Search Results (1,867)

To date, the grinding behavior of saprolite and lateritic overburden mixtures remains poorly understood. The Bond Work Index (BWI) is the principal indicator used to determine the specific energy consumption during the grinding process. To establish the F₈₀ and P₈₀ values, granulometric distribution models—Rosin–Rammler (RR), Gates–Gaudin–Schuhmann (GGS), and the Swebrec function (SWEF)—were evaluated. The mineral phases of the feed samples were analyzed by X-ray powder diffraction. This study provides evidence that the RR function is the most suitable for simulating the particle size distribution of the feed material, with residual errors below 6.30% and a coefficient of determination (R²) exceeding 97%. After the grinding equilibrium cycle is reached, the SWEF model proves to be the most appropriate, exhibiting residual errors under 3.50% and R² values above 98%. BWI reveals that saprolite is the most difficult ore to grind, with specific energy consumption increasing from 16.38 kWh/t to 25.50 kWh/t as the proportion of saprolite in the mixture rises. This reflects a clear upward trend, as confirmed by a fitted model with an R² of 98.54%. In contrast, the grindability index (Gbp) decreases, indicating that the material becomes increasingly resistant to grinding as the saprolite content increases. This may be attributed to inherent material properties, such as hardness, or to physical phenomena related to fragmentation. The declining Gbp further suggests that greater energy input is required to achieve additional particle size reduction. Overall, the findings demonstrate that saprolite is inherently difficult to grind and behaves according to its own grinding characteristics, regardless of whether it is processed alone or in combination with lateritic overburden. Full article

Fuel cell trucks are a promising solution to reduce the disproportionately high greenhouse gas emissions of heavy-duty long-haul transportation. However, unlike conventional diesel vehicles, they lack combustion engine waste heat for cabin heating. As a result, electric heaters are often employed, which increase auxiliary energy consumption and reduce driving range. To address this challenge, advanced control strategies are needed to improve heating efficiency while maintaining passenger comfort. This study proposes and validates a methodology for implementing Model Predictive Control (MPC) in the cabin heating system of a fuel cell truck. Vehicle experiments were conducted to characterize dynamic heating behavior, passenger comfort indices, and to provide validation data for the mathematical models. Based on these models, an MPC strategy was developed in a Model-in-the-Loop simulation environment. The proposed approach achieves energy savings of up to 8.1% compared with conventional control using purely electric heating, and up to 21.7% when cabin heating is coupled with the medium-temperature cooling circuit. At the same time, passenger comfort is maintained within the desired range (PMV within ±0.5 under typical winter conditions). The results demonstrate the potential of MPC to enhance the energy efficiency of fuel cell trucks. The methodology presented provides a validated foundation for the further development of predictive thermal management strategies in heavy-duty zero-emission vehicles. Full article

A personalized framework for smart home automation is introduced, utilizing machine learning to predict user activities and allow for the context-aware control of living spaces. Predicting user activities, such as ‘Watch_TV’, ‘Sleep’, ‘Work_On_Computer’, and ‘Cook_Dinner’, is essential for improving occupant comfort, optimizing energy consumption, and offering proactive support in smart home settings. The Edge Light Human Activity Recognition Predictor, or EL-HARP, is the main prediction model used in this framework to predict user behavior. The system combines open-source software for real-time sensing, facial recognition, and appliance control with affordable hardware, including the Raspberry Pi 5, ESP32-CAM, Tuya smart switches, NFC (Near Field Communication), and ultrasonic sensors. In order to predict daily user activities, three gradient-boosting models—XGBoost, CatBoost, and LightGBM (Gradient Boosting Models)—are trained for each household using engineered features and past behaviour patterns. Using extended temporal features, LightGBM in particular achieves strong predictive performance within EL-HARP. The framework is optimized for edge deployment with efficient training, regularization, and class imbalance handling. A fully functional prototype demonstrates real-time performance and adaptability to individual behavior patterns. This work contributes a scalable, privacy-preserving, and user-centric approach to intelligent home automation. Full article

This paper presents the design and evaluation of a Linear Time-Varying Linear Quadratic Gaussian (LTV-LQG) controller for an energy efficient electric vehicle, using a predetermined driving strategy as the reference trajectory. The proposed approach begins with the development of a structured nonlinear vehicle model based on relevant subsystems, enabling accurate energy consumption estimation with a deviation of less than 2% from experimental measurements. This model serves as the basis for computing a near-optimal driving trajectory. The nonlinear model is linearized along the predefined trajectory to support control design. A time-varying control structure is then developed, integrating a Kalman filter that estimates unmeasured external disturbances, such as wind, and enhances feedback performance. The proposed control strategy is evaluated through simulations and compared to a rule-based switching controller that replicates human-like driving behavior. The simulation results demonstrate that the LTV-LQG controller consistently satisfies the time constraints in both headwind- and tailwind-dominant scenarios, where the switching controller tends to exceed the time limit. Moreover, in tailwind-dominant cases, the LTV-LQG controller achieves lower energy consumption (up to 15.4%). The proposed framework represents a computationally efficient and practically feasible control solution for electric vehicles operating under realistic disturbance conditions. Full article

This study evaluates the energy performance of a BOGE C 22-2 oil-injected rotary screw compressor under real industrial conditions. Using direct measurements with a power quality analyzer and thermodynamic modeling, key performance indicators such as compression work, mass flow rate, compressor efficiency, and motor efficiency were determined. The results revealed actual efficiencies of 27–48%, significantly lower than the expected 60–70% for this type of equipment, mainly due to partial-load operation and low airflow demand. A low power factor of approximately 0.72 was also observed, caused by a high share of reactive power consumption. To address these inefficiencies, the study recommends the installation of an automatic capacitor bank to improve power quality and the integration of a secondary variable speed compressor to enhance performance under low-demand conditions. These findings underscore the importance of assessing compressor behavior in real-world environments and implementing techno-economic strategies to increase energy efficiency and reduce industrial electricity consumption. Full article

The development of the Connected and Autonomous Vehicle (CAV) and Hybrid Electric Vehicle (HEV) provides a new effective means for the optimization of eco-driving strategies. However, the existing research has not effectively considered the cooperative speed optimization and power allocation problem of the Connected and Autonomous Plug-in Hybrid Electric Vehicle (CAPHEV) platoon. To this end, a hierarchical eco-driving strategy is proposed, which aims to enhance driving efficiency and fuel economy while ensuring the safety and comfort of the platoon. Firstly, an improved car-following model is proposed, which considers the motion states of multiple preceding vehicles. On this basis, a platoon cooperative car-following decision-making method based on model predictive control is designed. Secondly, a distributed energy management strategy is constructed, and a bionic optimization algorithm based on the behavior of nutcrackers is introduced to solve nonlinear problems, so as to solve the energy distribution and management problems of powertrain systems. Finally, the tests are conducted under the driving cycle of the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET). The results show that the proposed strategy can ensure the driving safety of the CAPHEV platoon in different scenes, and has excellent tracking accuracy and driving comfort. Compared with the rule-based strategy, the equivalent energy consumption of UDDS and HWFET is reduced by 20.7% and 5.5% in the battery’s healthy charging range, respectively. Full article

In recent years, helicopters have shown stronger advantages than ground transportation in post-disaster emergency response due to their strengths of rapid response and cross-domain maneuverability. Especially against the backdrop of the increasing frequency of extreme weather and natural disasters, the issues of power supply guarantee and power grid security caused by extreme events have become increasingly severe. Making full use of helicopter resources can better meet the needs of repairing inaccessible faulty facilities in power systems after disasters and quickly restoring power supply. This paper studies the behavioral mechanism and application basis of helicopters participating in the post-disaster emergency response of power systems. It obtains route planning that reflects the maneuvering characteristics of helicopters by constructing a spatial route-planning model, and proposes a post-disaster restoration method for power systems with the joint action of helicopters and energy storage to verify its feasibility and superiority. Finally, the restoration model is supplemented from the perspectives of a cost consumption and benefit analysis of helicopter application, and verified in the improved IEEE 30-bus system. The results show that helicopters greatly reduce the loss of emergency load curtailment after disasters and have good economic benefits in the applied scenarios. The proposed analysis method can help balance the improvement in resilience and economic feasibility in helicopter deployment. Full article

Reverse osmosis (RO) is the most widely used technology in seawater desalination, accounting for around 70% of installations worldwide due to its efficiency and lower energy consumption compared to conventional thermal processes. However, a major challenge for RO is concentration polarization (CP), a phenomenon that reduces permeate flow, increases osmotic pressure, and compromises salt rejection, affecting the useful life of the membranes. In this work, an RO pilot plant was operated with synthetic solutions ranging from 4830 to 39,850 mgL⁻¹ at pressures between 0.69 and 5.79 MPa, to analyze and predict CP behavior. The results obtained showed salt rejection percentages ranging from 98.80% to 99.63%. The adjusted polynomial models presented correlation coefficients close to unity, which supports their high predictive capacity and statistical robustness for estimating the behavior of CP as a function of pressure. These models were implemented in Python software, allowing for the simulation of non-experimental scenarios and the anticipation of critical conditions that could compromise the RO process. Therefore, this work provides a robust predictive simulation tool to optimize RO processes and ensure the sustainable supply of drinking water in regions with water availability problems. Full article

Glazed façades play a decisive role in building energy performance, particularly in high-radiation equatorial climates. This study examines the thermal behavior and economic feasibility of three glazing systems—10 mm monolithic clear glass, laminated solar-control glass, and selective double glazing—applied to the Plataforma Gubernamental Norte, the largest institutional building in Ecuador. Dynamic simulations using DesignBuilder with the EnergyPlus engine assessed solar gains, HVAC demand, and operative temperatures, complemented by a sensitivity analysis of SHGC, U-value, and Tvis. Results indicate that selective double glazing reduced annual HVAC consumption by 78.21% (110.6 MWh), while laminated glazing achieved a 55.40% reduction. SHGC and U-value emerged as the most influential parameters, whereas Tvis had no impact on energy loads. Despite strong technical performance, the economic analysis revealed payback periods exceeding 235 years under Ecuador’s subsidized tariff (USD 0.10/kWh), compared to the 18–25 years commonly observed in Europe. This highlights the “efficiency paradox”: advanced glazing solutions deliver significant energy savings but remain financially unfeasible in subsidy-driven contexts. The findings underscore the need for policy reforms to better align façade design strategies with energy resilience, an issue particularly relevant after Ecuador’s 2024 electricity crisis and ongoing debates on subsidy elimination. Full article

This paper presents a resilient and energy-efficient framework for jamming mitigation in cluster-based wireless sensor networks (WSNs), addressing a critical vulnerability in hostile or interference-prone environments. The proposed approa ch integrates dynamic cluster reorganization, adaptive MAC-layer behavior, and multipath routing strategies to restore communication capabilities and sustain network functionality under jamming conditions. The framework is evaluated across heterogeneous topologies using Zigbee and Bluetooth Low Energy (BLE); both stacks were validated in a physical testbed with matched jammer and traffic conditions, while simulation was used solely to tune parameters and support sensitivity analyses. Results demonstrate significant improvements in Packet Delivery Ratio, end-to-end delay, energy consumption, and retransmission rate, with BLE showing particularly high resilience when combined with the mitigation mechanism. Furthermore, a comparative analysis of routing protocols including AODV, GAF, and LEACH reveals that hierarchical protocols achieve superior performance when integrated with the proposed method. This framework has broader applicability in mission-critical IoT domains, including environmental monitoring, industrial automation, and healthcare systems. The findings confirm that the framework offers a scalable and protocol-agnostic defense mechanism, with potential applicability in mission-critical and interference-sensitive IoT deployments. Full article

Sludge electro-dewatering technology is an attractive dewatering technology, but its application is limited by high energy consumption and filter cloth clogging caused by the dissolution of extracellular polymeric substances (EPSs). Thus, the addition of inorganic coagulants is expected to enhance the electro-dewatering efficiency of waste activated sludge (WAS). In this study, we evaluated the effects of the three typical inorganic coagulants (HPAC, PAC, and FeCl₃) on sludge electro-dewatering behavior. The results show that the electro-dewatering rate at the cathode was increased with the raising of the inorganic coagulants dosage, and FeCl₃ exhibited the best effect on the improvement of sludge electro-dewatering among the three inorganic coagulants. The zeta potential of the sludge flocs and the electro-osmotic effect were raised with the increasing of the inorganic coagulants dosage. The sludge floc conditioned by FeCl₃ is more compact than HPAC and PAC. Moreover, the dissolved EPS content reduced in the sludge electro-dewatering process when inorganic coagulant was added. In comparison to increasing ionic strength, the compression of extracellular polymeric substances (EPSs) plays a more critical role in enhancing the electro-dewatering process of sludge. The addition of inorganic coagulants also reduced the energy consumption during water removal in the electro-dewatering process. Full article

Background/Objectives: Optimal well-being is critical for athletic performance, yet nutritional intake among athletes is frequently inadequate. Although subjective tools such as the Hooper Index are widely used to monitor athlete condition, their relationship with routine eating behaviors remains insufficiently explored. This study aimed to characterize the nutritional intake of Japanese male collegiate handball players and to identify eating behaviors associated with their subjective well-being, as measured by the Hooper Score. Methods: In this cross-sectional study, 64 male collegiate handball players completed a 3-day dietary record and a web-based questionnaire assessing eating habits, training load, and the Hooper Index (sleep, muscle soreness, stress, fatigue). Associations between dietary factors and the Hooper Score were examined using partial correlation and multiple regression analyses, adjusted for potential confounders. Results: Mean energy intake (30.1 ± 10.7 kcal/kg/day) and several micronutrient intakes were below recommended levels. Partial correlation analysis revealed that lower intakes of energy and multiple nutrients were significantly associated with poorer well-being (higher Hooper Scores) and more Subjective Health Complaints (SHC). Multiple regression analysis identified consistent dinner timing, greater protein intake (g/kg), more frequent consumption of nutrient-dense snacks, and less frequent consumption of unhealthy snacks as significant independent predictors of better Hooper Scores (p < 0.05). Conclusions: Suboptimal energy and nutrient intakes were common and associated with poorer subjective well-being. Specific eating behaviors, particularly meal regularity, snack quality, and adequate protein intake, emerged as independent predictors of the Hooper Score, offering practical indicators for nutritional assessment and athlete condition monitoring. Full article

The growing dependence on fossil fuels has raised concerns over energy security, resource depletion, and environmental impacts, driving the need for renewable alternatives. Coffee husk, a widely available agro-industrial residue, represents an underutilized feedstock for biodiesel production. In this study, biodiesel was synthesized from coffee husk oil using a two-step transesterification process to address its high free fatty acid content (21%). Physicochemical analysis showed that Coffee Husk Oil Methyl Ester (CHOME) possessed a density of 863 kg m⁻³, viscosity of 4.85 cSt, and calorific value of 33.51 MJ kg⁻¹, compared to diesel with 812 kg m⁻³, 2.3 cSt, and 42.4 MJ kg⁻¹. FTIR analysis confirmed the presence of ester carbonyl and C–O functional groups characteristic of CHOME, influencing its combustion behavior. Engine tests were then conducted using B0, B10, B30, B50, and B100 blends under different loads, both with and without fuel preheating. Results showed that neat CHOME (B100) exhibited 11.8% lower brake thermal efficiency (BTE) than diesel, but preheating at 95 °C improved BTE by 5%, with preheated B10 slightly surpassing diesel by 0.5%. Preheating also reduced brake-specific fuel consumption by up to 7.75%. Emission analysis revealed that B100 achieved reductions of 6.4% CO, 8.3% HC, and 7.0% smoke opacity, while NOx increased only marginally (2.86%). Overall, fuel preheating effectively mitigated viscosity-related drawbacks, enabling coffee husk biodiesel to deliver competitive performance with lower emissions, highlighting its potential as a sustainable waste-to-energy fuel. Full article

With the rapid advancement of the electric vehicle (EV) industry, the ownership of EVs and their charging power have increased significantly, gradually exerting a greater impact on the power grid. To meet the diverse charging needs of different EV users, the coordinated planning of fast- and slow-charging stations can reduce the influence of charging loads on the power grid while fulfilling user demands and increasing the number of EVs that can be served. This paper establishes a collaborative planning model for multi-type charging stations (CSs), considering the comprehensive satisfaction of EV users. Firstly, a comprehensive satisfaction model of multi-type EV users considering their behavioral characteristics is established to characterize the impact of fast- and slow-charging CSs on the satisfaction of different types of users. Secondly, a two-layer cooperative planning model of multi-type CSs considering comprehensive satisfaction of EV users is established to determine the location of CSs and the number of fast- and slow-charging configurations to satisfy the users’ demand for different types of charging piles. Thirdly, a solution algorithm for the two-layer planning model based on the greedy theory algorithm is proposed, which transforms the upper layer charging pile planning model into a charging pile multi-round expansion problem to speed up the model solving. Finally, the validity of the proposed models is verified through case studies, and the results show that the planning scheme obtained can take into account the user’s charging satisfaction while guaranteeing the economy, and at the same time, the scheme has a positive significance in the promotion of new energy consumption, reduction in network loss, and alleviation of traffic congestion. Full article

This work presents the development of a novel multi-scale modeling framework for investigating the beneficial impact of Ti-, Zr-, and V-doped magnesium hydride nanomaterials on hydrogen storage performance. The proposed model integrates atomistic-scale simulations based on density functional theory (DFT) with system-level dynamic heat and mass transfer modeling. At the nanoscale, DFT analysis provides key thermodynamic and kinetic parameters, including reaction enthalpy, entropy, and activation energy, which are incorporated into the macroscopic model to predict the hydrogenation behavior of MgH₂ nanostructures under realistic thermal boundary conditions. Model validation is performed through comparison with experimental data from the literature, showing excellent agreement. The DFT analysis reveals that doping MgH₂ nanomaterials with Ti, V, and Zr modifies their thermodynamic properties, including enthalpy of formation and desorption temperature. At the reactor scale, these modifications lead to enhanced hydrogenation kinetics and improved thermal management. Compared to pristine MgH₂, hydrogenation time is reduced by 21%, 40%, and 42% for Ti-, Zr-, and V-doped nanomaterials, respectively, while thermal energy consumption during hydrogenation decreases by ~17% for V doping. These results highlight the strong correlation between nanoscale modifications and macroscopic system performance. The proposed multi-scale model provides a powerful tool for guiding the design and optimization of advanced nanostructured hydrogen storage materials for sustainable energy applications. Full article