Search Results (3,363)

Arid zones, such as the MENA regions and the Sahara countries, are experiencing significant water stress. To address this global challenge, desalination technologies provide a crucial solution, particularly the reverse osmosis (RO) technique, which is widely used to treat Seawater or Brackish water. Mauritania is among the countries facing a scarcity of potable water resources and relies on desalination technologies to meet its water demand. In this work, a numerical and experimental study was carried out on the functional and productive parameters of the Nouadhibou desalination plant in Mauritania using MATLAB/Simulink (R2016a). The study considered two operating scenarios: with and without the energy recovery unit. The objective of this paper is to perform an analytical study of the operating procedures of the Nouadhibou RO desalination plant by varying several parameters, such as the pressure exchanger, and the feed water mixing ratio in the pressure exchanger unit, etc., in order to determine the system’s optimal operating point. This paper analyzes the system’s performance under different conditions, including recovery rate, feed water temperature, and PEX splitter ratio. In Case No. 1 (without a pressure recovery unit), and with a recovery rate of 20%, doubling the plant’s productivity from 400 to 800 m³/d requires 400 kW of power. In contrast, in Case No. 2 (with a pressure recovery unit), achieving the same productivity requires only 100 kW, with a 75% of energy saving. When the desalination plant operates at a productivity of 400 m³/d@40%, the SPC decreases from 6 kWh/m³ (Case No. 1) to 2.7 kWh/m³ (Case No. 2), resulting in a 55% specific power consumption saving. The results also indicate that power consumption increases with both feed water temperature and PEX splitter ratio, while variations in these parameters have a negligible effect on permeate salinity. Full article

Improving ship energy efficiency has become a critical priority for reducing fuel consumption and meeting international decarbonization targets. In this study, eight major groups of energy efficiency improvement systems—including wind and solar energy technologies, hull and propeller modifications, air lubrication, green propulsion options, waste heat recovery, and engine power limitation—were evaluated against seven critical success factors. A hybrid neutrosophic fuzzy multi-criteria decision-making (MCDM) framework was employed to capture expert uncertainty and prioritize alternatives. Neutrosophic fuzzy sets were adopted because they more comprehensively represent uncertainty—simultaneously modeling truth, indeterminacy, and falsity, providing superior capability to address expert ambiguity compared with classical fuzzy, intuitionistic fuzzy, gray, or other uncertainty-handling frameworks. Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process (AHP) (TNF-AHP) was first applied to determine the relative importance of the criteria, highlighting fuel savings and cost-effectiveness as dominant factors with 38% weight. Subsequently, the Fuzzy Combined Compromise Solution (F-CoCoSo) method was used to rank the alternatives. Results indicate that solar energy systems and wind-assisted propulsion consistently rank highest (with 3.35 and 2.92 performance scores) across different scenarios, followed by green propulsion technologies, while waste heat recovery and engine power limitation show lower performance. These findings not only provide a structured assessment of current technological options, but also offer actionable guidance for shipowners, operators, and policymakers seeking to prioritize investments in sustainable maritime operations. Full article

In this paper, an event-triggered optimal control strategy is proposed for three-phase grid-connected voltage source inverters (VSIs) based on the voltage-modulated direct power control (VM-DPC) principle. The optimization control problem of VSIs is addressed in the framework of nonzero sum (NZS) games to ensure mutual cooperation between active power and reactive power. To achieve optimal performance, the power components are driven to track their desired references while minimizing the individual performance index function. Accurate tracking of active and reactive powers not only stabilizes the grid but also guarantees compliant renewable integration. An adaptive dynamic programming (ADP) approach is adopted, where the critic neural network (NN) approximates the value function and provides optimal control policies. Moreover, an event-triggered mechanism with a dead-zone operation is incorporated to reduce redundant updates, thereby saving computational and communication resources. The stability of the closed-loop system and a strictly positive minimum inter-event interval are guaranteed. Simulation results verify that the proposed method achieves accurate power tracking, improved dynamic performance, and efficient implementation. Full article

This study selects typical existing office buildings in Zhengzhou, a region with a cold climate, as the research object and conducts a comparative analysis of the influencing factors of cooling energy consumption in west-facing and south-facing office spaces. A multi-stage sensitivity analysis methodology integrating global and local sensitivity methods is systematically applied to evaluate 13 key parameters across four categories: building morphology, envelope structure, shading measures, and active design strategies. Five parameters are consistently ranked among the top seven most sensitive parameters for both west- and south-facing orientations: the infiltration rate, the window-to-wall ratio, the cooling setpoint temperature, the number of shading boards, and building width. Two parameters exhibit orientation-specific differences, namely lighting power density and the external wall heat transfer coefficient in west-facing spaces, whereas shading board width and the external window heat transfer coefficient play a greater role in south-facing spaces. Local sensitivity analysis further reveals that within the parameter variation range, the five parameters with higher energy-saving rates for both orientations are air tightness, the window-to-wall ratio, the cooling setpoint temperature, the number of horizontal shading boards, and horizontal shading board width. By increasing the cooling setpoint temperature, south-facing spaces can achieve an energy-saving rate of 25.32%, which is significantly higher than the 21.77% achieved by west-facing spaces. Horizontal shading board width exhibits the most pronounced orientation difference, with south-facing spaces achieving an energy-saving rate of 16.69%, while west-facing spaces only reach 2.97%. The research findings offer quantitative scientific evidence for formulating targeted energy-saving retrofit strategies for existing office buildings in cold climate regions, thereby contributing to the meticulous development of building energy efficiency technologies. Full article

This paper presents an energy-aware Artificial Intelligence of Things framework designed for intelligent remote device control in residential settings. The system architecture is grounded in the Power Administration Device (PAD), a cost-effective and non-intrusive smart plug prototype that measures real-time electricity consumption and actuates appliance power states. The PAD transmits data to a scalable, cross-platform cloud infrastructure, which powers a web-based interface for monitoring, configuration, and multi-device control. Central to this framework is Cross-Feature Time-MoE, a novel neural forecasting model that processes the ingested data to predict consumption patterns. Integrating a Transformer Decoder with a Top-K Mixture-of-Experts (MoE) layer for temporal reasoning and a Bilinear Interaction Layer for capturing complex cross-time and cross-feature dependencies, the model generates accurate multi-horizon energy forecasts. These predictions drive actionable recommendations for device shut-off times, facilitating automated energy efficiency. Simulation results indicate that this system yields substantial reductions in energy consumption, particularly for high-wattage appliances, providing a user-friendly, scalable solution for household cost savings and environmental sustainability. Full article

Building energy consumption accounts for a significant portion of total society energy use, and photovoltaic technology is being rapidly deployed across the construction sector. In order to improve the efficiency with which photovoltaic shading devices capture solar energy, a numerical calculation model for the ideal tilt angle of these devices is constructed in this study. This model is based on clear-sky solar radiation calculation algorithms and solar radiation resources across different latitudes. In order to maximize solar radiation collection, an ideal control strategy for photovoltaic shading devices on buildings with varied orientations at different latitudes and in different months is derived through numerical simulations. The findings demonstrate that the building’s orientation has a significant role in determining how well photovoltaic shading systems use solar energy. In winter, the ideal tilt angle for south-facing facades increases by 10° for every 10° increase in latitude. And for every 25° rise in latitude, the ideal tilt angle increases by only around 10° in summer. By applying optimal regulatory strategies, solar radiation consumption efficiency of roughly 65% can be attained, providing a reference basis for boosting power generating efficiency and building energy saving. Full article

Open-winding permanent-magnet synchronous motors feature flexible control and a high fault-tolerance capability, making them widely used in high-reliability and high-power scenarios such as military equipment and electric locomotives. To address the issues that traditional model predictive control fails to balance, such as zero-sequence current suppression, system loss optimization and the reliance of weight parameter design on experience (with online optimization consuming excessive resources), this paper proposes an OW-PMSM MPC strategy for loss optimization and a weight design method based on a residual neural network. Specifically, the former strategy adds a zero-sequence current suppression term and a loss quantification term to the MPC cost function, enabling coordinated control of the two objectives; the latter establishes a mapping between weight parameters and motor performance via ResNet (which avoids the gradient vanishing problem in deep networks) and outputs optimal weight parameters offline to save online computing resources. Comparative experiments under two operating conditions show that the improved MPC strategy reduces system loss by 25%, while the ResNet-based weight design improves the performance of the drive system by 30%, fully verifying the effectiveness of the proposed methods. Full article

The stability of unmanned aerial vehicles (UAVs) during propulsion failure remains a critical safety challenge. This study presents a center-of-mass (CoM) correction device, a compact, under-slung, and dual-axis prismatic stage, which can reposition a dedicated shifting mass within the UAV frame to generate stabilizing gravitational torques by the closed-loop feedback from the inertial measurement unit (IMU). Two major experiments were conducted to evaluate the feasibility of the system. In a controlled roll test with varying payloads, the device produced a corrective torque up to 1.2375 N·m, reducing maximum roll deviations from nearly 90° without the device to less than 5° with it. In a dynamic free-fall simulation, the baseline UAV exhibited rapid tumbling and inverted impacts, whereas with the CoM system activated, the UAV maintained a near-level attitude to achieve the upright recovery and greatly reduced structural stress prior to ground contact. The CoM device, as a fail-safe stabilizer, can also enhance maneuverability by increasing control authority, enable a faster speed response and more efficient in-air braking without reliance on the rotor thrust, and achieve comprehensive energy saving, at about 7% of the total power budget. In summary, the roll stabilization and free-fall results show that the CoM device can work as a practical pathway toward the safer, more agile, and energy-efficient UAV platforms for civil, industrial, and defense applications. Full article

Transportation is the highest emitting sector in the US, and electrifying transportation is an effective way to reduce emissions. However, electrification efforts have typically focused on battery electric vehicles (BEVs); but extended-range EVs (EREVs), EVs with a backup gasoline generator, could play a major role. Nonetheless, reducing transportation-related costs and carbon emissions hinges on understanding how an EREV’s range and charging profile affect electric miles driven and, by extension, emission savings. This study evaluates the distribution of vehicle miles traveled (VMT) between electric and gasoline modes for EREVs across electric range (25–150 miles) and charging frequency scenarios. Using 2023 U.S. trip data by distance and monthly VMT benchmarks, we apply a dynamic mean-distance estimation method to match observed totals and allocate VMT to EV or gasoline power based on trip length. We explore different charging, efficiency, and cost scenarios. Our results show, at current average efficiencies, that EREVs with a 50-mile range (13.7 kWh battery) could electrify 73.3% of national VMT, while 150-mile range EVs could electrify 86.8% illustrating that there are diminishing returns at higher ranges. We also compute corresponding carbon emissions savings using national fuel economy and emissions factors. Results highlight the nonlinear trade-offs between range and emissions reduction. Findings suggest that expanding the EREV range significantly boosts electrification potential up to 100 miles but offers marginal gains beyond. However, if users charge infrequently, larger range EVs are needed to maintain the benefits of vehicle electrification. Our results imply that policymakers and manufacturers should prioritize moderate range EREVs for households who frequently charge (e.g., homeowners) and long range BEVs for infrequent users (e.g., apartment dwellers). Full article

This article evaluates and develops a thermodynamic steady-state model, analyzing the thermodynamic properties of ammonia–ionic liquid (NH₃–IL) working pairs for use in high-temperature (>100 °C) absorption heat pumps. Given the increasing need for energy savings and reductions in greenhouse gas emissions, this is becoming an important consideration in the context of industrial facilities. Prior work on ammonia–ionic liquid (IL) pairs has largely focused on lower supply temperatures and offers no quantitative criteria connecting IL properties to high-temperature (>100 °C) cycle design. This article presents calculations based on correlations in the literature to determine the vapor pressures of pure ionic liquids using a modified Redlich–Kwong equation of state; the vapor–liquid equilibrium (VLE) of NH₃/[emim][SCN] and NH₃/H₂O mixtures in the NRTL model; the specific heats of pure ionic liquids (ILs); the specific heat capacities of NH₃–IL and NH₃–H₂O mixtures; and the excess enthalpy (HE) for NH₃/[emim][SCN] and NH₃/[emim][EtSO₄] as a function of temperature and composition, using a combination of NRTL + Gibbs–Helmholtz and Redlich–Kister polynomials. The calculations confirm the practically zero volatility of ionic liquids in the generator. This preserves the high purity of the ammonia vapor above the NH₃/[emim][SCN] solution (y₁ ≥ 0.997 over a wide range of temperatures and concentrations) and enables the rectification process in the generator to be omitted. The specific heat capacity of pure ionic liquids (ILs) has been shown to be 52–63% lower than that of water. Mixtures of ammonia (NH₃) and ILs with a mass fraction of 0.5/0.5 have a specific heat at 120 °C that is 34–37.5% lower than that of the ammonia–water (NH₃–H₂O) solution. This directly translates into a reduction in the power required in the generator. Excess enthalpy results show moderate or strongly negative values within the useful temperature and concentration range, indicating the exothermic nature of the mixture. At the same time, the NH₃/[emim][EtSO₄] mixture is characterized by a decrease in enthalpy with increasing temperature, suggesting that benefits for the COP of the system can be obtained. Based on these calculations, criteria for selecting ionic liquids for use in high-temperature absorption pumps were formulated: negligible volatility, a low specific heat capacity for the mixture, and a strongly negative excess enthalpy, which decreases with temperature, at the operating temperatures of the absorber and generator. Full article

Load Frequency Control (LFC) is essential for ensuring frequency stability in modern power systems subject to load fluctuations, uncertainties, and increasing renewable penetration. This paper introduces a novel hybrid control framework that unifies H∞ stability guarantees, H₂ performance, and pole placement for transient shaping. Its originality is threefold. First, it models load variation as a measurable disturbance (D₁₂ = 0, D₂₁ ≠ 0), departing from the standard assumption of an unknown input. This enables a low-order H∞ controller that improves transient response, enhances robustness, and reduces energy consumption. Second, the framework explicitly accounts for a wider spectrum of real-world uncertainties, including governor and turbine dynamics and the transmission-line synchronizing power coefficient. Third, it integrates explicit energy optimization to reduce mechanical stress and extend equipment lifespan. This strategy yields substantial energy savings by minimizing fuel use and operational costs. Simulation results confirm its superiority: the proposed H∞/H₂ pole placement controller with measured disturbances achieves a 98% reduction in control energy relative to a standard H∞ controller, along with a 70% reduction in overshoot and a drastic improvement in settling time—from 7 s to 0.2 s—compared to a conventional H∞/H₂ controller. These results establish the proposed framework as a new benchmark for robust, efficient, and high-performance LFC design. Full article

The tracking of assets or cargo is one of the main objectives of global logistics and transportation systems, ensuring operational efficiency, security, and timeliness. Currently, battery-operated GPS (Global Positioning System)-based tracking devices are used for this purpose. The main shortcoming of these devices is the lifetime of the batteries because they cannot be replaced or recharged, or because this is simply not economically feasible. Therefore, efficient methods are needed to prolong battery life as much as possible. Various existing energy-saving techniques can be applied to solve this problem. However, none of these consider situations in which multiple tracking devices are transported together and can cooperate to further increase their energy efficiency. In this study, we propose and evaluate the novel lightweight peer-to-peer energy-saving method for nearby wireless battery-powered trackers based on their cooperation. The proposed method is based on the short-range BLE (Bluetooth Low Energy) device discovery mechanism and the dynamic election of the leader tracker (with the highest battery capacity) to report the location of its own and other neighboring trackers to the central server. The experimental evaluation of the proposed method shows that, compared to the traditional approach, where each tracker sends its location individually, the proposed method allows a reduction in the average battery charge required for one position report from 19% to 240% per each cooperating tracker. The average energy consumption for one location report per node decreased from 4.68 mWh using the traditional approach to 3.93 mWh for 2 cooperating devices and 1.92 mWh for 15 cooperating devices. Full article

As data product transactions become increasingly standardized, the operational strategies of data product manufacturers and service providers play a pivotal role in shaping market outcomes. This study develops a game-theoretic framework that incorporates dynamic data updates under alternative power structures to examine the equilibrium performance of pricing, demand, technological investment, update rates, and promotional effort. The results indicate that optimal prices under Stackelberg leadership exceed those in the Nash game, whereas demand, technological investment, update frequency, and promotion are consistently higher in the Nash setting. The effects of these decisions are moderated by end-user preference heterogeneity: when users exhibit stronger promotion preferences, service-provider leadership generates superior outcomes, while stronger quality preferences favor manufacturer leadership. Demand preferences and cost coefficients significantly influence profitability—enhanced preferences improve the leader’s returns, whereas high technological and promotional costs suppress profits for both parties. Cost savings in dynamic updates and increases in perceived value exert strong positive effects on market competitiveness, while higher update investment and data acquisition costs exert negative effects. Overall, this study deepens the theoretical understanding of how power structures interact with dynamic updating and user preferences, providing analytical insights and decision support for optimizing operational strategies in data product markets. Full article

High-yielding lactating cows generate considerable internal heat, making thermoregulation challenging in warm conditions. Traditionally, sprinkler systems have cooled dairy cows by spraying water droplets onto their skin to aid heat dissipation, especially when used with fans. This study explores the benefits of AI-assisted monitoring of water usage for cooling dairy cows, aiming to optimize water consumption and enhance sustainability. An object detection model, trained with 200 random images from a fisheye security camera installed above pens of dairy cows in a dairy farm, was used to detect the presence or absence of cows in headgate sections to guide water sprinkler activity. According to the object detection model, the implementation of AI-assisted detection of cows’ presence or absence in headgates with an accuracy of 0.924 has the potential to save up to 75 percent of water annually for cooling cows. Additionally, the model can detect cows’ behavior patterns regarding location in the pens depending on the occurrence of heat stress. The implementation of AI-powered detection systems in dairy farms has been proven to enhance sustainability and significantly reduce expenses by curbing the excessive use of water. Full article

Driven by 5G/6G and the Internet of Things (IoT), wireless sensor networks (WSNs) are confronted with core challenges such as limited energy constraints, unbalanced resource allocation, and security vulnerabilities. To address these, WSNs are integrated with cognitive radio networks (CRNs) to alleviate spectrum scarcity, and reconfigurable intelligent surfaces (RIS) are adopted to enhance performance, but traditional passive RIS suffers from “double fading” (signal path loss from transmitter to RIS and RIS to receiver), which undermines WSNs’ energy efficiency and the physical layer security (PLS) (e.g., secrecy rate, SR) of primary users (PUs) in CRNs. This study leverages symmetry to develop an active RIS framework for WSN-integrated CRNs, constructing a tripartite collaborative model where symmetric beamforming and resource allocation improve WSN connectivity, reduce energy consumption, and strengthen PLS. Specifically, three symmetry types—resource allocation symmetry, beamforming structure symmetry, and RIS reflection matrix symmetry—are formalized mathematically. These symmetries reduce the degrees of freedom in optimization (e.g., cutting precoding complexity by ~50%) and enhance the directionality of green interference, while ensuring balanced resource use for WSN nodes. The core objective is to minimize total transmit power while satisfying constraints of PU SR, secondary user (SU) quality-of-service (QoS), and PU interference temperature, achieved by converting non-convex SR constraints into solvable second-order cone (SOC) forms and using an alternating optimization algorithm to iteratively refine CBS/PBS precoding matrices and active RIS reflection matrices, with active RIS generating directional “green interference” to suppress eavesdroppers without artificial noise, avoiding redundant energy use. Simulations validate its adaptability to WSN scenarios: 50% lower transmit power than RIS-free schemes (with four CBS antennas), 37.5–40% power savings as active RIS elements increase to 60, and a 40% lower power growth slope in multi-user WSN scenarios, providing a symmetry-aided, low-power solution for secure and efficient WSN-integrated CRNs to advance intelligent WSNs. Full article