutional Network (BiTCN) to extract multi-scale local features, a Bidirectional Gated Recurrent Unit (BiGRU) to capture forward and backward temporal dependencies, and Bidirectional Cross-Attention (BiCrossAttention) to achieve fine-grained bidirectional interaction and fusion of spatial and temporal features. During training, GradNorm is introduced to dynamically balance task weights and mitigate gradient conflicts. Experimental validation was conducted using a real-world multi-sensor dataset collected from an SDZ0100 electro-hydraulic servo fatigue testing machine. The results show that on the validation set, the cooler and servo valve achieved both accuracy and F1-scores of 100%, the motor-pump unit achieved an accuracy of 98.32% and an F1-score of 97.72%, and the servo actuator achieved an accuracy of 96.39% and an F1-score of 95.83%. Compared to single-task models with the same backbone, multi-task learning improved performance by approximately 3% to 4% for the hydraulic pump and servo actuator tasks, while significantly reducing overall deployment resources. Compared to single-task baselines, multi-task learning improves performance by 3–4% while reducing deployment parameters by 75%. Ablation studies further confirmed the critical contributions of the bidirectional structure and individual components, as well as the effectiveness of GradNorm in multi-task learning for testing machines, achieving an average F1-score of 98.38%. The method also demonstrated strong robustness under varying learning rates and resampling conditions. Compared to various deep learning and fusion baseline methods, the proposed approach achieved optimal performance in most tasks. This study provides an effective technical solution for high-precision, lightweight, and robust online health monitoring of electro-hydraulic servo fatigue testing machines under complex operating conditions. Full article

This paper explores the symmetry of integrated control technology to ensure the smooth operation of shearers, scraper conveyors and hydraulic supports in the context of integrated mechanized coal mining, so as to achieve the dual goals of improving coal mining efficiency and ensuring operation safety. Article paper addresses the critical research gap in system-level coordination for mechanized coal mining. While the shearer, scraper conveyor, and hydraulic support have been extensively studied individually, their integrated control under dynamic and complex geological conditions remains a challenge, often leading to production bottlenecks and safety risks. This study proposes a novel integrated control model to bridge this gap. The study formulates the research problem of achieving continuous and safe mining operations under complex geological conditions and employs modeling and simulation to validate the proposed control methodology. In the subsequent stages, a technological solution for the control of the coal mining process is investigated, and the effectiveness of the constructed model is thoroughly tested through simulation modeling methods. The study shows that through proportional–integral (PI) control, precise interaction between coal mining machines, scraper conveyors and hydraulic supports can be achieved, thereby ensuring the continuity and safety of coal mining operations and effectively preventing production interruptions and potential accidents. The research results are analyzed, and a forecast is made for the future trend of technology development, namely, the movement toward intelligence, automation and precision, so as to further promote technological innovation and industrial upgrading in the coal mining industry. Full article

Earth-rock dams are critical components of hydraulic engineering, undertaking core functions such as flood control and disaster mitigation. However, the potential occurrence of dam breach poses a severe threat to regional socioeconomic stability and ecological security. To address the limitations of traditional Bayesian network (BN) in capturing the complex nonlinear coupling and dynamic mutual interactions among risk factors, they are integrated with machine learning techniques, based on a collected dataset of earth-rock dam breach case samples, the PC structure learning algorithm was employed to preliminarily uncover risk associations. The dataset was compiled from public databases, including the U.S. Army Corps of Engineers (USACE) and Dam Safety Management Center of the Ministry of Water Resources of China, as well as engineering reports from provincial water conservancy departments in China and Europe. Expert knowledge was integrated to optimize the network topology, thereby correcting causal relationships inconsistent with engineering mechanisms. The results indicate that the established hybrid model achieved AUC, accuracy, and F1-Score values of 0.887, 0.895, and 0.899, respectively, significantly outperforming the data-driven model G1. Forward inference identified the key drivers elevating breach risk. Conversely, backward inference revealed that overtopping was the direct failure mode with the highest probability of occurrence and the greatest contribution. The integration of data-driven approaches and domain knowledge provides theoretical and technical support for the probabilistic quantification of earth-rock dam breach and risk prevention and control decision-making. Full article

Ensuring the structural integrity of mechanical components operating in fluid environments requires precise and reliable monitoring techniques. This study presents a methodology for reconstructing the full-field deformation of a flexible aluminium plate subjected to unsteady water flow in a water tunnel, using a structural modal reconstruction approach informed by experimental data. The experimental setup involves an aluminium lamina (200 mm × 400 mm × 2.5 mm) mounted in a closed-loop water tunnel and exposed to a controlled flow with velocities up to 0.5 m/s, corresponding to Reynolds numbers on the order of ${10}^4$, inducing transient deformations captured through an image-based optical tracking technique. The core of the methodology lies in reconstructing the complete deformation field of the structure by combining a reduced number of vibration modes derived from the geometry and boundary conditions of the system. The novelty of the present work consists in the integration of the Internal Strain Potential Energy Criterion (ISPEC) for mode selection with a data-driven machine learning framework, enabling real-time identification of active modal contributions from sparse experimental measurements. This approach allows for an accurate estimation of the dynamic response while significantly reducing the required sensor data and computational effort. The experimental validation demonstrates strong agreement between reconstructed and measured deflections, with normalised errors below 15% and correlation coefficients exceeding 0.94, confirming the reliability of the reconstruction. The results confirm that, even under complex, time-varying fluid–structure interactions, it is possible to achieve accurate and robust deformation reconstruction with minimal computational cost. This integrated methodology provides a reliable and efficient basis for structural health monitoring of flexible components in hydraulic and marine environments, bridging the gap between sparse measurement data and full-field dynamic characterisation. Full article

As critical infrastructure, hydropower dams require efficient and accurate detection of underwater structural surface defects to ensure their safety. This paper presents the design and implementation of a robotic control system specifically developed for underwater dam inspection in hydropower stations, aiming to enhance the robot’s operational capability under harsh hydraulic conditions. The study includes the hardware design of the control system and the development of a surface human–machine interface unit. At the software level, a modular architecture is adopted to ensure real-time performance and reliability. The solution employs a hierarchical architecture comprising hardware sensing, real-time interaction protocols, and an adaptive controller, and the integrated algorithm combining a fixed-time disturbance observer with adaptive super-twisting controller compensates for complex hydrodynamic forces. To validate the system’s effectiveness, field tests were conducted at the Baihetan Hydropower Station. Experimental results demonstrate that the proposed control system enables stable and precise dam inspection, with standard deviations of multi-degree-of-freedom automatic control below 0.5 and hovering control below 0.1. These findings confirm the system’s feasibility and superiority in performing high-precision, high-stability inspection tasks in complex underwater environments of real hydropower dams. The developed system provides reliable technical support for intelligent underwater dam inspection and holds significant practical value for improving the safety and maintenance of major hydraulic infrastructure. Full article

Traditional hydraulic-fracturing models are restricted by low computational efficiency, insufficient field data, and complex physical mechanisms, causing evaluation delays and failing to meet practical engineering needs. To address these challenges, this study innovatively develops a dynamic hydraulic-fracturing monitoring method that integrates machine learning with numerical simulation. Firstly, this study uses GOHFER 9.5.6 software to generate 12,000 sets of fracture geometry data and constructs a big dataset for hydraulic fracturing. In order to improve the efficiency of the simulation, a macro command is used in combination with a Python 3.11 code to achieve the automation of the simulation process, thereby expanding the data samples for the surrogate model. On this basis, a parameter sensitivity analysis is carried out to identify key input parameters, such as reservoir parameters and fracturing fluid properties, that significantly affect fracture geometry. Next, a neural-network surrogate model is established, which takes fracturing geological parameters and pumping parameters as inputs and fracture geometric parameters as outputs. Data are preprocessed using the min–max normalization method. A neural-network structure with two hidden layers is chosen, and the model is trained with the Adam optimizer to improve its predictive accuracy. The experimental results show that the efficiency of automated numerical simulation for hydraulic fracturing is significantly improved. The surrogate model achieved a prediction accuracy of over 90% and a response time of less than 10 s, representing a substantial efficiency improvement compared to traditional fracturing models. Through these technical approaches, this study not only enhances the effectiveness of fracturing but also provides a new, efficient, and accurate solution for oilfield fracturing operations. Full article

The groundwater inverse problem has several challenges such as instability, non-uniqueness, and complexity, especially for heterogeneous aquifers. Solving the inverse problem is the traditional way to calibrate models, but it is both time-consuming and sensitive to errors in the measurements. This study explores the use of machine learning (ML) surrogate models, namely Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to solve the inverse problem for the groundwater model calibration. Datasets for 20 hydraulic conductivity fields were created randomly based on statistics of hydraulic conductivity from the available data of the Northern Aquifer of Qatar, which was used as a case study. The corresponding hydraulic head values were obtained using MODFLOW simulations, and the data were used to train and validate the ML models. The trained surrogate models were used to estimate the hydraulic conductivity based on field observations. The results show that both RF and XGBoost have considerable predictive skill, with RF having better R² and RMSE values (R² = 0.99 for training, 0.93 for testing) than XGBoost (R² = 0.86 for training, 0.85 for testing). The ML-based method lowered the computational effort greatly compared to the classical solution of the inverse problem (i.e., using PEST) and still produced strong and reliable spatial patterns of hydraulic conductivity. This demonstrates the potential of machine learning models for calibrating complex groundwater systems. Full article

To solve the problem of traditional engineering drilling rig propulsion systems being difficult to adapt to complex working conditions due to their bulky structure and poor load adaptability, this study proposes a new type of mechanical hydraulic composite electro-hydraulic proportional propulsion system. The system innovatively adopts a composite design of parallel hydraulic cylinders and movable pulley groups in mechanical structure, aiming to achieve system lightweighting through displacement multiplication effect. In terms of control strategy, a fuzzy adaptive PID controller based on position feedback was designed to improve the dynamic tracking performance and robustness of the system under nonlinear time-varying loads. The study established a multi physics domain mathematical model of the system and conducted joint simulation using AMESim and MATLAB/Simulink to deeply verify the overall performance of the proposed scheme. The simulation results show that the mechanical structure can stably achieve a 2:1 displacement multiplication effect, providing a feasible path for shortening the system size. Compared with traditional PID control, the proposed fuzzy adaptive PID control strategy significantly improves the positioning accuracy of the system. The maximum tracking errors of the master and slave hydraulic cylinders are reduced from 6.3 mm and 10.4 mm to 2.3 mm and 5.6 mm, respectively, and the accuracy is improved by 63.49% and 46.15%, providing theoretical support and technical reference for the design of engineering drilling rig propulsion control systems. Full article