Search Results (852)

Many applications of quantum computers require the classical and quantum implementation of dynamic systems (DSs). These applications comprise interacting quantum and classical tasks. While quantum tasks evolve in the quantum domain, classical tasks behave in the classical domain. Besides tackling these kinds of tasks, the computational gap between these domains is covered by the current study. The quantum computing feature All at Once (A@O) executions is appropriate for static systems but less for DSs. The novelty of the proposed approach consists of using Distributed Quantum-Centric Petri Net (DQCPN) models composed of quantum and high-level Petri Nets for specification, design, verification, and implementation of classical–quantum applications. Quantum Processing Units (QPUs) are linked to classical components implementing the control and optimization operations in the proposed application. Many practical applications combine quantum and classical computing to address optimization problems. Quantum computers can be built with a combination of qubits and bosonic qumodes, leading to a new paradigm toward quantum computing. The optimizations are performed by some Evolutionary Algorithms (EAs), including Particle Swarm Optimization (PSO) methods and Genetic Algorithms (GAs). For experiments, an Urban Vehicle Traffic System (UVTS) is used as an open distributed system. The vehicle flows are implemented by discrete qubits, discrete vectors of qubits, or qumodes. Full article

Hydrogen has been explored as a greener alternative for greenhouse gas emissions reduction. Sodium borohydride (NaBH₄) is a favorable hydrogen carrier due to its high hydrogen content, safe handling, and rapid hydrogen release. This work presents a novel synthesis of the catalyst CoFe₂O₄/Fe₂O₃ using nanocellulose fibers (TCNF) as reactive templates for metal adsorption and subsequent calcination. The resulting material was tested for H₂ production from basic NaBH₄ aqueous solutions (10–55 °C). The catalyst’s composition is 74.8 wt% CoFe₂O₄, 25 wt% Fe₂O₃, and 0.2 wt% Fe₂(SO₄)₃ with agglomerated spheroidal particles (15–20 nm) and homogeneous Fe and Co distribution. The catalyst produced 1785 mL of H₂ in 15 min at 25 °C (50 mg catalyst, 4.0% NaBH₄, and 2.5 wt% NaOH), close to the stoichiometric maximum (2086 mL). The maximum H₂ generation rate (HGR) reached 3.55 L min⁻¹ g_cat⁻¹ at 40 °C. Activation energies were determined using empirical (38.4 ± 5.3 kJ mol⁻¹) and Langmuir–Hinshelwood (L–H) models (42.2 ± 5.8 kJ mol⁻¹), consistent with values for other Co-ferrite catalysts. Kinetic data fitted better to the L–H model, suggesting that boron complex adsorption precedes H₂ evolution. Full article

To improve the economy and stability of data center green power direct supply, the capacity configuration optimization of wind–light–load storage based on improved particle swarm optimization (PSO) is conducted. According to wind speed, the Weibull distribution of wind output is established, while the Beta distribution of solar output is established according to light intensity. Furthermore, by conducting the correlation analysis, it is indicated that there is a negative correlation between wind and solar output, which is helpful to optimize the mix of wind and solar output. To minimize the yearly average cost of wind–light–load storage, the capacity configuration optimization model is established, where the constraints include wind and solar output, energy storage capacity, balance between wind and solar output and data center load. To solve the capacity configuration optimization model, the improved PSO is adopted, compared to other optimization algorithms, like differential evolution (DE), genetic algorithm (GA) and grey wolf optimizer (GWO); by adjusting the inertia weight factor dynamically, the improved PSO is more likely to escape the local optimal solution. To validate the feasibility of data center green power direct supply with wind–light–load storage, a case study is conducted. By solving the capacity configuration optimization model of wind–light–load storage with the improved PSO, the balance rate between wind–solar output and data center load is improved by 12.5%, while the rate of abandoned wind and solar output is reduced by 17.5%, which is helpful to improve the economy and stability of data center green power direct supply. Full article

Identifying suitable catalyst types and efficient loading methods remains a key research challenge for implementing the in situ catalytic pyrolysis of tar-rich coal. This study investigated a lignite and a gas coal, employing NiCl₂ solution for Ni²⁺ catalyst loading via room-temperature impregnation and hydrothermal treatment on coal particles sized 6–13 mm. The efficiency of Ni²⁺ loading through hydrothermal treatment and the characteristics of pyrolysis product distribution and composition before and after treatment were examined. The results indicated that after NiCl₂ solution impregnation, the Ni²⁺ content in lignite increased from nearly undetectable to over 20 mg/g, whereas in gas coal, it only rose to less than 2 mg/g. Ion exchange is hypothesized to be a primary pathway for Ni²⁺ loading into coal. After hydrothermal treatment at 170 °C, the Ni²⁺ loadings in lignite and gas coal reached 33.6 and 1.45 mg/g, respectively. The loaded Ni²⁺ exhibited distinct catalytic effects on the two coals. For lignite, Ni²⁺ catalyzed the deoxygenation of oxygen-containing compounds and the aromatization of aliphatic hydrocarbons. For gas coal, hydrothermal treatment with NiCl₂ solution at 170 and 220 °C promoted hydrogen transfer reactions, resulting in an increase in tar yield from 10.67% to 11.30% and 11.64%, respectively. Also, the H₂ yield decreased, accompanied by a decrease in aromatic hydrocarbons and an increase in phenolic compounds within the tar. Full article

Virtual power plants (VPPs) integrate distributed energy sources and demand-side resources, but their efficient intelligent resource decision-making faces challenges such as high-dimensional constraints, output volatility of renewable energy, and insufficient adaptability of traditional optimization algorithms. To address these issues, an innovative intelligent decision-making framework based on the Ant Colony Algorithm–Simulated Annealing (ACO-SA) is first proposed in this paper, aiming to realize intelligent collaborative decision-making for the economy and operational stability of VPP in complex scenarios. This framework combines the global path-searching capability of the Ant Colony Algorithm (ACO) with the probabilistic jumping characteristic of the Simulated Annealing Algorithm (SA) and designs a dynamic parameter collaborative adjustment mechanism, which effectively overcomes the defects of traditional algorithms such as slow convergence and easy trapping in local optimal solutions. Secondly, a resource intelligent decision-making cost model under the VPP framework is constructed. To verify algorithm performance, comparative experiments covering multiple scenarios (agricultural parks, industrial parks, and industrial parks with energy storage equipment) are designed and conducted. Finally, the simulation results show that compared with ACO, SA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), ACO-SA exhibits significant advantages in terms of scheduling cost and convergence speed; the average scheduling cost of ACO-SA is 2.31%, 0.23%, 3.57%, and 1.97% lower than that of GA, PSO, ACO, and SA, respectively, and it can maintain excellent stability even in high-dimensional constraint scenarios with energy storage systems. Full article

This paper presents three simple and efficient advanced optimization algorithms, namely the best–worst–random (BWR), best–mean–random (BMR), and best–mean–worst–random (BMWR) algorithms designed to address unconstrained and constrained single- and multi-objective optimization tasks of the metal casting processes. The effectiveness of the algorithms is demonstrated through real case studies, including (i) optimization of a lost foam casting process for producing a fifth wheel coupling shell from EN-GJS-400-18 ductile iron, (ii) optimization of process parameters of die casting of A360 Al-alloy, (iii) optimization of wear rate in AA7178 alloy reinforced with nano-SiC particles fabricated via the stir-casting process, (iv) two-objectives optimization of a low-pressure casting process using a sand mold for producing A356 engine block, and (v) four-objectives optimization of a squeeze casting process for LM20 material. Results demonstrate that the proposed algorithms consistently achieve faster convergence, superior solution quality, and reduced function evaluations compared to simulation software (ProCAST, CAE, and FEA) and established metaheuristics (ABC, Rao-1, PSO, NSGA-II, and GA). For single-objective problems, BWR, BMR, and BMWR yield nearly identical solutions, whereas in multi-objective tasks, their behaviors diverge, offering well-distributed Pareto fronts and improved convergence. These findings establish BWR, BMR, and BMWR as efficient and robust optimizers, positioning them as promising decision support tools for industrial metal casting. Full article

The Maxwell–Boltzmann (MB) distribution is important because it provides the statistical foundation for connecting microscopic particle motion to macroscopic gas properties by statistically describing molecular speeds and energies, making it essential for understanding and predicting the behavior of classical ideal gases. This study advances the statistical modeling of lifetime distributions by developing a comprehensive reliability analysis of the MB distribution under an improved adaptive progressive censoring framework. The proposed scheme strategically enhances experimental flexibility by dynamically adjusting censoring protocols, thereby preserving more information from test samples compared to conventional designs. Maximum likelihood estimation, interval estimation, and Bayesian inference are rigorously derived for the MB parameters, with asymptotic properties established to ensure methodological soundness. To address computational challenges, Markov chain Monte Carlo algorithms are employed for efficient Bayesian implementation. A detailed exploration of reliability measures—including hazard rate, mean residual life, and stress–strength models—demonstrates the MB distribution’s suitability for complex reliability settings. Extensive Monte Carlo simulations validate the efficiency and precision of the proposed inferential procedures, highlighting significant gains over traditional censoring approaches. Finally, the utility of the methodology is showcased through real-world applications to physics and engineering datasets, where the MB distribution coupled with such censoring yields superior predictive performance. This genuine examination is conducted through two datasets (including the failure times of aircraft windshields, capturing degradation under extreme environmental and operational stress, and mechanical component failure times) that represent recurrent challenges in industrial systems. This work contributes a unified statistical framework that broadens the applicability of the Maxwell–Boltzmann model in reliability contexts and provides practitioners with a powerful tool for decision making under censored data environments. Full article

This work presents a systematic literature review of powder-gas jet stream (PGJS) characterisation techniques for coaxial nozzles in the laser directed energy deposition process (L-DEDp). The analysis includes thirty-four camera-based and four weight-based techniques. In weight-based techniques, the mapping of powder concentration is made by measuring the powder flow rate in certain areas within the PGJS. Despite being cost-effective, these methods are time-consuming, invasive, and less suitable for real-time monitoring. Camera-based techniques use laser light and a camera to capture particle intensities, allowing for the non-intrusive measurement of powder distribution. Despite its advantage, limitations are reported in the literature regarding the techniques. Detecting dense or fine powder flows accurately is challenging. Two-dimensional images cannot fully represent the jet’s three-dimensional structure, relying on image processing algorithms for the results. However, the non-existence of a common standard metric for evaluating and comparing results across various setups is a significant gap, as each characterisation often needs to be performed on a case-by-case basis. To address these challenges, a basic reporting structure is suggested to enable a standardised assessment of PGJS measurements, thereby supporting process control and quality assurance in L-DEDp applications. Full article

The fortification of bakery products with alternative protein sources, including edible insects, offers a promising approach to improving nutritional quality while addressing sustainability challenges. This study evaluated graded replacement of type 750 wheat flour with Acheta domesticus (house cricket) powder—together with an extreme 100% cricket-powder formulation—on the nutritional composition, color, particle size distribution, fermentative properties, baking loss, crumb hardness, and sensory quality of bread. Fifteen baked variants were prepared: a 100% wheat flour control; thirteen wheat–cricket blends containing 5–90% cricket powder; and an extreme formulation with 100% cricket powder. Increasing cricket-powder levels significantly increased protein, fat, fiber, zinc, and riboflavin contents while decreasing carbohydrate and starch levels. Technologically, higher substitution levels resulted in darker crumb color, a shift toward coarser particle size distribution, reduced gas retention during proofing, and increased baking loss. Sensory analysis indicated that up to 15% inclusion maintained full consumer acceptability, while 20–25% was at the acceptance threshold. Above 35%, acceptability declined sharply due to intensified earthy flavors and textural changes. The findings highlight 15% inclusion as the optimal balance between enhanced nutritional value and sensory quality, with potential for higher incorporation if appropriate technological modifications are applied. Full article

To address the challenges of low permeability, high gas adsorption, and a fragile structure in coalbed methane reservoirs, this study developed a high-strength composite proppant with an activated carbon skeleton via nitric acid pretreatment, silica–alumina sol coating, and calcination. Orthogonal experiments optimized the preparation conditions: 30–40 mesh activated carbon, Si/Al molar ratio of 4:1, calcination at 650 °C for 2 h. The resulting proppant exhibited an excellent performance: a single-particle compressive strength of 55.5 N, porosity of 33.2%, crushing rate of only 2.3% under 50 MPa closure pressure, and permeability 48.5% higher than quartz sand. In simulated acidic coalbed environments (pH 3–5), its acid corrosion rate was <2.8%, and it enhanced methane desorption by 16.2% compared to pure coal. Additionally, the proppant showed a superior transport performance in fracturing fluids, with better distribution uniformity in fractures than ceramsite, and its hydrophobic surface (contact angle 115.32°) improved fracturing fluid flowback efficiency. This proppant integrates high strength, good conductivity, gas desorption promotion, and corrosion resistance, offering a novel material solution for efficient coalbed methane extraction. Full article

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are a class of synthetic organic compounds with extremely high chemical stability and environmental persistence that are widely used in the industrial sector and in consumer goods. Their strong C-F bonds make them difficult to degrade, meaning they can migrate through the atmosphere and settle over long distances, posing long-term risks to the global ecological environment and human health. This article systematically reviews the classification, physicochemical properties, concentration levels, spatial distribution, migration and transformation behaviors, and health and ecological impacts of PFASs in the atmosphere, along with related analytical detection techniques and pollution control methods. Studies show that short-chain PFASs are more likely to migrate through the atmosphere due to their high water solubility and volatility, while long-chain PFASs tend to be adsorbed onto particulate matter and display stronger bioaccumulation. Although atmospheric research on PFASs lags behind that focused on their dynamics in water and soil, the existing data still reveal a difference in their distribution and regional pollution characteristics in the gas and particle phases. Toxicological studies have confirmed that PFAS exposure is associated with liver injury, immunosuppression, developmental toxicity, and cancer risk and can threaten ecological security through the food chain. Currently, governance technologies are confronted with the challenges of low efficiency and high cost. In the future, it will be necessary to combine multi-media models, new analytical techniques, and international collaboration to promote the development of source control and innovative governance strategies. Full article

With the progressive depletion of conventional oil and gas resources and the increasing demand for alternative energy, organic-rich sedimentary rock—oil shale—has attracted widespread attention as a key unconventional hydrocarbon resource. Pyrolysis is the essential process for converting the organic matter in oil shale into recoverable hydrocarbons, and a detailed understanding of its behavior is crucial for improving development efficiency. This review systematically summarizes the research progress on the pyrolysis characteristics of oil shale under laboratory conditions. It focuses on the applications of thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) in identifying pyrolysis stages, extracting kinetic parameters, and analyzing thermal effects; the role of coupled spectroscopic techniques (e.g., TG-FTIR, TG-MS) in elucidating the evolution of gaseous products; and the effects of key parameters such as pyrolysis temperature, heating rate, particle size, and reaction atmosphere on product distribution and yield. Furthermore, the mechanisms and effects of three distinct heating strategies—conventional heating, microwave heating, and autothermic pyrolysis—are compared, and the influence of inherent minerals and external catalysts on reaction pathways is discussed. Despite significant advances, challenges remain in quantitatively describing reaction mechanisms, accurately predicting product yields, and generalizing kinetic models. Future research should integrate multiscale experiments, in situ characterization, and molecular simulations to construct pyrolysis mechanism models tailored to various oil shale types, thereby providing theoretical support for the development of efficient and environmentally friendly oil shale conversion technologies. Full article

The optimal allocation of Wireless Smart Concentrators (WSCs) in low-voltage (LV) distribution networks poses significant challenges due to signal attenuation caused by varying building densities and vegetation. This paper proposes a Variable Neighborhood Search (VNS) algorithm to optimize the placement of WSCs in LV distribution networks. To comprehensively assess the proposed approach, both linear and nonlinear mathematical formulations are considered, depending on whether the distance between meters and concentrators is treated as a fixed parameter or as a decision variable. The performance of the proposed VNS algorithm is benchmarked against both exact solvers and metaheuristics such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Tabu Search (TS). In the linear formulation, VNS achieved the exact optimal solution with execution times up to 75% faster than competing methods. For the more complex nonlinear model, VNS consistently identified superior solutions while requiring less computational effort. These results underscore the algorithm’s ability to balance solution quality and efficiency, making it particularly well-suited for large-scale, resource-constrained utility planning. Full article

In order to solve the problem of low thermal efficiency of a 130 t/h industrial circulating fluidized bed boiler, a computational particle fluid dynamic approach was used in this work to study two-phase gas–solid flow, heat transfer, and combustion. The factors influencing coal particle size distributions, air distribution strategies, and operational loads are addressed. The results showed that particle distribution exhibits “core–annulus” flow with a dense-phase bottom region and dilute-phase upper zone. A higher primary air ratio (0.8–1.5) enhances axial gas velocity and bed temperature but reduces secondary air zone (2.5–5.8 m) temperature. A higher primary air ratio also decreases outlet O₂ mole fraction and increases fly ash carbon content, with optimal thermal efficiency at a ratio of 1.0. In addition, as the coal PSD decreases and the load increases, the overall temperature of the furnace increases and the outlet O₂ mole fraction decreases. Full article

The gas desorption behavior of coal is a key basis for guiding gas parameter determination, optimizing gas extraction, and preventing gas-related disasters. Coal in mine working faces typically exhibits a mixed particle size distribution. However, research on the gas desorption behavior of mixed-size coal samples and comparative studies with single-sized samples remains insufficient. This study employed a self-developed experimental system for the multi-field coupled seepage desorption of gas-bearing coal to conduct comparative experiments on gas desorption behavior between single-sized and mixed-size coal samples. Systematic analysis revealed significant differences in their desorption and diffusion patterns: smaller particle sizes and higher proportions of small particles correlate with greater total gas desorption amounts and higher desorption rates. The desorption process exhibits distinct stages: the initial desorption amount is primarily influenced by the particle size, while the later stage is affected by the proportion of coal samples with different particle sizes. The desorption intensity for both single-sized and mixed-size samples decays exponentially over time, with the decay rate weakening as the proportion of small particles decreases. The gas diffusion coefficient decays over time during desorption, eventually approaching zero, and increases as the proportion of small particles rises. Conversely, the gas desorption attenuation coefficient increases with a higher proportion of fine particles. Based on the desorption laws of coal samples with single and mixed particle sizes, this study can be applied to coalbed gas content measurements, emission prediction, and extraction design, thereby providing a theoretical foundation and technical support for coal mine operations. Full article