As one of the indispensable numerical tools in chemical engineering, Computational Fluid Dynamics (CFD) has been widely used in multiphase flow, heat transfer, mass transfer, chemical reaction processes, as well as the design, development and structural optimization of various chemical equipment. However, high-fidelity CFD simulations usually require significant computational resources, which limits their wide application in practical engineering. Consequently, field reconstruction techniques based on high-precision CFD data have become a research hotspot. This study reviews recent advancements in CFD field reconstruction, emphasizing the significant contribution of Machine Learning-based Reduced-Order Model (ROM-ML) and multi-fidelity approaches. These methods avoid the expensive Navier-Stokes equation solving by constructing surrogate models, significantly enhancing efficiency and reducing time costs. This paper discusses the principles behind three main reduced-order models: Proper Orthogonal Decomposition (POD), Dynamic Mode Decomposition (DMD) and Autoencoders (AE), and their integration with machine learning techniques. Furthermore, we also discuss the application of multi-fidelity methods in field reconstruction, including data-driven and image-based methods (e.g., ROM-based methods, diffusion models, and transfer learning). Finally, the review outlines the current challenges and future directions in CFD field reconstruction. ROM-ML efficiently reconstructs steady and transient flows, while DMD excels in time-dependent cases. Multi-fidelity methods reduce dependence on high-fidelity data, but challenges remain in data fusion, uncertainty quantification, and model interpretability. Enhancing surrogate extrapolation, integrating experimental and numerical data, and linking latent features to physical flow structures are key areas for future research.

Targeted regulation of metal-atom configurations is an effective strategy to modulate electronic structure and enhance peroxymonosulfate (PMS) activation. In this study, cobalt atom with an asymmetric coordination of five nitrogen atoms and one oxygen atom (Co-N5O1) anchored on oxygen-doped carbon nitride (OCN) surfaces was synthesized to activate PMS. Experimental and computational results revealed that the asymmetric N, O coordination of Co atoms not only facilitated PMS adsorption and activation, thereby generating more sulfate (SO4•-), superoxide radical (O2•-), and singlet oxygen (1O2) radicals; but also enabled dissolved oxygen to participate in radical generation and promoted the electron transfer from contaminants to the surface-bound PMS complexes. Under the main actions of SO4•-, 1O2, and electron transfer, the Co-N5O1/OCN + PMS system demonstrated remarkable degradation efficiency, achieving nearly 100% degradation for both electron-donating (ciprofloxacin, bisphenol A, sulfamethoxazole, 4-chlorophenol, tetracycline) and electron-withdrawing (p-nitrophenol, p-nitrobenzoic acid, metronidazole) pollutants at a concentration of 10 mg L-1. In a continuous-flow operation, 90% of ciprofloxacin was removed within 150 min (100 mL solution) with minimal metal leaching (< 0.3 mg L-1). This study elucidates the critical role of metal atom coordination in PMS activation and offers a promising catalyst for various types of contaminants degradation.

The conventional urea-based process for hydrazine hydrate production faces challenges including low product yield and high energy consumption. To overcome these limitations, we propose an innovative integrated approach combining jet reactor technology with membrane separation, further enhanced through heat network optimization. Through process simulation and sensitivity analysis, the following optimal distillation parameters were identified: nine theoretical stages, feed entry at the fifth stage, a reflux ratio of 0.6, and a distillate flow rate of 354 kg/h. Systematic optimization of the heat exchanger network (HEN) using pinch technology achieved substantial energy savings, reducing hot utility consumption by 66.8% (to 1317 MJ/h) and cold utility usage by 62.7% (to 1503 MJ/h). The redesigned HEN prioritized temperature-cascaded heat recovery, enabling 67% energy recuperation from exothermic reaction streams. Operational costs decreased by 12%, underscoring the economic viability of coupling process intensification with thermal integration. This work establishes a sustainable framework for hydrazine hydrate synthesis, balancing industrial feasibility with reduced environmental impact in chemical manufacturing.

In this paper, a novel venturi jet reactor is innovatively proposed for the process of hydrazine hydrate production using the urea method. In order to investigate the performance of this reactor in depth, we used the computational fluid dynamics method to optimize the design of the structure of the new venturi jet reactor based on the flow field condition, the degree of mixing uniformity, and the efficiency of the reactor using the component transport model. The results showed that the moderate increase of the distance of mixing tube to nozzle and nozzle diameter seven could help to improve the efficiency of the jet reactor; however, in terms of the mixing effect, the increase of the distance of mixing tube to nozzle led to the mixing effect to be enhanced and then weakened, while the increase in the nozzle diameter was not conducive to the full mixing of the two fluids. In addition, the effects of ratio of throat length to diameter and constriction angle on the efficiency of the jet reactor showed nonlinear characteristics, and the optimal values existed in the study range. Based on the above analysis, this paper determines the optimal range of structural parameters, i.e., the distance of mixing tube to nozzle of 7–13 mm, the nozzle outlet diameter of 5–7 mm, the ratio of throat length to diameter of 3–5, and the constriction angle of 30–40°, and the study provides guidance for the industrial application of the venturi jet reactor.

Efficient separation of C-2 hydrocarbons remains a significant challenge in the petrochemical industry. Metal-organic framework nanostructures, with their tunable pore environments and exposed nanoscale metal sites, have shown great promise in enhancing pi-electron gas separation. However, balancing molecular adsorption strength with desorption energy consumption continues to be a major challenge. In our previous work, we discovered that the gallate-based metal-organic framework nanostructure compounds exhibit selective adsorption of C2H4 through pi-electron-induced local reconstruction. Building on this, we have proposed a '' dynamic water molecule-driven competition '' strategy in this study, in which the introduction of water molecules enables dynamic regulation of gas binding at the active metal sites. By leveraging the differential responses of M-gallate (Co, Ni, Mg) to various gas molecules, we achieved high-efficiency C-2 separation and low-energy regeneration under dynamic adsorption-desorption competition modulated by water molecules. Experimental results, supported by DFT simulations, reveal that water molecules act as molecular modulators, reversibly competing with pi-electron gases for binding near metal centers. This nanostructure-level competition mechanism significantly enhances gas separation performance and material recyclability. The '' dynamic water molecule-driven competition '' strategy proposed in this study not only opens up directions for the design of pi-bonded gas separation materials but also provides an important technical reference for greening and decarbonizing industrial adsorption processes.

The rising environmental issues caused by carbon dioxide emissions and accumulation of industrial solid waste accelerate the development of carbon capture utilization and storage (CCUS), especially the technology using industrial solid waste as a raw material to prepare environmentally friendly and sustainable porous materials to capture CO2. This study developed four models including support vector regression(SVR), multivariate adaptive regression spline(Mars), random forest(RF), and gradient boosting machine(GBM) based on 762 CO2 adsorption datasets of zeolites synthesized from five different industrial solid waste materials to predict the CO2 adsorption capacity and analyze impact of various factors on CO2 adsorption performance during synthesis and adsorption processes. The results suggested that gradient boosting machine(GBM) and the support vector regression(SVR) have good accuracy and generalization performance. The R2 of the model reached 0.99 and 0.96 respectively, which is in good agreement with the laboratory data. In general, the specific surface area(S) and adsorption pressure(P) during the adsorption process of zeolite have a great influence on the final adsorption performance. The correlation between the specific surface area(S) and the hydrothermal reaction temperature(T2) is the largest, and its Pearson Correlation Coefficient is 0.61. This study paved a new approach for the accumulation treatment of industrial solid waste and low-carbon industry via statistical analysis and machine learning method, which is beneficial to environmental protection and sustainable development.

Tetracycline hydrochloride (TC) is a widely applied antibiotic. However, its instability requires overdose upon practical application, which raises the risk for the evolution of superbacteria. The solution to this matter lies in reducing the dosage of TC while maintaining its efficacy, which can be solved from two ways: 1) increasing the stability of TC; 2) improving the antibacterial ability of TC. Here in this study, with the aim to improve the stability of TC while with additional antibacterial ability, TC was assembled with ferric ion and polyvinylpyrrolidone (PVP) into TC multifunctional nanoparticles (TCFNPs). The Box-Behnken Design (BBD) was firstly introduced to recognize the crucial formulation variables on the influencing of particle size of TCFNPs. Under the optimized condition, TCFNPs with size of 9.95 nm can be obtained and showed high storage stability. The as-prepared TCFNPs not only showed comparable chemotherapeutic effects to free TC on both Gram-positive strain (Staphylococcus aureus, S. aureus) and Gram-negative strain of (Escherichia coli, E. coli), but also showed improved stability under room temperature upon light irradiation. Moreover, TCFNPs also exert photothermal effects which can synergistically provide enhanced antibacterial performance due to the combination of chemotherapy and photothermal therapy. Therefore, our study represents a new way to reform antibiotics with better stability and additional antibacterial ability for the better application of traditional antibacterial in agriculture.

In this paper, a discretization-free approach based on the physics-informed neural network (PINN) is proposed for solving the forward and inverse problems governed by the nonlinear convection-diffusion-reaction (CDR) systems. By embedding physical information described by the CDR system in the feedforward neural networks, PINN is trained to approximate the solution of the system without the need of labeled data. The good performance of PINN in solving the forward problem of the nonlinear CDR systems is verified by studying the problems of gas-solid adsorption and autocatalytic reacting flow. For CDR systems with different Péclet number, PINN can largely eliminate the numerical diffusion and unphysical oscillations in traditional numerical methods caused by high Péclet number. Meanwhile, the PINN framework is implemented to solve the inverse problem of nonlinear CDR systems and the results show that the unknown parameters can be effectively recognized even with high noisy data. It is concluded that the established PINN algorithm has good accuracy, convergence, and robustness for both the forward and inverse problems of CDR systems.

Aiming at the structural characteristics of Qinghai’s low-grade salt mine, a brine-replenishing pipe-driven solution mining device was designed, and the structural parameters of the brine-replenishing pipe were optimized. The laws of fluid seepage, salt dissolution and ionic migration in the deposit were simulated and analyzed. The results show that, the solution mining scheme adopts the up-down arrangement mode. The optimal design form of the brine-replenishing pipe employs the arrangement of holes in two rows.The angle between two rows of holes is 120°. The diameter of the hole is 5 mm and the hole center distance is 25 mm, The uniformity of seepage velocity in section can reach above 0.87. In the vertical direction of the model, the dissolution rates of NaCl, KCl and MgCl2generally show a trend that the upper is fast and the bottom is slow. After eight hours, the dissolving-out amount of NaCl and KCl are 23.25% and 92.65%respectively, which can dissolve KCl better under the premise of ensuring the integrity of salt mine.

放射废液经过长期贮存会析出可溶盐沉淀(多为硫酸盐),采用液体射流搅拌使沉积于罐底部的沉淀物得到充分搅动,喷嘴性能对于搅拌效果影响较大,采用响应面法对喷嘴结构进行优化设计,得到优化后的喷嘴结构参数为收缩角18°,长径比为1.6,喷嘴进出口直径比为2.优化后的喷嘴负压区域增大,且对泥浆相的清理效率得到提高,相比对照组所需的射流清理时间减少了10.4％左右.

It has become obvious that fluorinated drugs have a significant role in medicinal applications. In this study, the fluorination of mirtazapine antidepressant drug was investigated using density functional theory calculations. We found that the intramolecular hydrogen bonding and charge transfers of the mirtazapine drug were influenced by fluorine substitution. Our results also reveal that the fluorination altered the stability, solubility, and molecular polarity of the mirtazapine antidepressant drug. Moreover, our results show that the electronic spectra of fluorinated derivatives of the mirtazapine exhibit a red shift toward higher wavelengths compared to the original antidepressant drug. Our calculations show that the difference between G value of the gas and water (ΔG) of fluorinated derivatives of the mirtazapine drug was negative. We also found that the fluorination can increases the first hyperpolarizability of the mirtazapine antidepressant drug. Our results present an efficient strategy to improve the nonlinear optical responses of the antidepressant drugs. Consequently, the results of present study show that the fluorination of mirtazapine could be considered as a promising strategy to design antidepressant drugs with better pharmacological properties.Communicated by Ramaswamy H. Sarma.

The low power output of microbial fuel cell (MFC) requires high-performance catalysts to facilitate cathodic oxygen reduction reaction (ORR). In this work, a series of electrocatalysts (Fe/TTF) are synthesized by thermalizing N-rich covalent triazine framework (CTF) with various amount of FeCl3 center dot 6H(2)O at different calcination temperatures for ORR catalysis in MFC. Electrochemical measurements reveal that Fe/TTF-0.5-900 synthesized with a mass ratio of 1:2 (FeCl3 center dot 6H(2)O: CTF) at 900 degrees C exhibits the minimum charge transfer resistance and optimal catalytic behavior in ORR process. The MFC with Fe/TTF-0.5-900 cathode displays a high maximum power density of 2617 mW.m(-2), which surpasses those of other Fe/TTF electrocatalysts, thermalized covalent triazine framework (TTF) and the benchmark activated carbon (AC). The results indicate that TTF as a porous carbon matrix with favorable nitrogen doping promotes charge and mass transfer greatly, and the combination of iron carbide substantially benefits the improvement of MFC performance. Moreover, according to density functional theory (DFT) calculation, iron carbide is significantly preferable for ORR catalysis by accelerating the break of O-O bond in *OOH, which further reveals that the ORR on iron carbide follows four-electron pathway. Therefore, Fe/TTF-0.5-900 can be applied as an economical and highly active electrocatalyst in MFC to achieve high-efficiency energy conversion.

基于70 MPa加氢站工艺过程,对加氢站标准氢气冷却器提出一种窄流道、紧凑型错流氢气冷却器芯体结构,以应对其超高压、撬装式的工作特点.采用数值模拟方法对冷却器芯体结构参数进行分析与优化.结果表明:氢气孔径为6 mm的芯体体积最小,其满足强度需求的最优氢气孔水平孔心距为15 mm、交错孔垂直孔心距为12 mm,满足热负荷需求的最优氢气流通长度为260 mm.

基于CFD-DEM(计算流体力学-离散单元法),结合传热和传质模型,探究了气体速度、气体温度和粒径大小对高聚物颗粒干燥特性的影响规律.结果 表明:颗粒干燥过程主要发生在恒速干燥阶段,随着气体速度和气体温度的增大或颗粒粒径的减小,颗粒干燥速率逐渐增大,干燥时间逐渐减少;拟合模拟数据得到MR=aexp(-ktn)模型能够很好地描述颗粒的失水规律.

针对工业废气对环境造成巨大危害的问题,为提高脱白和VOCs废气处理效率,提出了三维网胞结构,并利用Fluent软件对该结构进行优化,研究了不同丝径、目数、层数的三维丝网结构对压降和水滴捕集效率产生的影响.结果表明:丝径和目数的增加会使压降增加,而水滴的捕集效率是先增加然后趋于稳定;随着层数的增加,压降减小,而水滴的捕集效率变化不大.

基于二氧化铀预氧化工艺,提出了一种平行板反应器,采用数值模拟的方法对板片动力响应的影响因素和规律进行了研究,并据此对板片组进行了合理优化.结果表明:侧夹持板片的应力与振打力和弹簧刚度系数成正相关,与夹持件宽度和厚度成负相关;板片表面加速度与振打力成正相关,与其他因素成负相关.相比于角夹持板片,侧夹持板片的结构强度和脱落率更高.优化后板片组的应力满足强度要求,脱落率达到100％.

针对传统干燥过程存在的弊端,设计开发了回转锥筒非相变脱水干燥机,将多孔材料内衬到回转锥筒,通过多孔材料毛细作用快速吸附湿物料携带的表面水分,以达到物料快速脱水的目的.为了研究多孔材料的吸附性能,应用描述多孔介质内部液相毛细上升高度随时间变化的数学模型进行数值计算,研究了多孔材料颗粒直径以及孔隙率对毛细吸水过程的影响.在理论研究的基础上,以多孔陶瓷为研究对象,研究数学模型的可靠性并分析多孔材料毛细吸水特性的影响因素.结果表明:数值计算与实验结果有较好的一致性,颗粒直径与多孔材料孔隙率是影响毛细吸水特性的主要因素.颗粒直径越大,多孔材料毛细吸水速率越大,毛细吸水高度越低.这是因为渗透率k与颗粒直径d的平方成正比关系,颗粒直径越大则渗透率也大,因此其毛细吸水速率大;同时颗粒直径越大,其内部孔隙结构相对越大,较大的孔隙结构使毛细水上升通道变大,因此其毛细吸水高度越低.孔隙率越大,多孔材料毛细吸水速率越大,毛细吸水高度越低.这是因为渗透率k随孔隙率ε 的增大而增大,因此其毛细吸水速率越大;同时在颗粒直径相同的情况下,孔隙率越大其内部孔隙结构相对越大,较大的孔隙结构使毛细水上升通道变大,因此其毛细吸水高度越低.

基于计算流体力学-离散单元法(CFD-DEM)探讨了不同振动参数对颗粒跳动高度、碰撞次数和颗粒停留时间的影响规律.结果表明:随着振动强度的增加,颗粒与气体分布板的碰撞点逐渐后移;颗粒跳动高度随振动参数的增大而呈线性增大;随着振动频率、振动幅值的增大和振动角度的减小,颗粒碰撞次数减少,颗粒停留时间缩短.

在小空间内利用普通不锈钢材料,开发高效传热与安全可靠的高压微通道错流换热器.基于有限元分析进行换热设备结构优化,提出加氢机用新型制式换热器设计方法.建立微通道换热管力学模型,对比经肋板结构强化和自增强处理前后管壁应力分布,结果表明:在70MPa工作压力下管壁最大应力下降了61.82MPa,安全裕量0.75,应力分布更加合理,满足设计需求.