Search Results (18,942)

Biohydrogen is considered to be one of the fuels of the future, so there is a justified need to find efficient and cost-effective technologies for its production. This study evaluated the efficiency of two biohydrogen production pathways, specifically biophotolysis and dark fermentation, using Tetraselmis subcordiformis biomass. Microalgae production was performed in three variants, where the separation criterion was the type of culture medium: a control sample (synthetic medium; V1–PCR), agricultural wastewater from hydroponic tomato cultivation (V2–SL-WW), and effluent from a microbial fuel cell (V3–MFC-WW). The highest increase in biomass of T. subcordiformis was obtained in V2–SL-WW—2730 ± 212 mg VS/L, which was also associated with the maximum chlorophyll a content (65.0 ± 5.1 mg Chl-a/L). In biophotolysis, the highest specific hydrogen yields were obtained in V1–PCR (55.3 ± 4.3 mL/g VS) and V2 (54.3 ± 3.7 mL/g VS). The total hydrogen production in these variants was 166 ± 13 mL (V1–PCR) and 163 ± 11 mL (V2–SL-WW), respectively. The average H₂ production rate reached 4.70 ± 0.33 mL/h in V2–SL-WW, and the rate constant (k) was 0.030–0.031 h⁻¹. In anaerobic fermentation, the highest total and specific H₂ production was obtained in V1–PCR, 453 ± 31 mL and 45.3 ± 3.1 mL/g VS, respectively. The qualitative composition of the biogas confirmed a high hydrogen content: 61.4% (biophotolysis, V1) and 41.1% (dark fermentation, V2–SL-WW). The results obtained confirm that T. subcordiformis can be effectively cultivated on waste media and that the biohydrogen production maintains a high technological efficiency through both photolytic and fermentative mechanisms. The medium from hydroponic tomato cultivation (V2–SL-WW) proved to be particularly promising, as it combines high biomass productivity with a satisfactory biohydrogen production profile. Full article

This paper investigates the abnormal combustion behavior—specifically knock and pre-ignition—of a hydrogen direct-injection (H₂-DI) engine operated under stoichiometric conditions. Two different cylinder head configurations with central and lateral injector placement are analyzed using thermodynamic measurements, CFD simulations, and the optical diagnostic system VISIOLution^®. The results show that combustion stability and knock tendency are significantly influenced by injector positioning, injection pressure, and ignition timing. Controlled mixture formation and high turbulence during the compression phase are key to achieving both high power density and thermal efficiency in hydrogen-fueled engines. Full article

To enhance the operational efficiency of fuel cell engineering vehicles in transportation, reliable energy management strategies (EMSs) are essential for optimizing fuel consumption and power distribution. In this paper, we propose a novel energy management framework that utilizes a reinforcement learning-based adaptive hierarchical equivalent consumption minimization strategy (ECMS) to regulate fuel cell/battery hybrid system. The structure integrates deep Q-network (DQN), fuzzy logic, and ECMS algorithms and employs a long short-term memory neural network for working condition prediction. By combining DQN with the equivalence factor obtained using the battery state of charge penalty function and adjusting it using a fuzzy logic controller, the stability of the subsequent ECMS is enhanced. In a simulation environment, the proposed EMS achieves a 97.44% fuel economy compared to the dynamic programming-based global optimized EMS. Experimental findings indicate that the hierarchical ECMS effectively decreases the equivalent hydrogen consumption by 3.38%, 9.12%, and 16.39% compared to the adaptive ECMS, DQN-based ECMS, and classic ECMS, respectively. Therefore, the proposed methodology offers superior economic benefits. Full article

Hydrogen-based technologies are progressing in several areas, such as transportation and energy, especially regarding their use as a replacement for greenhouse gas-emitting fuels. However, hydrogen is known for its explosiveness and large-scale flammability; hence, there is a need to ensure it can be detected and measured without risk. Several types of hydrogen sensors are available on the market. Each sensor is suited to a specific environment and operating conditions. In recent years, Artificial Intelligence tools have been increasingly used to improve the design and performance of these sensors in terms of safety, reliability, sensitivity, speed, and selectivity. This paper provides a review of available hydrogen sensors, their fields of application, and the main directions explored by the scientific community for integrating Artificial Intelligence tools to improve their performance. A comparative analysis is presented based on criteria related to sensor technologies, data processing tools, and target performance. This review highlights the results achieved and the challenges that remain to be addressed in various application fields. Full article

Heavy vehicles present high aerodynamic drag. This results in significant fuel consumption and, consequently, high emissions of harmful substances. This study examines the variation in aerodynamic drag in a European-type truck with different trailer configurations. Passive flow control by geometry modifications of the rear part of the trailer and active flow control using the Coanda effect were tested, with the aim of improving the aerodynamic efficiency of the vehicle. To achieve this, a modular structure of a 1:30 scaled truck was designed to enable different trailer configurations. Drag measurements were performed with a two-component external balance, and PIV tests were conducted to correlate the drag reduction with the aerodynamic changes behind the trailer. Passive control reduced drag by up to 5.7%, and active flow control reduced it by up to 12.6% compared to the unmodified base trailer. PIV flow visualization confirms that blowing effectively reduces the recirculation zone behind the trailer. Full article

This study examines the swelling behaviour of nitrile-butadiene rubber (NBR) when interacting with tyre pyrolysis oils (TPO), with a focus on the chemical composition of TPO and their interaction with rubber matrices. Initially, a comparative analysis with conventional diesel fuel (DF) was performed using advanced analytical techniques, including two-dimensional gas chromatography coupled to mass spectrometry (2D-GC/MS), infrared (IR) spectroscopy, and nuclear magnetic resonance (¹H-NMR) spectroscopy. The analysis revealed that TPO contains a significantly higher proportion of aromatic hydrocarbons than DF, along with unsaturated and oxygen-containing compounds not present in DF. Based on these compositional differences, blends of TPO and DF were formulated and evaluated for their suitability as liquid energy carriers according to the specifications of DF. In principle, blends with an addition of up to 5 vol% TPO in DF are technically suitable for use as fuel. Subsequently, the sorption behaviour of TPO, DF, and their blends in NBR was investigated. The swelling potential was determined based on mass, density, and volume, and the changes in the hardness and tensile strength of NBR were recorded. The results demonstrate that TPO induces pronounced swelling in NBR, as evidenced by a marked increase in mass uptake and volume expansion. A linear increase was observed between the degree of swelling and the increasing TPO content in the blends. Mechanical property assessments revealed a corresponding decrease in the hardness and tensile strength of NBR upon exposure to TPO, with the most severe effects associated with neat TPO. This work provides a comprehensive assessment of TPO as a potential blend component for DF. It highlights the need for careful consideration of material compatibility in practical applications. Full article

This study introduces a health-aware fuzzy logic (FL) energy management strategy (EMS) for fuel cell electric commercial vehicles (FCECVs) that aimed to improve energy efficiency and extending fuel cell system (FCS) lifespan. The FL-based EMS was developed using vehicle power demand and battery state of charge (SOC) as inputs, with the FCS power change rate as the output, aiming to mitigate degradation induced by abrupt load transitions. A multi-objective optimization framework was established to optimize the fuzzy logic controller (FLC) parameters, achieving a balanced trade-off between fuel economy and FCS longevity. The non-dominated sorting genetic algorithm-II (NSGA-II) was utilized for optimization across various driving cycles, with average Pareto-optimal solutions employed for real-time application. Performance evaluation under standard and stochastic driving cycles benchmarked the proposed strategy against dynamic programming (DP), charge-depletion charge-sustaining (CD-CS), conventional FL strategies, and a non-optimized baseline. Results demonstrated an approximately $38\%$ reduction in hydrogen consumption (HC) relative to CD-CS and over $75\%$ improvement in degradation mitigation, with performance superior to that of DP. Although the strategy exhibits an average $17.39\%$ increase in computation time compared to CD-CS, the average single-step computation time is only $2.1$ ms, confirming its practical feasibility for real-time applications. Full article

Nowadays, we live in the smart mobility era in which vehicles are equipped with small sensing devices to collect various road information. With such sensors, we are able to provide an overview of what is happening on the road and offer an efficient solution for transport problems such as congestion, accidents, avoiding traffic lights, fuel consumption, etc. Particularly, driver drowsiness is one of the most important problems that transportation systems face and mostly leads to severe accidents, injuries, and deaths. In order to overcome such a problem, a set of sensor devices has been integrated into vehicles to monitor driver and driving behaviors, and then to evaluate the driver’s situation, e.g., drowsy or awake. Unfortunately, most of the proposed drowsiness detection techniques are dedicated to analyzing one behavior type, but not both, which may affect the accuracy rate of the detection. In this paper, we propose an efficient drowsiness detection framework (RDDF) that may analyze one behavior or be adapted to both of them in order to increase the accuracy of drowsiness detection. Mainly, RDDF periodically monitors the driver and driving behaviors, extracts important patterns, and then uses and compares a set of supervised learning models to detect drowsy drivers. After that, RDDF proposes a modified version of the K-nearest neighbors (KNN) model called Jaccard-KNN (JKNN) that increases drowsiness detection accuracy and overcomes several challenges imposed by traditional models. The proposed framework has been preliminarily validated through real sensor data, and we show the effectiveness of our framework in detecting real-time drowsy drivers with an accuracy rate of up to $99\%$. Full article

Road transport in the European Union is responsible for approximately 60% of PM10 emissions and 45% of PM2.5 emissions. Acoustic agglomeration is researched to be the most effective after-treatment method to control particle pollution. Recent experimental research suggests that at a frequency of around 20 kHz and a sound pressure level of 140 dB, particles can be agglomerated. The kinetic energy of the particles is influenced by the presence of acoustics, and this enhances the collision efficiency between the particles. These collided fine particles increase in size and can be easily filtered through conventional filters. Additionally, clean burning biofuels produce comparatively fewer particles; hence RME is used for experiments along with its two blends of isopropanol (RME95I5 and RME90I10). The results are then compared to those of standard diesel fuel. With an increase in load, an average reduction of 20% in fine particles is observed along with an increase in large-sized particles. The aggregation of smaller particles is observed in a range of 0–50% in almost all tested conditions. With the increase in isopropanol from 5 to 10%, oxygen content in the fuel increased by 7%, a 1% reduction in carbon and a 2% reduction in C/H ratio is observed which led to a 6 and 9% reduction in particle emissions at 60 Nm and 90 Nm, respectively. At higher loads, D100, RME95I5 and RME90I10 recorded an agglomeration of 10%, 111% and 189%, respectively. Similar results are observed for the tendency for agglomeration at lower loads. Full article

Hydrotreated vegetable oil (HVO) is a second-generation biofuel with physicochemical properties similar to conventional diesel. Composed mainly of n-paraffins, it offers favorable autoignition characteristics. Produced by hydrotreating vegetable oils or animal fats, including waste sources such as used cooking oil, HVO contributes to lower greenhouse gas emissions and waste utilization. Thanks to its similarity to diesel, it can be used directly or in blends without engine modifications. Blending reduces fossil fuel use and pollutant emissions while maintaining engine performance. This study investigates the autoignition behavior of diesel, neat HVO, and HVO–diesel blends containing 25%, 50%, and 75% HVO by volume. Experiments were conducted in a constant-volume combustion chamber at 550 °C and 650 °C to simulate engine-relevant conditions. Autoignition quality was assessed using ignition delay, combustion delay, average and maximum pressure rise rate, maximum pressure rise, apparent heat release rate, and derived cetane number. The results show that higher HVO content increases the sensitivity of ignition delay, combustion delay, and average pressure rise rate to lower chamber temperature. In addition, a linear increase in derived cetane number was observed with increasing HVO concentration, providing new insights into ignition and combustion behavior of renewable fuel blends. Full article

With the development of rocket technology, achieving high-precision landing has become a key technical challenge in the field of aerospace. To cope with this challenge, we propose a lossless convexification algorithm based on the integral pseudospectral method in this paper. Firstly, for the fuel optimization problem, the continuous dynamic equations and constraints are discretized with high accuracy using an integral-type pseudospectral method. By constructing a global integration matrix at Legendre–Gauss nodes, the original complex continuous problem is effectively transformed into a discrete form that is more tractable for numerical optimization. Secondly, the non-convex constraints are transformed using the lossless convexification technique, thereby reformulating the original problem as a second-order cone programming (SOCP) problem. The effectiveness of the proposed algorithm is validated through numerical simulations, which demonstrate high landing accuracy, robustness, and fuel efficiency. These results highlight the algorithm’s high performance and strong potential for practical application in space missions. Full article

Maritime power systems must reduce fuel use and emissions while improving resilience. We study a shipboard PV–battery subsystem interfaced with a DC–DC converter running maximum power point tracking (MPPT) and curve-scanning GMPPT to manage partial shading. Dynamic average-value models capture irradiance steps and show GMPPT sustains operation near the global MPP without local peak trapping. We compare converter options—conventional single-port stages, high-gain bidirectional dual-PWM converters, and three-level three-port topologies—provide sizing rules for passives, and note soft-switching in order to limit loss. A Fourier framework links the switching ripple to power quality metrics: as irradiance falls, the current THD rises while the PCC voltage distortion remains constant on a stiff bus. We make the loss relation explicit via $I_{rms}^{2}R$ scaling with THDi and propose a simple reactive power policy, assigning VAR ranges to active power bins. For AC-coupled cases, a hybrid EMT plus transient stability workflow estimates ride-through margins and critical clearing times, providing a practical path from modeling to monitoring. Full article

This study investigates the viability of using Ammonia as a carbon-free fuel for heat generation in terms of both reactive Nitrogen and Carbon emissions and production cost. As a carbon-free, environmentally friendly energy carrier, Ammonia has the potential to play a significant role in the sustainable, clean energy supply of the future. However, a major drawback of the steady combustion of ammonia for process heat generation is the extremely high levels of NO_x emissions it produces. In this pilot-scale study, the experimental results show that, through the oscillating combustion of NH₃, NO_x emissions can be reduced by as much as 80%. Production costs were compared to evaluate the economic feasibility of Ammonia-based heat; the results reveal the economic challenges associated with using Ammonia compared to natural gas, even when accounting for the development of CO₂ pricing. Only in terms of Carbon Capture and Storage requirements is Ammonia-based heat economically advantageous. This study also scrutinizes the economies of the production of gray and green Ammonia. Considering CO₂ certificate costs, the cost of green ammonia would be competitive in the near future. Full article

The use of artificial oxygenation to counteract the effects of hypoxia and improve living standards in high-altitude, low-oxygen settings is widespread. A recognized consequence of this intervention is that it elevates the risk of fire occurrence. In this study, we simulated a real fire environment with low-pressure oxygen enrichment in a plateau area. A new multi-measuring apparatus was constructed by integrating an electronic control cone heater and a low-pressure oxygen enrichment combustion platform to enable the simultaneous measurement of multiple parameters. The combined effects of varying oxygen concentrations and thermal irradiance on the combustion behavior of wood plastic composites (WPCs) under specific low-pressure conditions were investigated, and alterations in crucial combustion parameters were examined and evaluated. Increasing the oxygen concentration and heat flux significantly reduced the ignition and combustion times. For instance, at 50 kW/m², the ignition time decreased from 75 s to 16 s as the oxygen concentration increased from 21% to 35%. This effect was suppressed by higher heat fluxes. Compared with low oxygen concentrations and low thermal radiation environments, the ignition time of the material under high oxygen concentrations and high thermal radiation conditions was shortened by more than 78%, indicating that its flammability is enhanced under extreme conditions. Higher oxygen concentrations enhanced the heat feedback to the fuel surface, which accelerated pyrolysis and yielded a more compact flame with reduced dimensions and a color transition from blue-yellow to bright yellow. This intensified combustion was further manifested by an increased mass loss rate (MLR), elevated flame temperature, and a decline in residual mass percentage. The combustion of WPCs displayed distinct stage characteristics, exhibiting “double peak” features in both the MLR and flame temperature, which were attributed to the staged pyrolysis of its wood fiber and plastic components. Full article

Reliable operation of proton exchange membrane fuel cells (PEMFCs) is crucial for their widespread commercialization, and accurate fault diagnosis is the key to ensuring their long-term stable operation. However, traditional fault diagnosis methods not only lack sufficient interpretability, making it difficult for users to trust their diagnostic decisions, but also one-dimensional (1D) feature extraction methods highly rely on manual experience to design and extract features, which are easily affected by noise. This paper proposes a new interpretable fault diagnosis algorithm that integrates Gramian angular field (GAF) transform, convolutional neural network (CNN), and gradient-weighted class activation mapping (Grad-CAM) for enhanced fault diagnosis and analysis of proton exchange membrane fuel cells. The algorithm is systematically validated using experimental data to classify three critical health states: normal operation, membrane drying, and hydrogen leakage. The method first converts the 1D sensor signal into a two-dimensional GAF image to capture the temporal dependency and converts the diagnostic problem into an image recognition task. Then, the customized CNN architecture extracts hierarchical spatiotemporal features for fault classification, while Grad-CAM provides visual explanations by highlighting the most influential regions in the input signal. The results show that the diagnostic accuracy of the proposed model reaches 99.8%, which is 4.18%, 9.43% and 2.46% higher than other baseline models (SVM, LSTM, and CNN), respectively. Furthermore, the explainability analysis using Grad-CAM effectively mitigates the “black box” problem by generating visual heatmaps that pinpoint the key feature regions the model relies on to distinguish different health states. This validates the model’s decision-making rationality and significantly enhances the transparency and trustworthiness of the diagnostic process. Full article