Search Results (1,111)

Coastal wetlands are important sinks for heavy metals, yet the linkage between vertical redistribution, quantitative source contributions, and ecological–health risk drivers remain insufficiently understood. This study collected the stratified sediment samples from three depth intervals (0–20, 20–40, and 40–60 cm) from 28 representative sites in the coastal wetlands of western Bohai Bay, and evaluated the spatial distribution, vertical variation, pollution status, potential sources, and ecological–human health risks of eight heavy metals (Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb). The mean concentrations were below the Class I limits of the Marine Sediment Quality Standard. Most metals were close to or slightly below regional background values, whereas Cu and As showed mild enrichment and Cr was higher than values reported for Bohai Bay and Hangzhou Bay. Vertical profiles were generally homogeneous, with weak enrichment of specific metals, probably due to sediment resuspension, tidal disturbance, and bioturbation. Pollution assessment based on the geoaccumulation index (Igeo), Nemerow pollution index (P_N), and pollution load index (PLI) consistently indicated low contamination levels, with Hg and Pb as the main contributors to the regional pollution load. Source apportionment indicated that heavy metals were jointly influenced by lithogenic background (38.9%), atmospheric deposition (31.1%), and local anthropogenic activities (30.0%). Ecological risk assessment showed that the integrated risk index (RI) remained within the low-risk category, although Hg consistently fell within the moderate-risk range and Cd approached the moderate-risk threshold. Health risk assessment showed that both non-carcinogenic and carcinogenic risks were within acceptable limits for all receptor groups. Children had higher risks in core residential areas, whereas adults showed higher risks in non-core industrial–agricultural zones because of increased exposure frequency. The source–risk analysis indicates that non-carcinogenic risk is mainly associated with background-derived Cr, whereas carcinogenic risk is primarily linked to anthropogenic As inputs. These findings indicate that source contributions and risk contributions are not necessarily consistent, highlighting the need for source-oriented risk management in industrialized coastal wetlands. Full article

Industrial Internet of Things (IIoT) edge computing supports latency-sensitive services through task offloading to distributed edge resources. However, large-scale emergencies such as node failures and traffic surges may trigger cascading failures, leading to severe performance degradation and poor post-crisis recovery. Existing offloading methods mainly optimize operational efficiency under normal conditions while overlooking resilience against cascading disruptions. To address this issue, we propose Cascade-Emergency Resilient Offloading (CERO), an adversarial deep reinforcement learning framework for resilient task offloading in IIoT edge computing. Distinct from existing works, CERO introduces a structure-aware shared node encoder to capture heterogeneous topological roles of edge nodes, providing critical structural information for cascade-aware decision making, and incorporates cascade-oriented adversarial training to enhance robustness against compound disturbances. CERO integrates structure-aware state representation, minimax adversarial training, and potential-based reward shaping to learn resource-allocation policies balancing task efficiency and system resilience. By interacting with dynamically generated crisis scenarios, the agent learns resilient offloading policies and achieves high post-crisis recovery performance after cascading disruptions. All performance evaluations are conducted via discrete-event simulation experiments. Simulation results for normal, single-crisis, and compound-crisis scenarios show that CERO achieves comparable task efficiency under normal conditions and significantly superior post-crisis recovery performance compared to conventional rule-based strategies. In the hardest compound-crisis case involving simultaneous node failures and load surges, CERO achieves a post-recovery task-completion rate of 97.8%, surpassing the best rule-based baseline by more than 63 percentage points. Statistical significance is confirmed by the Wilcoxon signed-rank test with Bonferroni correction over 10 independent runs. These results demonstrate that CERO effectively improves the robustness and recoverability of IIoT edge-computing systems under cascading emergency scenarios. Full article

The solvent dehydration tower in Purified Terephthalic Acid (PTA) production is characterized by strong multivariable interactions, slow vapor–liquid dynamics, and high sensitivity to upstream disturbances, often limiting the effectiveness of conventional proportional–integral (PI) control. Despite increasing interest in model predictive control (MPC) for separation systems, its application to industrial-scale PTA dehydration under realistic disturbance scenarios and operational constraints remains limited. This study develops a 5 × 5 multivariable model predictive control (MMPC) strategy for an industrial PTA dehydration tower based on a validated nonlinear first-principles UniSim^® Design R500 model and a complete 25-element first-order plus dead time (FOPDT) prediction model identified from systematic dynamic tests. The proposed MMPC was evaluated against the existing industrial PI controller under four representative industrial disturbance scenarios, including feed temperature, feed flow rate, and feed composition variations in two inlet streams. The results show that the proposed MMPC reduced the Integral Absolute Error (IAE) and Integral Squared Error (ISE) by approximately 87–100%, depending on the disturbance scenario and controlled variable. The greatest improvement was obtained under feed composition disturbances, where the MMPC achieved IAE and ISE values of 117.9 and 21.5 for Stream 1, and 8.1 and 0.1 for Stream 2, respectively. The only exception was the inlet temperature disturbance, for which the existing industrial PI controller remained slightly superior because of the predominantly local thermal dynamics and relatively weak process interactions. These results demonstrate that MMPC is particularly effective for strongly coupled multivariable disturbances and provide a practical framework for implementing advanced control in industrial PTA dehydration systems using validated process models. Full article

The systemic shift in the energy consumption structure from high-carbon fossil fuels to low-carbon clean energy constitutes a critical pathway toward global climate governance and carbon neutrality. However, this sustainable transition is consistently impeded by deep-seated institutional frictions and structural barriers, such as governance fragmentation and carbon lock-in effects embedded in traditional industrial organization. Whether digital government development can overcome these barriers by nurturing resilient business ecosystems and thereby promote a systemic low-carbon energy transition remains an urgent question within sustainable development research. To address this issue, this study integrates digital government development, regional e-commerce ecosystem competitiveness, and the low-carbon transition of the energy consumption structure into a unified analytical and sustainable governance framework. Using panel data from 30 Chinese provinces from 2012 to 2022, we exploit the institutional reform of provincial big data administrations as a quasi-natural experiment to identify the impacts of digital government. Regional e-commerce ecosystem competitiveness is comprehensively evaluated across four sustainable dimensions: ecological innovation capacity, market connectivity, ecological global integration, and inclusive infrastructure. Methodologically, we employ a spatial difference-in-differences model to capture geographic interdependencies alongside a double machine learning framework to handle high-dimensional confounding and nonlinear disturbances. The empirical findings reveal that both digital government development and regional e-commerce ecosystem competitiveness significantly drive the low-carbon transition of the energy consumption structure. The institutional effect of digital government exhibits strong regional embeddedness with localized impacts, whereas e-commerce ecosystem competitiveness generates positive spatial spillovers that accelerate energy optimization in neighboring regions. Crucially, regional e-commerce ecosystem competitiveness serves as a significant partial mediator, constructing a reliable transmission channel from institutional design to market-based decarbonization. Further pathway analysis indicates that market connectivity and inclusive infrastructure function as the primary transmission channels, effectively mitigating transportation energy intensity and bridging the digital-green divide, while the mediating contribution of ecological innovation capacity is relatively constrained due to cross-organizational coordination thresholds. This study clarifies the interactive mechanism between public digital governance and market ecosystem competitiveness in advancing environmental sustainability, thereby offering fresh theoretical insights and actionable policy implications for emerging market economies striving for economic growth and decarbonization. Full article

Electricity price uncertainty directly affects the economy and reliability of scheduling, especially when flexible loads are scheduled only according to point forecasts. To improve the coupling between price forecasting uncertainty and load scheduling, this paper proposes a two-stage affine adjustable robust optimization method for flexible loads based on the confidence level of electricity price prediction via an improved hybrid convolutional neural network temporal convolutional network (CNN-TCN) model. An attention-enhanced CNN-TCN model is used to obtain day-ahead electricity price forecasts, and conformalized quantile regression (CQR) is introduced to construct calibrated asymmetric prediction intervals under different confidence levels. The interval bounds are then converted into a budgeted price uncertainty set and embedded in a two-stage affine adjustable robust optimization model for industrial, commercial, and residential loads. The model considers power limits, ramping constraints, total energy requirements, baseline deviation limits, and smoothing penalties, enabling load transfer from high-price periods to low-price periods while preserving operational feasibility. Case studies based on Spanish electricity market data show that the proposed method reduces operating costs under forecast, worst-case, and abnormal disturbance scenarios compared with the original load plan. The results also show that the 90% confidence level provides a suitable balance among cost reduction, risk coverage, and scheduling conservatism in the studied case. Full article

With intensifying mineral resource extraction, groundwater ingress in large cross-sectional intersection roadways near aquifers is becoming increasingly common. Disturbances from roadway excavation and mining may induce fractures connecting to aquifers and a series of related adverse hydrogeological effects, posing severe challenges to surrounding rock stability control of such excavations. Large-span roadway intersections in water-bearing sandstone–mudstone interbedded weak strata are frequently subjected to severe instability, bringing great challenges to the design of roadway supports. A typical large-section intersection roadway from a mine in Xinjiang is taken as the research object. Systematic research is carried out via field investigation, numerical modeling and field testing. Results show that the original aquiclude structure of sandstone–mudstone interbeds is destroyed by excavation disturbance. Mudstone strength degradation induced by sandstone pore water migration is confirmed as the core cause of surrounding rock instability. Surrounding rock deformation increases rapidly with the rise in mudstone moisture content, and obvious sudden change characteristics are presented when the water content approaches saturation. A collaborative support strategy with waterproofing as the core is proposed. Targeted drainage, high-reliability zoned support and anti-corrosion measures for support components are set as supporting measures. The proposed strategy is verified to perform well in field industrial tests. Waterproof measures, waterproof anchoring agents, anchor cable grouting and high-performance anchor-mesh-cable shotcreting support are integrated in the strategy. Surrounding rock deformation can be effectively controlled and a good application effect is achieved. The proposed support system is also applicable to roadway projects in metal and non-metal mines with similar geological conditions. Full article

Centrifugal pumps serve as key equipment in water conveyance systems. Different tee inlet structures distort the internal inflow field, induce extra hydraulic losses and reduce system energy efficiency, since pump power consumption and operational stability are highly sensitive to tee pipeline layouts. Pressure pulsation directly reflects internal flow disorder and reveals the root causes of hydraulic performance attenuation, so it is adopted as the core analytical tool in this work. Using the Shear Stress Transport (SST) k-ω turbulence model, numerical simulations are carried out on an 80 mm centrifugal pump with four inlet structures: straight pipe (SP), reducing tee (RT), reducing wye (RW), and asymmetric reducing wye (ARW). Combined with pressure pulsation signals, this study reveals the propagation rules of flow disturbances induced by tee inlet structures and their inherent energy loss mechanisms. The results show that tee inlet structures barely affect overall pump performance, except RT, which reduces the hydraulic head by 0.44 m and efficiency by 1.47%, accompanied by higher power consumption. Impeller pulsation intensity increases from the leading edge to the trailing edge, with the mid-passage leading edge being the most sensitive region. Impeller spectra are dominated by low-order shaft frequency harmonics, while volute signals are dominated by blade frequency (BF) harmonics. Different tee inlet structures have little impact on circumferential volute pulsation but significantly alter flow characteristics at the volute tongue and outlet diffuser, where 2BF becomes the primary dominant frequency. Disturbance propagation laws differ greatly between the impeller and downstream volute. Centered on low-energy pump system design, this study provides theoretical support for inlet pipeline optimization and energy-saving operation of municipal, industrial, marine and water conservancy pumping facilities. Full article

Space inertial actuators, represented by reaction wheels, momentum wheels, flywheels, and control moment gyroscopes, are indispensable angular-momentum exchange devices in spacecraft attitude determination and control systems. Owing to their non-consumable operation, high reliability, and precise torque-generation capability, these actuators are widely employed for attitude stabilization, rapid attitude maneuvering, payload disturbance mitigation, and precision pointing in modern satellites and space vehicles. With the rapid growth of high-resolution Earth observation missions, deep-space exploration programs, and large-scale commercial satellite constellations, the performance requirements imposed on inertial actuators are becoming increasingly stringent. This paper systematically reviews the current state of the art in space inertial actuator technologies by integrating peer-reviewed research with publicly available industrial product information, and summarizes recent research progress, representative products, and development strategies in the United States, Europe, Russia, and China. By comparing the technical characteristics and evolutionary pathways of mainstream reaction wheel, momentum wheel, flywheel, and control moment gyroscope systems, the study identifies the principal technological drivers shaping future development. The analysis reveals five dominant trends: extended operational lifetime, higher control accuracy, greater torque and angular-momentum density, reduced micro-vibration disturbance, and scalable, cost-effective manufacturing. These findings provide a systematic technical reference for future research, engineering design, and industrial development of advanced space inertial actuator systems in China and internationally, particularly for next-generation spacecraft requiring long service life, high pointing accuracy, low disturbance, and scalable production. Full article

Accurate state-of-charge (SOC) estimation is essential for preventing battery degradation, improving energy management, and providing reliable driving-range predictions in electric vehicles (EVs). The extended Kalman filter (EKF) is a widely adopted model-based estimation technique and remains an industry-standard approach in EV battery management systems (BMS). However, its performance can be degraded by model nonlinearities, parameter uncertainties, measurement noise, and interference bursts commonly encountered in real-world operating environments. To overcome these limitations, this paper proposes an event-triggered adaptive SOC estimation framework that integrates a recursive least squares (RLS) filter with the EKF. In the proposed approach, the RLS filter recursively updates its weighting coefficients in real time to compensate for model uncertainties and measurement disturbances, thereby generating an alternative residual signal for SOC estimation. An event-triggered mechanism dynamically selects the most reliable innovation sequence for updating the EKF state estimate, enhancing estimation robustness under adverse operating conditions. A second-order RC equivalent circuit model (ECM) is employed as the nominal battery model, and a Hybrid Pulse Power Characterization (HPPC)-based current profile is used to evaluate performance over the entire SOC operating range. Extensive simulations are conducted to assess the effectiveness of the proposed event-triggered RLS-EKF algorithm under various noise levels and interference-burst scenarios. The estimation accuracy is compared with that of the conventional EKF, cubature Kalman filter (CKF), and square root cubature Kalman filter (SR-CKF) using root mean square error (RMSE) and mean absolute error (MAE) as performance metrics. Simulation results demonstrate that, under regular noise conditions and short-term interference bursts, the proposed event-triggered RLS-EKF achieves estimation performance comparable to that of the SR-CKF while consistently outperforming the EKF and CKF in both RMSE and MAE. Under long-term interference-burst conditions, the proposed method further surpasses the SR-CKF, achieving approximately 10% improvement in overall estimation accuracy as measured by RMSE and MAE. These results confirm the effectiveness and robustness of the proposed framework, highlighting its potential for practical implementation in advanced EV battery management systems. Full article

This paper presents a high-performance sensorless control strategy for Brushless DC (BLDC) motors based on predictive current control combined with back-EMF-based speed and position estimation. The main objective is to achieve accurate speed regulation and fast dynamic response without the need for mechanical speed sensors, thereby reducing system cost, improving reliability, and simplifying hardware complexity. The proposed predictive current control algorithm ensures precise current tracking and rapid torque production under varying operating conditions, including speed reference changes and load torque disturbances. A comprehensive comparative analysis is conducted between the proposed sensorless approach and a conventional sensored control scheme. The obtained results demonstrate that the sensorless controller achieves speed, torque, and current performance that is nearly identical to the sensored system, with negligible differences in rise time, overshoot, steady-state error, and torque ripple. The dynamic response remains smooth and well-damped, confirming the effectiveness of the proposed estimation technique in maintaining accurate rotor synchronization under transient conditions. In addition, the influence of a proportional–integral speed controller with and without anti-windup compensation is investigated. The results show that the anti-windup mechanism significantly improves transient performance by reducing overshoot, eliminating startup undershoot, shortening settling time, and mitigating speed estimation errors during large reference changes and actuator saturation conditions, while preserving zero steady-state error. To validate the real-time feasibility of the proposed control strategy, a Processor-in-the-Loop (PIL) co-simulation platform is implemented using the C2000 LaunchXL-F28379D digital signal processor. The PIL results confirm that the algorithm can be executed under strict real-time constraints with acceptable computational burden, memory usage, and execution time. Overall, the proposed sensorless predictive current control strategy demonstrates strong robustness, high accuracy, and practical applicability for industrial BLDC motor drive systems operating under diverse and dynamic conditions. Full article

With the increasing penetration of renewable energy, frequency stability issues in new-type power systems have become increasingly prominent due to reduced system inertia and weakened primary frequency regulation capability. To address the insufficient frequency response capability of power systems in high-renewable regions, this paper proposes a coordinated multi-type load frequency control strategy based on controllable load damping factors. First, an improved system frequency response model considering renewable penetration is established to analyze the impacts of renewable penetration on maximum frequency deviation, rate of change of frequency (RoCoF), and quasi-steady-state frequency deviation. Subsequently, coordinated frequency control strategies are designed for feeder voltage-sensitive loads, distributed constant power loads, and energy-intensive industrial loads. Finally, an electromagnetic transient simulation model based on an actual Yunnan power grid is established to verify the effectiveness of the proposed method. The results show that increasing renewable penetration deteriorates frequency response by reducing the frequency nadir, increasing RoCoF, and enlarging quasi-steady-state frequency deviation. Under a −0.1 p.u. active power disturbance, the studied grid triggers under-frequency load shedding when renewable penetration exceeds approximately 44% without load-side frequency regulation, whereas the proposed strategy enables the system to satisfy the 49.2 Hz UFLS constraint at 70% renewable penetration, increasing the allowable renewable accommodation level by about 26 percentage points. Economic analysis further indicates that the proposed load-side control has lower regulation cost than renewable curtailment-based frequency support. Full article

The performance degradation of Permanent Magnet Synchronous Motors (PMSMs) is caused by nonlinear friction torque during low-speed operation, a phenomenon known as low-speed crawling. To address this issue, a composite compensation strategy based on a piecewise modeling approach is proposed in this paper. To accurately characterize the friction behavior, a dynamic learning rate neural network (DLRNN) is utilized for the ultra-low-speed region, while an optimal 5th-order polynomial model is established for the low-speed region. Building on this piecewise modeling, a composite control strategy with friction compensation is studied. This strategy employs a friction compensator for the feedforward compensation of the deterministic friction torque components. Furthermore, a reduced-order extended state observer (RESO) is designed to estimate and compensate for residual friction errors and random disturbance torques, thereby achieving high-precision motor control. Finally, experimental results on a surface-mounted PMSM validate that the proposed strategy effectively improves tracking accuracy and suppresses torque ripple near zero-speed, all while remaining within the computational constraints of standard industrial control hardware. Full article

Axial flow fans play a critical role in the safety and efficiency of industrial processes. This paper proposes an operation monitoring approach for axial flow fans based on physics-guided support vector regression and alarm delay timer. The physical model of the axial flow fan is established based on aerodynamics to guide variable selection and feature construction. The physically augmented features are then used to train the support vector regression model for fan pressure prediction. The model residuals serve as the monitoring indicator for operation monitoring of the axial flow fan. The alarm delay timer is introduced to reduce false alarms caused by transient disturbances. The physically augmented features enhance the interpretability and generalization of the support vector regression model. The alarm delay timer improves the operation monitoring performance for the axial flow fan. Industrial case studies illustrate the effectiveness of the proposed approach. Full article

Rapid urbanization and the urban-rural dualism are subjecting traditional villages to various slow-onset disturbances. The resilience of traditional villages (RTV) has become essential for their sustainable development. By measuring, classifying, and zoning RTV, this study aims to reveal its actual state and heterogeneous characteristics, thereby offering clear guidance for differentiated sustainable development strategies in traditional villages. From an integrated perspective of the tripartite attributes of traditional villages, this study develops an RTV assessment framework comprising three dimensions: structural persistability (SP) as vernacular heritage, functional adaptability (FA) as rural communities, and industrial transformability (IT) as tourism resources. Using hierarchical clustering, the obstacle degree model, the optimal parameters-based geographical detector, and spatially weighted hierarchical clustering, this study identifies distinct RTV types, along with their statistical distributions, key constraints, and spatial patterns. The main conclusions are as follows. (1) Most traditional villages in Jincheng exhibit low or medium-low levels of resilience. Moreover, the three dimensions of RTV are unevenly developed, with the IT dimension lagging markedly behind the others. (2) The key obstacles to enhancing RTV are the scarcity of high-value heritage resources, insufficient public services, low regional socioeconomic vitality, low public visibility, a scarcity of high-quality tourism assets, inadequate tourism support facilities, and a limited local tourism supply market. (3) Jincheng’s traditional villages cluster into four resilience-based zones, enabling a regional approach to their conservation. Full article

Over the years, industrial demands have determined the main course of electric drives research and development. Modern drive trains are forced to provide extremely efficient operation under a variety of unfavorable circumstances. Moreover, the maintenance of the drive is often a critical factor, including both its reliability in the long-term perspective and deployment costs. In addition, the sophistication of up-to-date industrial machinery increases the number of stochastic disruptions that affect the final control quality. Thus, the Control Theory satisfies the need for a novel, robust strategy by proposing the Active Disturbance Rejection Control (ADRC) algorithm. It stands out with great dynamic performance and versatility. It has been widely tested in a variety of different industrial applications, including aviation, autonomous and unmanned vehicles, marine robots, automotive solutions, renewable energy, and power systems. Many of the above-mentioned applications use electric drive units. This paper elaborates on the review of the current state-of-the-art in the field of electric drive control with the ADRC strategy employed. Then, the ADRC designs regarding multi-mass drive trains are reviewed with emphasis on the speed control issue. This paper evaluates its variants and control approaches depending on the application purpose. Moreover, an exemplary dynamic properties analysis is performed to verify the default effectiveness of the algorithm. Then, the summary section is followed by an indication of possible future research directions. Full article