Abstract To address the issue of high carbon dioxide (CO 2 ) and nitrogen oxides (NOx) emissions from cement precalciner kilns, this study employs CFD numerical simulation to investigate the co-combustion of pulverized coal and hydrogen in an RSP-type cement precalciner of a production line with a raw meal processing capacity of 3800 tons per day (t/d). The combustion behavior of the fuel (pulverized coal and hydrogen), the decomposition of raw meal, and the generation of CO 2 and NOx within the precalciner were analyzed under different Thermal Substitution Ratios (TSR) of hydrogen and varying numbers of hydrogen injection pipes. The research demonstrates that: As the TSR increases, the decomposition rate of CaCO 3 slightly decreases, while the precalciner outlet temperature experiences a minor increase. Concurrently, the concentrations of CO 2 and NOx inside the precalciner exhibit a decreasing trend. Consequently, the CO 2 mass flow rate and NOx concentration at the precalciner outlet progressively decrease. Under the condition of TSR = 50%, the number of hydrogen injection pipes has no significant effect on the CaCO 3 decomposition rate, precalciner outlet temperature, outlet CO 2 mass flow rate, or outlet NOx concentration.

This paper presents an Implicit Neural Representation method for Event-Based Imaging Velocimetry (INR-VG) to reconstruct dense velocity fields from sparse event streams. The core idea is to learn a mapping (multilayer perceptron) from spatial coordinates to flow velocities, v(x)=f(x;θ), which thereby enables dense velocity measurements at any desired spatial resolution. The neural network is optimized through test-time optimization by minimizing the alignment error between warped voxel grids of events. Extensive evaluations on synthetic datasets and real-world flows demonstrate that INR-VG achieves high accuracy (errors as low as 0.05 px/ms) and maintains robustness in challenging conditions where existing methods typically fail, including low event rates and large displacements, significantly outperforming optical-flow-based baselines. To the best of our knowledge, this work represents a successful application of implicit neural representations to event-based imaging velocimetry (EBIV), establishing a new paradigm for dense and robust event-based flow measurement. The implementation and experimental details are publicly available to support reproducibility and future research.

In a calcined clay rotary cooler, the flow behavior and heat transfer characteristics of the granular bed are key factors determining the cooling efficiency. In this study, an Euler-Euler multiphase model coupled with the kinetic theory of granular flow (KTGF) was used to simulate the granular bed flow and heat transfer in a rotating drum of a rotary cooler. Unlike conventional large-particle beds, the 11 mu m calcined clay particles interact more strongly with the gas phase, resulting in stratification and fluidization in the fine-particle bed. The effects of rotational speed, baffle configuration, and number of baffles on the flow and heat transfer behavior of the calcined clay granular bed were investigated. The results show that L-shaped baffles provide superior cooling, achieving a granular bed temperature and heat transfer coefficient (HTC) of 656.88 K and 151.15 W/(m2 & centerdot;K), respectively. At 2 rpm, the maximum temperature decrement and HTC increment are 5.73 K and 46.30 W/(m2 & centerdot;K), whereas excessive rotational speeds intensify bed fluidization. Additionally, increasing the number of L-shaped baffles has limited influence on expanding the fluidized region. With 12 L-shaped baffles, the temperature decrement peaks at 2.86 K and the HTC increment reaches a relatively high 33.27 W/(m2 & centerdot;K). This study provides a theoretical basis for the design and optimization of industrial-scale rotary cooling equipment for fine-particle beds.

Event cameras provide microsecond-level temporal resolution for brightness changes, which makes them particularly suitable for event-based imaging velocimetry (EBIV). However, accurately estimating velocity vectors from event data is challenging because of its inherent asynchronous and sparse nature. This work proposes a projection concentration maximization with smooth annealing (PCM-SA) method that estimates the velocity by maximizing the concentration of raw-event projections. Specifically, raw events are continuously projected as parameterized Gaussian kernels, yielding a smooth mixture-of-Gaussians (MoG) representation of event alignment. The degree of alignment is quantified using a continuous Simpson concentration index, which defines a fully differentiable objective with respect to the velocity parameters. To ensure robust optimization of this nonconvex objective, we adopt a smooth annealing strategy that initially enlarges the basin of attraction for Newton-Raphson iterations and gradually restores the original objective to recover a high-accuracy solution. Without relying on intermediate pseudoframes, PCM-SA directly estimates velocity by formulating event projection concentration as a continuous, differentiable objective defined on raw event data. Extensive experiments illustrate that PCM-SA consistently outperforms baseline methods on both synthetic and real event data. Remarkably, the EBIV pixel-locking effect is observed for the first time due to PCM-SA's improved accuracy. In addition, the implementation of our PCM-SA is publicly available for interested researchers. Overall, it provides a new avenue for event-based imaging velocimetry.

As a storage and transportation medium for hydrogen and a clean fuel with zero carbon emissions, ammonia (NH3) plays an important role in promoting hydrogen energy economy and renewable energy utilization. However, NH3 faces issues of low combustion intensity and the difficulties of ignition when used as a fuel. To address these problems, a novel combustion method with high temperature resistance and strong activity catalyst for NH3 pre-cracking is proposed in this paper. The cracking product, hydrogen, has a higher combustion rate, lower ignition temperature, and higher combustion intensity, which can improve the combustion characteristics of pure NH3. Firstly, Computational Fluid Dynamics was used to simulate the whole process of catalytic cracking and combustion of NH3. An Eulerian multiphase flow model with a granular phase was employed to simulate the catalyst particles and a porous medium to simulate the support carrier for the catalyst particles. Secondly, the Langmuir-Hinshelwood model was built using user-defined functions (UDF) to describe the reaction kinetic rates of the adsorption, cracking, and desorption processes of NH3 on the surface of Ni/Al2O3 catalyst. Then, species indexing in the flow field was implemented using UDF to couple the catalytic reaction rate with the surface coverage concentration to improve the simulation accuracy and reliability. Finally, the simulation results revealed that the catalytic pre-cracking combustion method can significantly improve the thermal efficiency and stability of NH3 combustion.

This study presents an enhancement to the Mountain Gazelle Optimizer (MGO) and proposes a new optimization algorithm—Mapping Mountain Gazelle Optimizer (MMGO). Through systematic experiments, we have validated the performance of the MMGO in addressing complex optimization problems. To further enhance optimization effectiveness, we integrated the new algorithm MMGO with Radial Basis Function (RBF) neural networks, resulting in the development of two optimization algorithm models: RBF_MMGO and RBF_MGO. In the practical application of robotic arm trajectory tracking control, we conducted a comprehensive performance evaluation and comparison of these two optimization algorithm models. Experimental results indicate that RBF_MMGO significantly outperforms RBF_MGO in terms of tracking accuracy and stability. This finding not only validates the effectiveness of MMGO in optimization problems but also demonstrates the application potential of optimization algorithm models in robotic arm control. Through comparative analysis, we discovered that the RBF_MMGO model exhibits greater adaptability in dynamic environments, enabling it to better cope with the challenges posed by trajectory changes. This model has shown higher accuracy and lower tracking errors when handling complex nonlinear systems. These advantages suggest that the MMGO has broader applicability and higher reliability in practical applications. This research provides theoretical insights into the MMGO's application and lays the foundation for advancements in robotic arm trajectory tracking control. It illustrates the feasibility of combining optimization algorithms with neural networks, offering innovative approaches for future research.

Spray cooling efficiency plays a critical role in the heat dissipation process from the external surface of industrial low-carbon cement rotary coolers. This study numerically investigated the thermal performance of high-temperature zones by examining four spray parameters: spray angle, nozzle distance, spray height, and mass flow rate. Multi-objective optimization design (MOD) was subsequently performed using response surface methodology (RSM). RSM reveals spray angle as the most significant parameter affecting heat transfer. With temperature uniformity as a constraint, MOD yields the following optimal parameters: 89° spray angle, 380 mm nozzle distance, and 663.5 mm spray height. This configuration achieves an average surface temperature of 814.33 K and a heat flux of 131,588.3 W/m2. The optimized spray parameters ensure high heat flux and uniform surface temperature while enlarging the heat transfer area and strengthening the synergistic heat transfer between dual nozzles. This approach provides a reliable technical pathway for efficient thermal management in industrial rotary cooler exteriors.

To enhance environmental and energy efficiency, ammonia-methane (NH3-CH4) co-combustion is considered one of the most efficient and clean energy supply methods. However, several challenges exist in NH3-CH4 co-combustion, including long ignition delay time, low combustion intensity, and high nitrogen oxide emissions. To this end, the co-combustion characteristics of NH3-CH4 mixtures were investigated numerically using a kinetic mechanism. Firstly, a novel kinetic model was developed that accurately predicts the co-combustion characteristics of NH3-CH4 mixtures. Moreover, the proposed mechanism is more concise and exhibits enhanced accuracy. Improvements in the mechanism structure compensate for the previously missing data across a wider range of conditions. The results show that as pressure increases, its influence on the ignition delay time gradually diminishes. Additionally, the variation trend of laminar flame speed aligns well with that of the OH radical, demonstrating a strong correlation between them. Compared with closed constant-volume combustion, the reaction pathways CH4-CH3-CH2(S)-(CH2-)CO, CH4-H2, and CH2O-CH2-CO are more pronounced in premixed laminar combustion. The latter emphasizes CH4-CH3-CH2O, whereas CH2O-H2 becomes less significant. Furthermore, the proportions of NH2-NNH and NH2-N2 increase by 5.1% and 5.0%, respectively, indicating a more balanced distribution of reaction pathways in laminar flame combustion. The formation of NO2 mainly originates from the oxidation of NO, with 12.6% of NO being oxidized to NO2, while 48% of NO2 is reduced back to NO. The highest NO concentrations are observed when the equivalence ratio ranges from 0.8 to 0.9 and the temperature from 1400 K to 1600 K. Fuel-rich combustion effectively reduces NOx emissions; when the equivalence ratio is 1.4, NOx can be reduced to 200 ppm.

Dust pollution generated by construction activities poses a serious threat to the health of workers. Studies have mainly focused on dust removal equipment, with relatively few studies on construction site layout planning (CSLP) for dust pollution reduction. This work designed a novel multiobjective CSLP model that integrates dust diffusion with the standard CSLP model (safety risk and transportation cost). After performance analysis between different solvers, our new model was solved by the popular Non-Dominated Sorting Genetic Algorithm III (NSGA-III). Furthermore, weight sensitivity analysis was also conducted to determine the optimal weights of model objectives. Finally, the practicality was validated through several construction examples. The experimental results indicated (1) the optimal layout had average reductions of 9.1% in safety risk, 4.3% in transportation cost, and 40.4% in dust pollution compared to the original layout; and (2) NSGA-III demonstrated superior convergence and diversity compared to other competitive algorithms under both hypervolume and inverted generational distance metrics. This study enhances dust reduction approaches in the construction industry by integrating dust control into the preconstruction optimization of CSLP, enhancing the accuracy of dust pollution predictions and offering practical solutions for dust mitigation.

To enhance environmental and energy efficiency, ammonia-methane co-combustion is considered one of the efficient and clean energy supply methods. However, the greatest challenge with the combustion of NH3 is NOx emissions. In this work, computational fluid dynamics (CFD) technology was employed to simulate combustion in a burner chamber. Additionally, a user-defined function (UDF) was used to construct the working condition fluctuation model and the species concentration coupling model. And experiments focused on detecting the composition of flue gas after combustion were carried out. To this end, a dynamic and precise denitrification method was proposed, and its performance was systematically compared with two other scenarios, namely non-denitrification and conventional fixed injection denitrification. The combustion and denitrification models employed in this work were verified by comparison with previous studies. The results showed that tail denitrification treatment effectively reduces NO emissions. Furthermore, the average NO concentration at the outlet decreased by 2,378 ppm through ordinary fixed value denitrification. However, this method demonstrated poor denitrification performance under fluctuating operating conditions. In contrast, the dynamic denitrification method can accurately control the average outlet NO concentration to about 73 ppm, reduced nitrogen oxides by 97%. In the end, the result was experimentally validated with an error within 5%.

Coal remains a primary energy source in many industries due to its stable combustion quality and low cost. However, the SO2 generated during combustion poses a significant environmental challenge. Consequently, the use of carbon-free and sulfur-free ammonia is proposed as a replacement for pulverized coal combustion. When combined with the dry calcium oxide desulfurization (DCOD) technique, SO2 emissions can be effectively controlled. In this work, the desulfurization process of mixed ammonia/powdered coal combustion in a burner has been numerically investigated using computational fluid dynamics (CFD) analysis. The response surface methodology (RSM) was employed to explore the effects of load (l), ammonia/coal ratio (r), and porosity (p) in the desulfurization zone on sulfur removal performance. Subsequently, the operating parameters were optimized through multi-objective optimization design (MOD). Results indicate that the load, ammonia/coal ratio, and porosity significantly affect the combustion performance of flue gas desulfurization (FGD). The established prediction model effectively forecasts the performance metrics. The optimized outlet SO2 concentration was reduced by 58.24 %, while pressure loss decreased by 34.69 %. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm addresses the weighted subjectivity inherent in the desirability function approach. This study proposes an interdisciplinary research approach utilizing an intelligent algorithm for ammonia/coal combustion. The simulation and optimization methods presented in this work are also promising for other similar applications.

Symmetric image deformation has been considered as the only method for achieving second-order accuracy in particle image velocimetry (PIV). However, two deformed images with interpolation errors might lead to a doubling of the measurement uncertainty. Alternatively, this work proposed a post-correction method (FDI2CDI) to correct the velocity results of asymmetric image deformation to second-order accuracy, aiming at reducing the random interpolation error because only one deformed particle image is required. Specifically, the implicit geometric relationship between asymmetric forward difference interrogation (FDI) and symmetric central difference interrogation (CDI) is derived. And the correction problem is thus modeled as a fixed-point problem, which is solved using iterative updates. Tested on several synthetic velocity fields, massive synthetic particle image pairs, and two captured recordings, our FDI2CDI method demonstrates fast convergence, noise robustness, and significant improvement in accuracy. Besides, our FDI2CDI method also exhibits strong generalizability across different one-pass displacement estimators, as shown through experiments with optical flow and cross correlation. In addition, we provide a publicly available repository of FDI2CDI, including all reported results for the interested practitioners. In summary, our FDI2CDI post-correction method revitalizes the asymmetric image deformation for more accurate PIV measurement.

Due to the gas-air premixing problem, it is a challenge to design a large-scale industrial burner with stable flame combustion and low pollutant emission. This work numerically investigates large-scale (10m height) burners equipped with two premixing elements—multiple ejector layout and turbulent swirl blades. Along with the computational fluid dynamics (CFD) and combustion simulation techniques, the well-known orthogonal experiment method is adopted to reveal the premixing performance and combustion temperature distribution for different design parameters. As a result, two optimized denitration burners for rectangular flue and circular flue are obtained with satisfactory premixing performance and combustion temperature uniformity. Thus, the optimized burner enhances the premixing effect of the gas, promotes stable combustion and reduces pollutant emissions.

Nowadays, since the air pollution problem is becoming global and denitrification is efficient to control nitrogen oxides, research and development of burners with low pollutant emissions in industries are urgent and necessary due to the increasingly severe environmental requirements. Based on the advanced CFD (computational fluid dynamics) numerical analysis technique, this work focuses on developing an industrial denitration-used burner, aiming to decrease the emission of nitrogen oxides. A burner with multiple ejectors is proposed, and the gas premixing and combustion process in the burner are systematically studied. Firstly, for the ejector, the well-known orthogonal experiment method is adopted to reveal the premixing performance under different structural parameters. Results show that the angle and number of swirl blades have significant effects on the CO mixing uniformity. The CO mixing uniformity first decreases and then increases with thr rising swirl blade angle, and it enhances with more swirl blades. Through comparison, a preferred ejector is determined with optimal structure parameters including the nozzle diameter of 75 mm, the ejector suction chamber diameter of 290 mm, the blade swirl angle of 45∘, and the swirl blade number 16. And then, the burners installed with the confirmed ejector and two types of flues, i.e., a cylindrical and a rectangular one, are simulated and compared. The effects of ejector arrangements on the temperature distributions at the burner outlet are analyzed qualitatively and quantitatively. It is found that the temperature variances at the outlets of R2 and C1 are the smallest, respectively, 13.12 and 23.69, representing the optimal temperature uniformity under each type. Finally, the burner of the R2 arrangement is verified with a satisfied premixing performance and combustion temperature uniformity, meeting the denitration demands in the industry.

Vertical stirred mills (VSM) are widely used for powder processing in many situations like mechanical alloying preparation and raw material crushing and shaping. Many structural and operational parameters like stirrer helix angle and rotating speed have great significance on VSM performance, especially in a large industry-scale situation. Therefore, it becomes essential to investigate these parameters systematically to obtain high energy efficiency and good product quality. In this work, the discrete element method (DEM) was used to examine the effects of stirrer helix angle (α), stirrer diameter (d), and rotating speed (n) on the grinding performance in an industrial VSM, and then the response surface method (RSM) was employed for multi-objective optimization in the VSM. It is found that a media vortex phenomenon may happen near the stirring shaft. The media collisions are significantly influenced by α, d, and n. Through multi-objective optimization design (MOD), the power consumption (P) of the stirrer reduced by 8.09%. The media collision energy (E) increased by 9.53%. The energy conversion rate (R) rises by 20.70%. The collision intensity and frequency are both improved. This optimization method can help determine good operating parameters based on certain structures.

采用小孔节流的平面静压气浮运动平台由于其精度高、成本低等特点被广泛应用于精密设备当中,但相对于传统轴承存在承载力和刚度低的问题,很难将其应用到高速重载的设备中.针对以小孔节流的平面静压气体轴承承载力和刚度低的问题,通常采用开设气腔和均压槽来实现高的承载力,但是气腔的结构也会对气体静压轴承产生不同影.响.文中采用Fluent软件对比分析有无气腔结构对气浮平台承载能力及刚度的影响,并分析圆柱型气腔结构下气体轴承稳定性.研究结果表明:增加气腔可以提高平面静压气体轴承承载特性,但是由于开设气腔结构导致高压、气体在气腔处产生涡流,造成气浮导轨变得不稳定.在圆柱型气腔结构中供气压力越大,气体轴承稳定性越差;节流孔直径越小,气体轴承稳定性越差.

引射式燃烧器的结构参数与燃气和空气预混效果之间存在非线性关系,难以获得解析表达式,而预混效果又是燃烧器的关键评价指标.为了设计预混效果更好的引射式燃烧器,利用CFD进行模拟仿真计算,并结合正交试验的方法对燃烧器的结构参数进行了优化设计,以燃烧器出口截面燃气和空气混合不均匀度为目标函数,得到了燃烧器的燃气喷嘴内径、一次烟气入口内径以及旋流叶片角度和数量的最优参数组合,优化后燃烧器出口截面的混合不均匀度降至0.015 34.

This work concentrates on the energy consumption and grinding energy efficiency of a laboratory vertical roller mill (VRM) under various operating parameters. For design of experiments (DOE), the response surface method (RSM) was employed with the VRM experiments to systematically investigate the influence of operating parameters on the energy consumption and grinding energy efficiency. The prediction models of energy consumption (Ecs) and grinding energy efficiency (η) were established respectively with the operating parameters (loading pressure, rotation speed and moisture content). Analysis of variance (ANOVA) was performed to obtain useful knowledge in designing operating parameters. Moreover, the multi-objective optimization design (MOD) method was conducted to seek out the optimal parameters of the VRM, and a set of optimal parameters was gained based on the desirability approach by Design-Expert. It is proved that the optimized prediction results match the experimental results well, which indicates this research offers a reliable guidance for reducing energy consumption and improving grinding energy efficiency.