Search Results (400)

Crude oil emulsions continue to pose significant challenges across production, transportation, and refining due to their inherent stability and complex interfacial chemistry. Their persistence is driven by the synergistic effects of asphaltenes, resins, acids, waxes, and fine solids, as well as operational factors such as temperature, pH, shear, and droplet size. These emulsions increase viscosity, accelerate corrosion, hinder catalytic activity, and complicate downstream processing, resulting in substantial operational, economic, and environmental impacts—underscoring the necessity of effective demulsification strategies. This review provides a comprehensive examination of emulsion behavior, beginning with their formation, classification, and stabilization mechanisms and progressing to the fundamental processes governing destabilization, including flocculation, coalescence, Ostwald ripening, creaming, and sedimentation. Separation techniques are critically assessed across chemical, thermal, mechanical, electrical, membrane-based, ultrasonic, and biological domains, with attention to their efficiency, limitations, and suitability for industrial deployment. Particular emphasis is placed on hybrid and emerging methods that integrate multiple mechanisms to improve performance while reducing environmental impact. By uniting fundamental insights with technological innovations, this work highlights current progress and identifies future directions toward greener, more efficient oil–water separation strategies tailored to diverse petroleum operations. Full article

This study addresses the issues related to the inaccurate assessment of transient voltage stability margins and the limited participation of resources involved in regulation during high-voltage direct current (HVDC) receiving-end grid faults under high-penetration new energy integration. This paper proposes a method for transient voltage emergency control at the HVDC receiving-end grid, utilizing a multi-resource approach based on transient voltage similarity partitioning with a multiple-two-element notation criterion. First, the transient voltage stability margin and the new energy transient off-grid margin index, based on the multiple-two-element notation criterion, are introduced. Second, a grid partitioning scheme is employed, which clusters nodes based on the similarity of their transient voltage features, and the impact of multiple resources on the transient voltage stability of the HVDC receiving-end system is analyzed using trajectory sensitivity. On this basis, a multi-resource optimization model for transient voltage emergencies is established with the aim of minimizing the control cost, considering the transient voltage stability, off-grid new energy, and other safety evaluation constraints, in order to coordinate multiple resources participating in transient voltage control until the stability requirements are met. Finally, the validity of the proposed control scheme is verified using the improved frequency stability benchmark test system (Chinese Society for Electrical Engineering—Frequency Stability, CSEE-FS). The research results demonstrate that the scheme proposed in this study can be utilized to accurately assess the transient voltage stability and off-grid potential of renewable energy units following failure at the HVDC receiving-end system. Additionally, it can reasonably partition the grid based on transient operating conditions while fully exploiting the potential of multiple resources within the faulted partition to control transient voltage emergencies in the grid. Full article

The ongoing energy transition and decarbonization efforts have prompted the development of Hybrid Renewable Energy Systems (HRES) capable of integrating multiple generation and storage technologies to enhance energy autonomy. Among the available options, hydrogen has emerged as a versatile energy carrier, yet most studies have focused either on stationary applications or on mobility, seldom addressing their integration withing a single framework. In particular, the potential of Metal Hydride (MH) tanks remains largely underexplored in the context of sector coupling, where the same storage unit can simultaneously sustain household demand and provide in-house refueling for light-duty fuel-cell vehicles. This study presents the design and analysis of a residential-scale HRES that combines photovoltaic generation, a PEM electrolyzer, a lithium-ion battery and MH storage intended for direct integration with a fuel-cell electric microcar. A fully dynamic numerical model was developed to evaluate system interactions and quantify the conditions under which low-pressure MH tanks can be effectively integrated into HRES, with particular attention to thermal management and seasonal variability. Two simulation campaigns were carried out to provide both component-level and system-level insights. The first focused on thermal management during hydrogen absorption in the MH tank, comparing passive and active cooling strategies. Forced convection reduced absorption time by 44% compared to natural convection, while avoiding the additional energy demand associated with thermostatic baths. The second campaign assessed seasonal operation: even under winter irradiance conditions, the system ensured continuous household supply and enabled full recharge of two MH tanks every six days, in line with the hydrogen requirements of the light vehicle daily commuting profile. Battery support further reduced grid reliance, achieving a Grid Dependency Factor as low as 28.8% and enhancing system autonomy during cold periods. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article

The integration of multiple renewable and storage units in electric vehicle (EV) hybrid energy systems presents significant challenges in stability, dynamic response, and disturbance rejection, limitations often encountered with conventional sliding mode control (SMC) and super-twisting SMC (STSMC) schemes. This paper proposes a condition-based integral terminal super-twisting sliding mode control (CBITSTSMC) strategy, with gains optimally tuned using an improved gray wolf optimization (I-GWO) algorithm, for coordinated control of a multi-source DC–DC converter system comprising photovoltaic (PV) arrays, fuel cells (FCs), lithium-ion batteries, and supercapacitors. The CBITSTSMC ensures finite-time convergence, reduces chattering, and dynamically adapts to operating conditions, thereby achieving superior performance. Compared to SMC and STSMC, the proposed controller delivers substantial reductions in steady-state error, overshoot, and undershoot, while improving rise time and settling time by up to 50%. Transient stability and disturbance rejection are significantly enhanced across all subsystems. Controller-in-the-loop (CIL) validation on a Delfino C2000 platform confirms the real-time feasibility and robustness of the approach. These results establish the CBITSTSMC as a highly effective solution for next-generation EV hybrid energy management systems, enabling precise power-sharing, improved stability, and enhanced renewable energy utilization. Full article

This paper presents an intelligent communication architecture, designed to manage multiple power devices operating within a shared Low-Voltage Direct Current (LVDC) bus. These devices act either as energy consumers, e.g., Electric Vehicle (EV) chargers, Power Distribution Units (PDUs), or as sources and regulators, e.g., Alternating Current-to-Direct Current (AC/DC) converters, energy storage system (ESS) units. Communication is established using industrial protocols such as Modular Digital Bus (MODBUS) over Transmission Control Protocol (TCP) or Remote Terminal Unit (RTU), and Controller Area Network (CAN). The proposed system supports both data acquisition and configuration of field devices. It exposes their information to an Energy Management System (EMS) via a MODBUS TCP server. A key contribution of this work is the integration of a lightweight Machine Learning (ML)-based data prioritization mechanism that dynamically adjusts the update frequency of each MODBUS parameter based on its current relevance. This ML-based method has been prototyped and evaluated within a virtualized Internet of Things (IoT) gateway environment. It enables real-time, efficient, and scalable communication without altering the EMS or disrupting legacy protocol operations. Furthermore, the proposed approach allows for early testing and validation of the prioritization strategy before full hardware integration in the demonstrators planned as part of the SHIFT2DC project under the Horizon Europe program. Full article

To address the challenge of anomaly detection in vibration signals from high-speed electric multiple unit (EMU) motor bearings, characterized by strong non-stationarity and multi-component coupling, this study proposes a synergistic approach integrating variational mode decomposition (VMD) and deep learning. Unlike datasets focused on fault diagnosis (identifying known fault types), anomaly detection identifies deviations into unknown states. The method utilizes real-world, non-real-time vibration data from ground monitoring systems to detect anomalies from early signs to significant deviations. Firstly, adaptive VMD parameter selection, guided by power spectral density (PSD), optimizes the number of modes and penalty factors to overcome mode mixing and bandwidth constraints. Secondly, a hybrid deep learning model integrates convolutional neural networks (CNNs), bidirectional long- and short-term memory (BiLSTM), and residual network (ResNet), enabling precise modal component prediction and signal reconstruction through multi-scale feature extraction and temporal modeling. Finally, the root mean square (RMS) features of prediction errors from normal operational data train a one-class support vector machine (OC-SVM), establishing a normal-state decision boundary for anomaly identification. Validation using CR400AF EMU motor bearing data demonstrates exceptional performance: under normal conditions, root mean square error $(RMSE=0.005)$, Mean Absolute Error $(MAE=0.002)$, and Coefficient of Determination $(R^2=0.999)$; for anomaly detection, accuracy = 95.2% and F1-score = 0.909, significantly outperforming traditional methods like Isolation Forest (F1-score = 0.389). This provides a reliable technical solution for intelligent operation and maintenance of EMU motor bearings in complex conditions. Full article

To reduce GHG emissions from food miles and enhance urban food security, this study develops and evaluates an integrated framework combining three strategies: the 15-minute city concept, urban agriculture, and a renewable-energy-powered green transportation (GT) system. The goal is to create a scalable, holistic approach to sustainable food production and distribution within neighborhoods. A Food Production and Transportation Framework is proposed, modeling vegetable cultivation across rooftops, facades, and lot spaces, with optimized allocations based on a tailored Food Production Schedule. The harvested produce is distributed via GT powered by sidewalk-integrated photovoltaics (PVs). Results demonstrate that using 15% of roof, facade, and lot spaces yields an achieved annual food self-sufficiency of 100%. The transportation system operates with a single GT unit powered by 98 m² of sidewalk PVs, reducing CO₂ emissions by 98% from the base case. Economic analysis indicates a payback period of 2.8 years, with the cost of PV-generated electricity estimated at C$0.92/kWh. This framework highlights that 0.19 units of local food production offset one unit of CO₂ emissions. This integrated approach advances multiple UN Sustainable Development Goals (SDGs), including SDG 2 (Zero Hunger), SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Full article

This study presents FARMBENV, a user-friendly, real-time, and web-based LCA tool developed specifically for the agricultural sector, enabling dynamic environmental impact assessments and supporting Environmental Product Declarations (EPDs). To demonstrate its functionality, three wheat production systems in Greece—differing in harvest frequency and the use of green manure through the addition of vetch—were assessed using primary data. Environmental impacts were calculated using a cradle-to-gate approach, with a functional unit of 1000 kg of wheat. Results from FARMBENV were validated with OpenLCA v2.4.1, confirming the tool’s accuracy. The addition of vetch in wheat production significantly reduced the Global Warming Potential (GWP), while the single-harvest systems applying green manure present better environmental sustainability performance. In addition, lab-scale experiments were conducted to process wheat residues via three waste-to-energy (WtE) pathways—pellet, biodiesel, and bioethanol production—and their environmental performance was assessed under multiple electricity sourcing scenarios. The source of electricity for the production systems is crucial for minimizing the impact on the GWP for the WtE pathways. The integration of WtE pathways and wheat production scenarios reduces the GWP by up to 49%. Overall, this study demonstrates FARMBENV’s capacity to deliver real-time, scenario-specific LCA results and highlights the potential of circular bioenergy strategies in sustainable agriculture. Full article

Ensuring the long-term electro-mechanical reliability of high-voltage alternating current (HVAC) insulator strings requires a detailed understanding of how multiple environmental and electrical stressors influence the corrosion behavior of hot-dip galvanized steel fittings. In this study, a three-factor, three-level L9(3³) orthogonal accelerated corrosion test was conducted to systematically evaluate the individual and interactive effects of marine salt deposition (0–10 g m⁻² day⁻¹), acetic acid pollution (0–8 µg m⁻³), and 50 Hz AC leakage current (0–10 mA) on miniature pin-type assemblies. A comprehensive post-corrosion characterization approach was employed. The results revealed that chloride loading from salt deposition was the dominant contributor to corrosion. However, the synergistic interaction between salt and leakage current led to an acceleration in zinc depletion compared to the additive effect of the individual factors. A quadratic regression model with a high correlation coefficient was developed to predict corrosion volume per unit area. The findings offer a mechanistic explanation for field-reported failures in coastal power grids and provide actionable guidance for optimizing corrosion-resistant coatings and implementing electrical mitigation strategies. Full article

Background: Today, in Western countries, patients with allo-antibodies to high-frequency antigens or with complex antibody mixtures represent one of the most significant challenges in transfusion medicine. Another important aspect is the prevention of allo-immunization of patients who lack high-frequency antigens. In these conditions, the availability of a bank of a rare red blood cell group, supported by a database of donors subjected to extensive erythrocyte typing (preferably using erythrogenomic study), can constitute a resource of great value. Materials and Methods: Repeat Caucasian blood donors of group A or O, with selected Rh phenotypes (CCDee, ccDEE, ccdee, ccDee), aged under 52 years, were considered for typing. Moreover, we selected all non-Caucasian repeat blood donors for typing. For extended phenotyping and genotyping we adopted commercial methods supplied by Grfols and Werfen, respectively. For cryopreservation, we selected a high glycerol method in −80 °C electric freezer; blood unit processing was performed using a Haemonetics ACP 215 automated cell processor with close circuit devices. Results: We considered the five patients as follows: PA was massively transfused for a road trauma, developed multiple allo-antibodies (anti-D, anti-k), and required compatible blood units for an elective cardiac surgery; PB was a pregnant woman with anti-Coa (a high frequency antigen) monitored during pregnancy and in which it was necessary to proceed with the transfusion of the newborn; PC was a poly-transfused patient with myelo dysplastic syndrome who developed multiple allo-antibodies (anti-k, anti-CW, anti-Lea) and required continuous supportive therapy, including the procurement of compatible units and the implementation of therapeutic actions in an attempt to reduce the transfusion requirement using luspatercept; PD was a patient with sickle cell disease. They had a Fy (null) genotype, making it very difficult to find compatible units; and PE was interesting for the complexity of the immunohematological and erythrogenomic study performed to characterize a recipient with a rare phenotype and thus allow the transfusion of compatible units, preventing allo-immunization. Discussion: In this report, we have maintained a narrative approach. Starting with five patients representing as many clinical situations as possible, we have illustrated the approach followed for the immune-hematological study and the choices made to try to guarantee effective and safe transfusion therapy. Full article

To address thermal management challenges in CR400BF high-speed EMU electrical cabinets—stemming from heterogeneous component integration, multi-condition dynamic thermal loads, and topological configuration variations—a dual-metric-driven finite element model calibration method is proposed using ANSYS Workbench. A multi-objective optimization function, constructed via the coefficient of determination ($R^2$) and root mean square error ($RMSE$), integrates gradient descent to inversely solve key parameters, achieving precise global–local model matching. This establishes an equivalent model library of 52 components, enabling rapid development of multi-physical-field coupling models for electrical cabinets via parameterization and modularization. The framework supports temperature field analysis, thermal fault prediction, and optimization design for multi-topology cabinets under diverse operating conditions. Validation via simulations and real-vehicle tests demonstrates an average temperature prediction error $\le 10\%$, verifying reliability. A thermal management optimization scheme is further developed, constructing a full-process technical framework spanning model calibration to control for electrical cabinet thermal design. This advances precision thermal management in rail transit systems, enhancing equipment safety and energy efficiency while providing a scalable engineering solution for high-speed train thermal design. Full article

This research considers the production of Potassium Nitrate product, a water-soluble nitrogen–potassium (NK) fertilizer containing 13.7% nitrogen and 46% potassium oxide. Potassium Nitrate (NOP) is produced as a fertilizer grade. The current system incurred high energy consumption, elevated emissions of greenhouse gases, resource degradation, and excessive production costs. Consequently, this research aims to implement the four steps of Cleaner Production (CP) to assess the environmental impacts of Potassium Nitrate products and their main manufacturing processes, and identify the best solution that achieves environmental goals. Environmental assessment was then used to calculate the unit indicators for raw materials, energy, waste generation, product, and packaging. The results showed that the integrated indicator was 5.18, with the energy profile being the most influential factor. Solar thermal and photovoltaic (PV) cell systems were suggested to reduce the high consumption of heavy fuel oil (HFO), including a solar thermal system to support the steam boilers and photovoltaic cells to support the electrical generator. The two alternatives were assessed based on multiple criteria using feasibility analysis and the Analytical Hierarchical Process (AHP). The solar thermal system, comprising 250 evacuated tube collectors, was preferable and resulted in savings of HFO by 121 tons/year, which led to a reduction in gaseous emissions by 375.6 metric tons of CO₂ and 21.685 kg of N₂O per year. Such improvements can also result in significant cost reductions. In conclusion, applying the CP methodology supported decision-makers in deciding the best system to enhance energy efficiency and reduce environmental nuisance at NOP plants. Full article

The 2 × 25 kV flexible traction power supply system (FTPSS), using a three-phase-single-phase converter as its power source, effectively addresses the challenges of neutral section transitions and power quality issues inherent in traditional power supply systems (TPSSs). However, the bidirectional fault current and low short-circuit current characteristics degrade the effectiveness of traditional TPSS protection schemes. This paper analyzes the fault characteristics of FTPSS and proposes a fault identification method based on empirical wavelet transform (EWT) and 1-sequence faulty energy. First, a composite sequence network model is developed to reveal the characteristics of three typical fault types, including ground faults and inter-line short circuits. The 1-sequence differential faulty energy is then calculated. Since the 1-sequence component is unaffected by the leakage impedance of autotransformers (ATs), the proposed method uses this feature to distinguish the TPSS faults from disturbances caused by electric multiple units (EMUs). Second, EWT is used to decompose the 1-sequence faulty energy, and relevant components are selected by permutation entropy. The fault variance derived from these components enables reliable identification of TPSS faults, effectively avoiding misjudgment caused by AT excitation inrush or harmonic disturbances from EMUs. Finally, real-time digital simulator experimental results verify the effectiveness of the proposed method. The fault identification method possesses high tolerance to transition impedance performance and does not require synchronized current measurements from both sides of the TPSS. Full article

Despite the emergence of eco-friendly solvents and scalable methods for polymeric membrane fabrication, studies on the impacts of solvent synthesis and manufacturing scale-up have not been conducted. To this end, a life cycle assessment (LCA) was developed with the goal of determining the global environmental and health impacts of producing polysulfone (PSf) membranes with the solvents PolarClean and γ-valerolactone (GVL) via doctor blade extrusion (DBE) and slot die coating (SDC). Along with PolarClean and GVL, dimethylacetamide (DMAc) and N-methyl-2-pyyrolidone (NMP) were included in the LCA as conventional solvents for comparison. The dope solution viscosity had a major influence on the material inventories; to produce a normalized membrane unit on a surface area basis, a larger quantity of PSf-PolarClean-GVL materials was required due to its high viscosity. The life cycle impact assessment found electricity and PolarClean to be major contributing parameters to multiple impact categories during membrane fabrication. The commercial synthesis route of PolarClean selected in this study required hazardous materials derived from petrochemicals, which increased its impact on membrane fabrication. Due to more materials being required to fabricate membranes via SDC to account for tool fluid priming, the PSf-PolarClean-GVL membrane fabricated via SDC exhibited the highest impacts. The amount of electricity and concentration of PolarClean were the most sensitive parameters according to Spearman’s rank coefficient analysis. A scenario analysis in which the regional energy grid was substituted found that using the Swedish grid, which comprises far more renewable technologies than the global and US energy grids, significantly lowered impacts in most categories. Despite the reported eco-friendly benefits of using PolarClean and GVL as alternatives to conventional organic solvents, the results in this study provide a wider perspective of membrane fabrication process impacts, highlighting that upstream impacts can counterbalance the beneficial properties of alternative materials. Full article