Search Results (9,805)

This study explored the optimization of green hydrogen production via seawater electrolysis powered by a hybrid photovoltaic (PV)-wind system in KwaZulu-Natal, South Africa. A Box–Behnken Design (BBD), adapted from Response Surface Methodology (RSM), was utilized to address the synergistic effect of key operational factors on the integration of renewable energy for green hydrogen production and its economic viability. Addressing critical gaps in renewable energy integration, the research evaluated the feasibility of direct seawater electrolysis and hybrid renewable systems, alongside their techno-economic viability, to support South Africa’s transition from a coal-dependent energy system. Key variables, including electrolyzer efficiency, wind and PV capacity, and financial parameters, were analyzed to optimize performance metrics such as the Levelized Cost of Hydrogen (LCOH), Net Present Cost (NPC), and annual hydrogen production. At 95% confidence level with regression coefficient (R² > 0.99) and statistical significance (p < 0.05), optimal conditions of electricity efficiency of 95%, a wind-turbine capacity of 4960 kW, a capital investment of $40,001, operational costs of $40,000 per year, a project lifetime of 29 years, a nominal discount rate of 8.9%, and a generic PV capacity of 29 kW resulted in a predictive LCOH of 0.124$/kg H₂ with a yearly production of 355,071 kg. Within the scope of this study, with the goal of minimizing the cost of production, the lowest LCOH observed can be attributed to the architecture of the power ratios (Wind/PV cells) at high energy efficiency (95%) without the cost of desalination of the seawater, energy storage and transportation. Electrolyzer efficiency emerged as the most influential factor, while financial parameters significantly affected the cost-related responses. The findings underscore the technical and economic viability of hybrid renewable-powered seawater electrolysis as a sustainable pathway for South Africa’s transition away from coal-based energy systems. Full article

This study presents a comparative investigation of combustion and emission characteristics in a two-stroke MAN 5S35ME-B9.5 marine diesel engine equipped with a Controllable Pitch Propeller and an Exhaust Gas Recirculation system. Experimental data were obtained from both factory shop tests conducted under the IMO NOx Technical Code 2008 E2 cycle and sea trials performed onboard the T/S Baek-Kyung. Engine performance was evaluated under Tier II-FB, ecoEGR, and Tier III modes, focusing on specific fuel oil consumption, peak cylinder pressure, exhaust gas temperature, and regulated emissions. Results indicate that Tier III achieved the greatest NOx abatement, reducing emissions by up to 76.4% (1464 to 346 ppm), but with penalties of 16.8% higher SFOC and 45.2% higher CO₂ concentration. EcoEGR provided a more favorable compromise, reducing NOx by 52.3% while limiting SFOC increases to ≤15.4% and CO₂ increases to ≤30.9%. Strong correlations were observed between NOx, Pmax, and exhaust gas temperature, reaffirming fundamental trade-offs, while O₂ and CO correlations showed greater variability under sea operation. Despite operational scatter, sea trial results reproduced the key patterns observed in shop tests, confirming robustness across conditions. Overall, this correlation-based analysis provides quantified evidence of performance–emission trade-offs and offers a practical foundation for optimizing CPP-equipped two-stroke engines under varying EGR strategies. Full article

The demand for sesame oil is increasing due to its nutritious and medicinal qualities and industrial applications such as biodiesel production. Mechanical oil extraction is commonly used although yield is lower. Roasting conditions could improve oil yield. The present study investigated heating conditions (temperature: 40, 50, and 60 °C and time: 15, 30, and 45 min) on oil extraction parameters of yellowish and blackish sesame varieties under a screw pressing operation based on a factorial design involving twenty-six experimental runs. The determined amounts of moisture content of yellowish and blackish sesame samples were 3.49 ± 0.19% w.b. and 6.69 ± 0.07% w.b. In that order, the oil contents of the samples were 38.73 ± 2.61% and 45.31 ± 6.51%. The overall optimal factor levels for explaining the calculated parameters (weight loss, seedcake, sediments in the oil, extraction loss, extracted crude oil, oil yield, and oil expression efficiency) were the heating temperature of 50 °C and time of 22.5 min for yellowish sesame, whereas those of blackish sesame were 60 °C and 15 min. The determined regression models with the significant terms predicted the crude oil, oil yield, and oil expression efficiency of yellowish sesame with the amounts of 25.496 g, 25.806%, and 66.631% in comparison with blackish sesame with the amounts of 20.449 g, 22.215%, and 49.029%. Yellowish sesame produced higher oil output than blackish sesame under the heating conditions. Similarities of absorption peaks were observed which can be used to assess adulteration and oil quality parameters. Full article

Operating power systems has become challenging due to the complexity of these systems. Stability studies are essential to ensure that a system operates under suitable conditions. Low-frequency oscillation modes (LFOMs) are one of the main branches of system angular stability studies and are often studied in small-signal stability. Many LFOMs in the system may have low and insufficient damping rates, negatively affecting the operation of power systems. Different control strategies have been proposed, such as the Wide-Area Damping Controller (WADC), to adequately and easily dampen these LFOMs. The operating principle of a WADC requires the reception of remote and synchronized signals from system PMUs through communication channels. However, WADCs are subject to communication failures and cyberattacks that compromise their proper operation. This paper proposes a multi-objective optimization model whose variables are the WADC parameters and the objective function guarantees the previously desired and high damping rates for the system under normal conditions and when there are permanent communication failures caused by a Denial-of-Service attack. The design method uses Linear Quadratic Regulator theory, where the parameters of this method are tuned by a bio-inspired algorithm. The studies were performed in the IEEE 68-bus system, considering a set of different operating points. The results achieved in the modal and time domain analysis confirm the successful and robust design of the WADC. Full article

Severe snowstorms pose multiple threats to high-speed rail systems, including sudden drops in track friction coefficients, icing of overhead contact lines, and reduced visibility. These conditions can trigger dynamic risks such as train speed restrictions, cascading delays, and operational disruptions. Addressing the limitations of traditional scheduling methods in spatio-temporal modeling during blizzards, real-time multi-objective trade-offs, and high-dimensional constraint solving efficiency, this paper proposes a collaborative optimization approach integrating temporal forecasting with deep reinforcement learning. A dual-module LSTM-PPO model is constructed using LSTM (Long Short-Term Memory) and PPO (Proximal Policy Optimization) algorithms, coupled with a composite reward function. This design collaboratively optimizes punctuality and scheduling stability, enabling efficient schedule adjustments. To validate the proposed method’s effectiveness, a simulation environment based on the Lanzhou-Xinjiang High-Speed Railway line was constructed. Experiments employing a three-stage blizzard evolution mechanism demonstrated that this approach effectively achieves a dynamic equilibrium among safety, punctuality, and scheduling stability during severe snowstorms. This provides crucial decision support for intelligent scheduling of high-speed rail systems under extreme weather conditions. Full article

Planetary gearbox serves as a key transmission component in planetary ball screw actuator systems. Under the action of alternating loads, the stress concentration locations of the planet carrier in actuators with planetary gear trains are prone to fatigue cracks, which can lead to catastrophic system breakdowns. However, due to the complex vibration transmission path and the interference of uninterested vibration components, the characteristic modulation signal is ambiguous, so it is challenging to diagnose this fault. Therefore, this paper proposes a new fault diagnosis method. Firstly, a vibration signal model is established to accurately characterize the amplitude and phase modulation effects caused by cracked carriers, providing theoretical guidance for fault feature identification. Subsequently, three novel sideband evaluators of the modulation signal bispectrum (MSB) and their parameter selection ranges are proposed to efficiently locate the optimal fault-related bifrequency signatures and reduce computational cost, leveraging the effects identified by the model. Finally, a novel health indicator, the mean absolute root value (MARV), is used to monitor the state of the planet carrier. The effectiveness of this method is verified by experiments on the planetary gearbox test rig. The results show that the robustness of the amplitude and phase modulation effect of the cracked carrier in the low-frequency band is significantly higher than that in the high-frequency band, and the initial carrier crack can be accurately identified using this phenomenon under different operating conditions. This study provides a reliable solution for the condition monitoring and health management of the actuation system, which is helpful to improve the safety and reliability of operation. Full article

Advances in smart animal husbandry necessitate efficient pig behavior monitoring, yet traditional approaches suffer from operational inefficiency and animal stress. We address these limitations through a lightweight YOLOv8n architecture enhanced with SPD-Conv for feature preservation during downsampling, LSKBlock attention for contextual feature fusion, and a dedicated small-target detection head. Experimental validation demonstrates superior performance: the optimized model achieves a 92.4% mean average precision (mAP@0.5) and 87.4% recall, significantly outperforming baseline YOLOv8n by 3.7% in AP while maintaining minimal parameter growth (3.34M). Controlled illumination tests confirm enhanced robustness under strong and warm lighting conditions, with performance gains of 1.5% and 0.7% in AP, respectively. This high-precision framework enables real-time recognition of standing, prone lying, lateral lying, and feeding behaviors in commercial piggeries, supporting early health anomaly detection through non-invasive monitoring. Full article

Urban traffic congestion is a significant challenge faced by cities globally, resulting in delays, increased emissions, and diminished quality of life. This study introduces an innovative traffic load-balancing algorithm developed as part of the IN2CCAM Horizon 2020 project, which was specifically tested in the city of Vigo, Spain. The proposed method incorporates short-term traffic forecasting through machine learning models—primarily Long Short-Term Memory (LSTM) networks—alongside a dynamic routing algorithm designed to equalize travel times across alternative routes. Historical speed and volume data collected from Bluetooth sensors were analyzed and modeled to predict traffic conditions 15 min ahead. The algorithm was implemented within the PTV Vissim microsimulation environment to assess its effectiveness. Results from 20 distinct traffic scenarios demonstrated significant improvements: an increase in average speed of up to 3%, an 8% reduction in delays, and a 10% decrease in total standstill time during peak weekday hours. Furthermore, average emissions of CO₂, NO_x, HC, and CO were reduced by 4% to 11% across the scenarios. These findings highlight the potential of integrating predictive analytics with real-time load balancing to enhance traffic efficiency and promote environmental sustainability in urban areas. The proposed approach can further support policymakers and traffic operators in designing more sustainable mobility strategies and optimizing future urban traffic management systems. Full article

This article presents an innovative approach to optimizing the seismic modeling and analysis of high-rise buildings by automating the process with Python 3.13 and the ETABS 22.1.0 API. The process begins with the collection of information on the base building, a structure of seventeen regular levels, which includes data from structural elements, material properties, geometric configuration, and seismic and gravitational loads. These data are organized in an Excel file for further processing. From this information, a code is developed in Python that automates the structural modeling in ETABS through its API. This code defines the sections, materials, edge conditions, and loads and models the elements according to their coordinates. The resulting base model is used as a starting point to generate an optimal solution using a genetic algorithm. The genetic algorithm adjusts column and beam sections using an approach that includes crossover and controlled mutation operations. Each solution is evaluated by the maximum displacement of the structure, calculating the fitness as the inverse of this displacement, favoring solutions with less deformation. The process is repeated across generations, selecting and crossing the best solutions. Finally, the model that generates the smallest displacement is saved as the optimal solution. Once the optimal solution has been obtained, it is implemented a second code in Python is implemented to perform static and dynamic seismic analysis. The key results, such as displacements, drifts, internal and basal shear forces, are processed and verified in accordance with the Peruvian Technical Standard E.030. The automated model with API shows a significant improvement in accuracy and efficiency compared to traditional methods, highlighting an R² = 0.995 in the static analysis, indicating an almost perfect fit, and an RMSE = 1.93261 × 10⁻⁵, reflecting a near-zero error. In the dynamic drift analysis, the automated model reaches an R² = 0.9385 and an RMSE = 5.21742 × 10⁻⁵, demonstrating its high precision. As for the lead time, the model automated completed the process in 13.2 min, which means a 99.5% reduction in comparison with the traditional method, which takes 3 h. On the other hand, the genetic algorithm had a run time of 191 min due to its stochastic nature and iterative process. The performance of the genetic algorithm shows that although the improvement is significant between Generation 1 and Generation 2, is stabilized in the following generations, with a slight decrease in Generation 5, suggesting that the algorithm has reached its level has reached a point of convergence. Full article

During the transition from urban expansion to renewal, optimizing environmental comfort under resource constraints presents critical challenges. While existing research confirms that multisensory interactions critically shape environmental comfort, these insights are rarely operationalized into protocols for resource-constrained contexts. Focusing on historic urban quarters that need to balance modification and preservation, this study quantifies multisensory (acoustic, visual, thermal) interactions and integrations to establish operational resource-optimization strategies. Through laboratory reproduction of 144 field-based experimental conditions (4 sound sources × 3 sound pressure levels × 4 green view indexes × 3 air temperatures), systematic subjective evaluations of acoustic, visual, thermal, and overall comfort were obtained. Key findings demonstrate: (1) Eliminating extreme comfort evaluations (e.g., “very uncomfortable”) within any single sensory domain stabilizes cross-sensory contributions to overall comfort, ensuring predictable cross-domain compensations and safeguarding resource efficacy; (2) Accumulating modest improvements across ≥2 sensory domains reduces per-domain performance threshold for satisfactory overall comfort, enabling constraint resolution (e.g., visual modification limits in historic districts); (3) Cross-domain optimization of environmental factors (e.g., sound source and air temperature) generates mutual enhancement effects, maximizing resource economy, whereas intra-domain optimization (e.g., sound source and sound pressure level) induces competitive inefficiencies. Collectively, these principles establish operational strategies for resource-constrained environmental improvements, advancing sustainable design and governance through evidence-based multisensory approaches. Full article

With buildings accounting for 40% of global energy consumption, heating, ventilation, and air conditioning (HVAC) systems represent the single largest opportunity for emissions reduction, consuming up to 60% of commercial building energy while maintaining occupant comfort. This critical balance between energy efficiency and human comfort has traditionally relied on rule-based and model predictive control strategies. Given the multi-objective nature and complexity of modern HVAC systems, these approaches fall short in satisfying both objectives. Recently, reinforcement learning (RL) has emerged as a method capable of learning optimal control policies directly from system interactions without requiring explicit models. However, standard RL approaches frequently violate comfort constraints during exploration, making them unsuitable for real-world deployment where occupant comfort cannot be compromised. This paper addresses two fundamental challenges in HVAC control: the difficulty of constrained optimization in RL and the challenge of defining appropriate comfort constraints across diverse conditions. We adopt a safe RL with a neural barrier certificate framework that (1) transforms the constrained HVAC problem into an unconstrained optimization and (2) constructs these certificates in a data-driven manner using neural networks, adapting to building-specific comfort patterns without manual threshold setting. This approach enables the agent to almost guarantee solutions that improve energy efficiency and ensure defined comfort limits. We validate our approach through seven experiments spanning residential and commercial buildings, from single-zone heat pump control to five-zone variable air volume (VAV) systems. Our safe RL framework achieves energy reduction compared to baseline operation while maintaining higher comfort compliance than unconstrained RL. The data-driven barrier construction discovers building-specific comfort patterns, enabling context-aware optimization impossible with fixed thresholds. While neural approximation prevents absolute safety guarantees, reducing catastrophic safety failures compared to unconstrained RL while maintaining adaptability positions this approach as a developmental bridge between RL theory and real-world building automation, though the considerable gap in both safety and energy performance relative to rule-based control indicates the method requires substantial improvement for practical deployment. Full article

To address the persistent challenge of reliable real-time flowrate estimation in complex offshore oil production systems using Electric Submersible Pumps (ESPs), this study proposes a hybrid modeling approach that integrates a first-principles hydrodynamic model with Long Short-Term Memory (LSTM) neural networks. The aim is to enhance prediction accuracy across five offshore wells (A through E) in Brazil, particularly under conditions of limited or noisy sensor data. The methodology encompasses exploratory data analysis, preprocessing, model development, training, and validation using high-frequency operational data, including active power, frequency, and pressure, all collected at one-minute intervals. The LSTM architectures were tailored to the operational stability of each well, ranging from simpler configurations for stable wells to more complex structures for transient systems. Results indicate that prediction accuracy is strongly correlated with operational stability: LSTM models achieved near-perfect forecasts in stable wells such as Well E, with minimal residuals, and effectively captured cyclical patterns in unstable wells such as Well B, albeit with greater error dispersion during abrupt transients. The model also demonstrated adaptability to planned interruptions, as observed in Well A. Statistical validation using ANOVA, Levene’s test, and Tukey’s HSD confirmed significant performance differences (α < 0.01) among the wells, underscoring the importance of well-specific model tuning. This study confirms that the LSTM-based hybrid approach is a robust and scalable solution for real-time flowrate forecasting in digital oilfields, supporting production optimization and fault detection, while laying the groundwork for future advances in adaptive and interpretable modeling of complex petroleum systems. Full article

Biomass gasification, a key waste-to-energy technology, is a complex thermochemical process with many input variables influencing the yield and quality of syngas. In this study, data-driven machine learning models are developed to capture the nonlinear relationships between feedstock properties, operating conditions, and syngas composition, in order to optimize process performance. Random Forest (RF), CatBoost (Categorical Boosting), and an Artificial Neural Network (ANN) were trained to predict key syngas outputs (syngas composition and syngas yield) from process inputs. The best-performing model (ANN) was then integrated into a multi-objective optimization framework using the open-source Optimization & Machine Learning Toolkit (OMLT) in Pyomo. An optimization problem was formulated with two objectives—maximizing the hydrogen-to-carbon monoxide (H₂/CO) ratio and maximizing the syngas yield simultaneously, subject to operational constraints. The trade-off between these competing objectives was resolved by generating a Pareto frontier, which identifies optimal operating points for different priority weightings of syngas quality vs. quantity. To interpret the ML models and validate domain knowledge, SHapley Additive exPlanations (SHAP) were applied, revealing that parameters such as equivalence ratio, steam-to-biomass ratio, feedstock lower heating value, and fixed carbon content significantly influence syngas outputs. Our results highlight a clear trade-off between maximizing hydrogen content and total gas yield and pinpoint optimal conditions for balancing this trade-off. This integrated approach, combining advanced ML predictions, explainability, and rigorous multi-objective optimization, is novel for biomass gasification and provides actionable insights to improve syngas production efficiency, demonstrating the value of data-driven optimization in sustainable waste-to-energy conversion processes. Full article

To address the limitations inherent in traditional simulation control schemes for dual-engine parallel operation systems in diesel engines—such as protracted development cycles, suboptimal interface compatibility, insufficient real-time performance, and inadequate support for dynamic condition simulation in applications like marine power systems—this paper proposes an embedded real-time controller based on model-based design. This methodology facilitates efficient development and high-precision real-time control of parallel operation systems. A multi-domain coupled simulation model integrating diesel power and parallel control algorithms is built in MATLAB/Simulink, with optimized C code auto-generated via Embedded Coder. Hardware centers on STM32F407VE, enabling 4–20 mA speed acquisition, CAN communication, and Ethernet transmission. Experimental results indicate that the architecture shortens development cycles from 8 to 3 weeks, with 895 microseconds of simulation steps meeting 1-millisecond real-time requirements. Vessel tests achieve ±1.8 r/min synchronization error and ±1.2% load distribution error at low cost. It adapts to varied diesel power via modular substitution and supports RS485/CAN-FD. In conclusion, the controller effectively handles real-time simulated diesel engine parallel systems and excels in efficiency, compatibility, and cost, offering a viable technical pathway for modernizing parallel power systems in applications such as marine vessels and power generation. Full article

Chicken meat is highly consumed worldwide due to its nutritional value, but its high water content and abundance of polyunsaturated fatty acids make it particularly vulnerable to structural and oxidative damage during freezing and thawing. Slow freezing, in particular, generates large ice crystals that severely impair water-holding capacity (WHC), increase drip loss, promote color deterioration, and intensify protein and lipid oxidation. Innovative thawing strategies are therefore required to mitigate these quality losses. Ultrasound (US) has been successfully applied to accelerate thawing of fast-frozen meat; however, its potential for slowly frozen chicken breast remains poorly understood. This study aimed to evaluate the effects of US-assisted thawing at two frequencies (25 and 130 kHz), two amplitudes (100% and 60%), and three operating modes (normal, sweep, and degas) on the quality of slowly frozen chicken breast. Conventional thawing required 50 min, yielding WHC of 9.87%, drip loss of 4.65%, free sulfhydryls of 16.38 µmol/g, and ∆E of 3.91. In contrast, the optimized US condition (25 kHz, 100% amplitude, sweep mode) thawed samples in only 18 min, with markedly improved WHC (23.14%), reduced drip loss (3.25%), higher preservation of free sulfhydryls (24.69 µmol/g), and minimal color change (∆E = 3.72). Conversely, less effective parameters (e.g., 130 kHz, 60% amplitude, normal mode) prolonged thawing and compromised quality, with WHC dropping to 9.96% and drip loss increasing to 9.05%. Overall, US reduced thawing time under all conditions, but quality responses depended strongly on the applied parameters. The present findings demonstrate the novelty of optimizing US frequency, amplitude, and mode for thawing slowly frozen chicken breast, highlighting sweep mode at 25 kHz and 100% amplitude as the most effective strategy. Future research should explore its scalability and industrial applicability for poultry processing. Full article