Search Results (103,825)

An aqueous leaf extract of Egeria densa was used to green-synthesize iron (II) and iron (III) oxide nanoparticles from ferrous sulphate and ferric chloride, respectively. The successful green synthesis of the nanoparticles was confirmed through UV–visible spectroscopy, and the colour of the mixtures changed from light-yellow to green-black and reddish-brown for FeO–NPs and Fe₂O₃–NPs, respectively. The morphological characteristics of the nanoparticles were determined using an X-ray diffractometer (XRD), a Fourier transform infrared spectrophotometer (FTIR), a transmission electron microscope (TEM), and energy-dispersive X-ray spectroscopy (EDX). The UV–Vis spectrum of the FeO–NPs showed a sharp peak at 290 nm due to the surface plasmon resonance, while that of the Fe₂O₃–NPs showed a sharp peak at 300 nm. TEM analysis revealed that the FeO–NPs were oval to hexagonal in shape and were clustered together with an average size of 18.49 nm, while the Fe₂O₃-NPs were also oval to hexagonal in shape, but some were irregularly shaped, and they clustered together with an average size of 27.96 nm. EDX analysis showed the presence of elemental iron and oxygen in both types of nanoparticles, indicating that these nanoparticles were essentially present in oxide form. The XRD patterns of both the FeO–NPs and Fe₂O₃–NPs depicted that the nanoparticles produced were crystalline in nature and exhibited the rhombohedral crystal structure of hematite. The FT-IR spectra revealed that phenolic compounds were present on the surface of the nanoparticles and were responsible for reducing the iron salts into FeO–NPs and Fe₂O₃–NPs. Conclusively, this work demonstrated for the first time the ability of Elodea aqueous extract to synthesize iron-based nanoparticles from both iron (II) and iron (III) salts, highlighting its versatility as a green reducing and stabilizing agent. The dual-path synthesis approach provides new insights into the influence of the precursor oxidation state on nanoparticle formation, thereby expanding our understanding of plant-mediated nanoparticle production and offering a sustainable route for the fabrication of diverse iron oxide nanostructures. Furthermore, it provides a simple, cost-effective, and environmentally friendly method for the synthesis of the FeO–NPs and Fe₂O₃–NPs using Egeria densa. Full article

This study explored the feasibility of the carbon-free smelting of ferrochrome (FeCr) using a complex reducing agent—ferroaluminosilicalcium alloy (FeAlSiCa)—produced from industrial waste and ferrosilicochrome (FeSiCr) dust. Laboratory-scale smelting experiments were conducted with Cr concentrate and the addition of FeAlSiCa and FeSiCr dust under four different reducing agent contents: (1) 10% deficiency, (2) stoichiometric amount, (3) 10% excess, and (4) 20% excess. It was found that with a 10% excess, a nearly complete reduction of Cr₂O₃ was achieved (residual content in slag ≤ 0.9%), resulting in the formation of low-carbon FeCr (LC FeCr) with a high nitrogen content (up to 2.6%). Based on a thermodynamic analysis of the reduction reactions, the high reactivity of the FeAlSiCa and FeSiCr components (Ca, Al, and Si) at 1500 °C was confirmed. These reactions were exothermic, which demonstrates the energy efficiency of using these ferroalloys as reducing agents in FeCr smelting. The resulting slag is structurally stable and does not disintegrate over time, making it a promising candidate for potential reuse as a secondary raw material. The results demonstrate the promise of the proposed technology for both reducing the carbon footprint of ferroalloy production and lowering the cost of the metallothermic production of LC FeCr. Full article

We propose an auto regressive integrated moving average Markov model (ARIMAMKM) for predicting annual energy consumption in China and enhancing the accuracy of energy consumption forecasts. This novel model extends the traditional auto regressive integrated moving average (ARIMA($p,\mathrm{d},\mathrm{q}$)) model. The stationarity of China’s energy consumption data from 2000 to 2018 is assessed, with an augmented Dickey–Fuller (ADF) test conducted on the $\mathrm{d}$-order difference series. Based on the auto correlation function (ACF) and partial auto correlation function (PACF) plots of the difference time series, the optimal parameters $\mathrm{p}$ and $\mathrm{q}$ are selected using the Akaike information criterion (AIC) and Bayesian information criterion (BIC), thereby determining the specific ARIMA configuration. By simulating real values using the ARIMA model and calculating relative errors, the estimated values are categorized into states. These states are then combined with a Markov transition probability matrix to determine the final predicted values. The ARIMAMKM model is validated using China’s energy consumption data, achieving high prediction accuracy as evidenced by metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), $\mathrm{S}\mathrm{T}\mathrm{D}$, and ${\mathrm{R}}^2$. Comparative analysis demonstrates that the ARIMAMKM model outperforms five other competitive models: the grey model (GM(1,1)), ARIMA(0,4,2), quadratic function model (QFM), nonlinear auto regressive neural network (NAR), and fractional grey model (FGM(1,1)) in terms of fitting performance. Additionally, the model is applied to Guangdong province’s resident population data to further verify its validity and practicality. Full article

This article presents a comprehensive analysis of the potential for microgeneration of electrical energy from human physical activity and reviews current commercial and research solutions, including stationary bicycles, treadmills, rowing ergometers, strength equipment, and kinetic floor systems. The physiological foundations of human energy generation are examined, with attention to key factors such as age, gender, fitness level, maximum oxygen uptake, heart rate, and hydration. The study includes mathematical models of energy conversion from metabolic to electrical output, incorporating fatigue as a limiting factor in long-duration performance. Available energy storage technologies (e.g., lithium-ion batteries, supercapacitors, and flywheels) and intelligent energy management systems (EMS) for use in sports facilities and net-zero energy buildings are also reviewed. As part of the study, a conceptual design of a multifunctional training and diagnostic device is proposed to illustrate potential technological directions. This device integrates microgeneration with dynamic physiological monitoring and adaptive load control through power electronic conversion. The paper highlights both the opportunities and limitations of harvesting human-generated energy and outlines future directions for sustainable energy applications in fitness environments. A preliminary economic analysis is also included, showing that while the energy payback alone is limited, the device offers commercial potential when combined with diagnostic and smart fitness services and may contribute to broader building energy efficiency strategies through integration with intelligent energy systems. Full article

With increasing global energy transition and environmental awareness, liquefied natural gas (LNG) is rapidly developing as an efficient and clean energy source. LNG pumps are widely used in industrial applications. This study focuses on the LNG pump and pipeline system, and it innovatively establishes a computational model based on weak compressible fluid in order to better reflect the characteristics of pressure pulsation and the flow situation. Through numerical simulations, the flow characteristics of the pump were analyzed. In addition, the flow conditions at the pipe tee were analyzed, and the attenuation patterns of pressure waves at different frequencies within the pipe were also investigated. The internal flow field of the pump was analyzed at three specific time points. The results indicate that, during the initial start-up phase, the internal flow state of the pump is complex, with significant vortices and pressure fluctuations. As the flow rate and rotational speed increase, the flow gradually stabilizes. Moreover, the pressure pulsation coefficient within the pipeline varies significantly with position. Full article

The photovoltaic (PV) energy storage (ES) inverter is an effective way to solve the problems of energy shortage and environment pollution. However, when considering the constraints such as economic benefits and power supply reliability, the energy optimization and dispatching of this PV–ES system poses great challenges. This paper proposes an optimization method based on the combination of the particle swarm algorithm and non-linear penalty function to dispatch the energy of household PV–ES inverter. Based on the established optimization model of the PV–ES inverter system, compared with the static penalty function, the penalty factor can be automatically adjusted according to the range beyond the constraint by using the proposed non-linear penalty function. Furthermore, the particle swarm algorithm is used as the optimization engine, and the energy scheduling scheme is obtained by the combination of the particle swarm algorithm and the proposed non-linear penalty function. Finally, the simulation and hardware-in-the-loop results verify the correctness of the proposed algorithm, compared with the static penalty function, the user electricity expenses can be effectively reduced, and economic requirements can be met. Full article

This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm progresses through the optimization hyperspace. This modification addresses issues of poor convergence and suboptimal search in the original algorithm. Both the modified and standard algorithms were employed to design an HRES system comprising photovoltaic panels, wind turbines, fuel cells, batteries, and hydrogen storage, all connected via a DC-bus microgrid. The components were integrated with the microgrid using DC-DC power converters and supplied a designated load through a DC-AC inverter. Multiple operational scenarios and multi-objective criteria, including techno-economic metrics such as levelized cost of energy (LCOE) and loss of power supply probability (LPSP), were evaluated. Comparative analysis demonstrated that mSAO outperforms the standard SAO and the honey badger algorithm (HBA) used for the purpose of comparison only. Our simulation results highlighted that the PV–wind turbine–battery system achieved the best economic performance. In this case, the mSAO reduced the LPSP by approximately 38.89% and 87.50% over SAO and the HBA, respectively. Similarly, the mSAO also recorded LCOE performance superiority of 4.05% and 28.44% over SAO and the HBA, respectively. These results underscore the superiority of the mSAO in solving optimization problems. Full article

The rapid growth of urban metro systems requires novel strategies to guarantee operational dependability and energy efficiency. This article describes a new way to use deep reinforcement learning (DRL) to help metro networks with predictive maintenance that adapts to changing conditions and energy optimization. We used real-world transit data from the General Transit Feed Specification (GTFS) to model the maintenance scheduling and energy management problem as a Markov Decision Process. This included important operational metrics like peak-hour demand, train arrival times, and station stop densities. A custom reinforcement learning environment mimics the changing conditions of metro operations. Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) sophisticated deep reinforcement learning techniques were used to identify the optimal policies for decreasing energy consumption and downtime. The PPO hyperparameters were additionally optimized using Bayesian optimization by implementing Optuna, which produces a far greater performance than baseline DQNs and basic PPO. Comparative tests showed that our improved DRL-based method improves the accuracy of predictive maintenance and the efficiency of energy use, which lowers operational costs and raises the dependability of the service. These results show that advanced learning and optimization techniques could be added to public transportation systems in cities. This could lead to more sustainable and smart transportation management in big cities. Full article

Photovoltaic (PV) energy has become a pillar of clean energy in rural areas. However, its extensive deployment in regions with geographically dispersed locations and limited road conditions has made efficient inspection a significant challenge. To address these issues, this study proposes a multi-regional PV inspection path planning method based on the crested porcupine optimization (CPO) algorithm. This method first employs a hybrid optimization framework combining a genetic algorithm, Simulated Annealing, and Fuzzy C-Means Clustering (GASA-FCM) to divide PV power stations into multiple regions, adapting to their dispersed distribution characteristics. Subsequently, the CPO algorithm is used to calculate obstacle-avoidance paths, replacing the Euclidean distance in the traditional Traveling Salesman Problem (TSP) with adaptive rural road constraint conditions to better cope with the geographical complexity in real-world scenarios. The simulation results verify the advantages of this method, achieving significantly shorter path lengths, higher computational efficiency, and stronger stability compared to the traditional solutions, thereby improving the efficiency of rural PV inspection. Moreover, the proposed framework not only provides a practical inspection strategy for rural PV systems but also offers a solution to the Multiple-Depot Multiple Traveling Salesmen Problem (MDMTSP) under constrained conditions, expanding its application scope in similar scenarios. Full article

Accurate long-term electricity load forecasting is critical for energy planning, infrastructure development, and risk management, especially under increasing uncertainty from climate and economic shifts. This study proposes a multi-resolution probabilistic load forecasting framework that leverages temporal hierarchies to generate coherent forecasts at hourly, daily, monthly, and yearly levels. The model integrates climate and economic indicators and employs tailored forecasting techniques at each resolution, including XGBoost and ARIMAX. Initially incoherent forecasts across time scales are reconciled using advanced methods such as Ordinary Least Squares (OLS), Weighted Least Squares with Series Variance Scaling (WLS_V), and Structural Scaling (WLS_S) to ensure consistency. Using historical data from Alberta, Canada, the proposed approach improves the accuracy of deterministic forecasts and enhances the reliability of probabilistic forecasts, particularly when using the OLS reconciliation method. These results highlight the value of temporal hierarchy structures in producing high-resolution long-horizon load forecasts, providing actionable insights for utilities and policymakers involved in long-term energy planning and system optimization. Full article

Accurate State-of-Health (SOH) estimation is a key technology for ensuring battery safety, optimizing energy management, and enhancing lifecycle value. This paper proposes a novel SOH estimation method for lithium-ion batteries, utilizing incremental energy features and a hybrid deep learning model that combines Convolutional Neural Network (CNN), Kolmogorov–Arnold Network (KAN), and Bidirectional Long Short-Term Memory (BiLSTM) (CNN-KAN-BiLSTM). First, the battery’s voltage, current, temperature, and other data during the charging stage were measured and recorded through experiments. Incremental Energy Analysis (IEA) was conducted on the charging data to extract various incremental energy characteristics. The Pearson correlation method was used to verify the strong correlation between the proposed characteristics and the battery SOH. This paper includes experimental verification of the method for both battery cells and battery pack. For the battery cell, a complete multi-feature sequence was formed based on the incremental energy curve characteristics combined with temperature characteristics. For the battery pack, the characteristics of the incremental energy curve were supplemented with Variance of Voltage Means (VVM) as an inconsistent feature, combined with Standard Deviation of Temperature Means (SDTM), to create a complete multi-feature sequence. The features were then input into the CNN-KAN-BiLSTM deep learning model developed in this study for training, successfully estimating the SOH of lithium batteries. The results demonstrate that the proposed method can accurately estimate the SOH of lithium batteries, even though the SOH degradation of lithium batteries has significant nonlinear characteristics. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for the lithium battery pack were 0.3910 and 0.4797, respectively, with an average coefficient of determination (R²) exceeding 99%. The final SOH estimation MAE values for battery cells at different charging rates of 0.1 C (250 mA), 0.2 C (500 mA), and 0.5 C (1250 mA) were 0.2728, 0.3301, and 0.2094. The RMSE were 0.3792, 0.4494, and 0.2699, respectively. The corresponding R² values were 98.76%, 97.07%, and 99.37%, respectively. Finally, the effectiveness and universality of the method proposed in this paper were verified using the NASA battery dataset and the CALCE battery dataset. Full article

The continuous evolution of Internet of Things (IoT) technologies presents significant opportunities and challenges within the domain of engineering education. This paper introduces a novel and comprehensive framework that extends the established Engineering Design Process (EDP) by incorporating a modular Digital Twin (DT) structure specifically tailored to smart building IoT applications in education. Unlike previous approaches, our framework enables real-time system feedback, simulation-based design iteration, and hands-on experimentation—all integrated within a pedagogical flow aligned with engineering curricula. It comprises seven distinct phases, providing a complete methodology that guides learners from fundamental concepts to advanced applications, including data visualization, real-time simulation, and system optimization. To demonstrate the applicability of the proposed framework, we design and experiment with a practical use case related to a meteorological station and data, which incorporate IoT-enabled sensors, actuators, and microcontrollers for real-time monitoring of environmental parameters and energy consumption within a smart building campus facility. Additionally, to support EDP extension, a hybrid pedagogical approach is introduced, which combines traditional engineering hands-on education methodologies with DT activities, to further foster experimental learning, iterative system design, and complex systems thinking development. To this end, our approach aims to bridge the gap between theoretical science and engineering knowledge, along with practical application use cases, contributing to a better preparation of future engineers capable of addressing interdisciplinary challenges associated with smart systems and digital transformation within the Industry 4.0 era. Full article

Auxetic re-entrant (RE) unit cell-based honeycombs exhibit a negative Poisson’s ratio (NPR) and possess a greater energy absorption capacity than conventional hexagonal honeycombs. The energy absorption capabilities of these structures can be further enhanced through design modifications. This study explores novel double-cylindrical-shell-based RE unit cell (REC) designs with negative Poisson’s ratios (NPRs), and the impact of material variations on NPR is analyzed in detail. The REC structures have two distinct geometric configurations: narrow REC (REC-N) and wide REC (REC-W). To demonstrate that these new geometries exhibit NPR behavior, samples were produced using additive manufacturing (AM) with materials including polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and functionally graded (FG) PLA-ABS composites. Compression tests were conducted on the samples, following ASTM-D695-15 standards, to determine the Poisson’s ratios. The experimental results obtained were validated against numerical results for all material combinations. It is demonstrated that the NPR can vary by up to 20% with changes in the REC cell geometry design for the same material combination. It is stated that changes in the material composition can alter the NPR by up to 11%. Therefore, it is shown that both the REC cell design and material variations lead to significant changes in the NPR. Full article

In the pursuit of advancing the knowledge of energetic materials, we successfully formulated the energetic perovskite DAI-X1 with the chemical formula (C₁₂H₅₀I₆N₃Na₂O₃₆). Energetic perovskites hold great promise in various applications, including high-energy storage and propulsion systems, due to their unique combination of high energy density and structural versatility. DAI-X1, in particular, has attracted our attention because of its potential for optimized performance in these areas. We conducted an in-depth investigation into the high-pressure stability of DAI-X1 using in situ high-pressure Raman spectroscopy analysis. DAI-X1 possesses a cubic ABX₃ perovskite structure, and notable modifications in its Raman spectroscopic characteristics were noted within the pressure interval of 2.5–7.8 GPa, indicating structural instability under high pressure and suggesting a possible phase transition. Upon pressure release following compression to 12.5 GPa, the Raman spectra exhibit partial reversibility of the phase transition, as certain characteristic peaks return to their original positions while others retain irreversible shifts. This study establishes fundamental understanding for investigating high-pressure responses in DAI-X1 and analogous energetic materials. Full article

With the rapid advancement of modern society, the demand for efficient and convenient transportation has increased significantly, making traffic congestion a pressing challenge that must be addressed in the process of urban expansion. To effectively mitigate this issue, we propose an approach that leverages sensor networks to monitor real-time traffic data across road networks, enabling the precise characterization of traffic flow dynamics. This method integrates the Webster algorithm with a proportional–integral–derivative (PID) controller, whose parameters are optimized using a genetic algorithm, thereby facilitating scientifically informed traffic signal timing strategies for enhanced traffic regulation. Geomagnetic sensors are deployed along the roads at a ratio of 1:50–1:60, and radar sensors are deployed on the roadsides of key sections. This can effectively detect changes in road traffic flow and provide early warnings for possible accidents. The integration of the Webster method with a genetically optimized PID controller enables adaptive traffic signal timing with minimal energy consumption, effectively reducing road occupancy rates and mitigating congestion-related risks. Compared to conventional fixed-time control schemes, the proposed approach improves traffic regulation efficiency by 17.3%. Furthermore, it surpasses traditional real-time adaptive control strategies by 3% while significantly lowering communication energy expenditure. Notably, during peak hours, the genetically optimized PID controller enhances traffic control effectiveness by 13% relative to its non-optimized counterpart. A framework is proposed to improve the efficiency of road operation under the condition of random traffic changes. The k-means method is used to mark key roads, and weights are assigned based on this to coordinate and regulate traffic conditions. These findings underscore our contribution to the field of intelligent transportation systems by presenting a novel, energy-efficient, and highly effective traffic management solution. The proposed method not only advances the scientific understanding of dynamic traffic control but also offers a robust technical foundation for alleviating urban traffic congestion and improving overall travel efficiency. Full article