从多尺度到介尺度——复杂化工过程模拟的新挑战

化工过程普遍面对具有多尺度结构的复杂系统,而作为从基本单元相互作用形成系统整体行为与功能的关键环节,介尺度结构对化工过程的定量描述和定向调控具有重要意义。同时,化学、材料、生物、物理和系统科学等领域也都逐步认识到各自的介尺度问题及其共同特性。这表明对介尺度结构与行为共性的深入研究将对科学界产生全局性的影响,同时也表明这样的研究必须通过多学科充分交流、紧密合作才能取得重大进展。本文试图从多尺度研究的背景出发探讨化工及相关过程中介尺度模拟的意义、挑战和方法,并展望其发展。     

多尺度离散模拟在钢铁行业技术研发中的应用

为实现“双碳”目标,钢铁行业正面临紧迫的转型升级,高精度、高效率的数值模拟在推动其工艺智能化和绿色化方面可以发挥重要作用。本工作讨论了基于问题、模型、软件和硬件逻辑与结构一致的EMMS范式实现高性能多尺度离散模拟的可能。概述了相关软件在钢铁行业设备结构和操作条件优化方面的初步应用,包括铁矿石原料分选、烧结矿竖冷炉结构优化、钢渣滚筒处理优化和炼铁高炉操作优化等,展示了该模拟方法在钢铁行业的应用潜力,并展望了进一步结合在线测量、人工智能、人机虚拟交互和在线控制实现钢铁行业虚拟过程工程的前景。要点：(1) EMMS范式将提升模拟的精度、性能和效率。(2)基于EMMS范式的多尺度离散模拟可从颗粒尺度模拟钢铁行业工业设备。(3)多尺度离散模拟可作为钢铁行业虚拟过程工程的核心计算引擎。     

气固颗粒系统模拟的研究进展

综述了气固颗粒系统模拟方法的发展状况。首先,从宏观、中观及直接数值模拟角度对基于欧拉-欧拉架构下的双流体模型、拉格朗日-欧拉架构下的离散气泡模型以及欧拉-拉格朗日架构下的计算流体力学和离散单元法耦合模型(CFD-DEM)及直接数值模拟方法就模型基本原理、计算量、预测精度以及应用前景等方面进行对比和分析。接着,针对工程应用前景较强的双流体模型和CFD-DEM模型进行了详细阐述,着重强调了欧拉架构下的模型植入过程,颗粒相的动力学过程和微观行为描述、两相耦合作用及相间耦合模型描述。基于欧拉-拉格朗日架构下的多相耦合模型的普适性,模型的应用已经从研究尺度的模拟逐渐走向工程尺度的模拟,成为今后研究多相颗粒系统的热点。     

基于介尺度稳定性条件的多相流曳力与群体平衡模型

介尺度结构和介尺度机制是化工、冶金、能源等过程工程中的重要科学问题。尽管多相流数理模型在过去的几十年中已取得长足进展，但仍存在准确性依赖可调参数、模型适用性有限、计算量大等问题，难以适应当前快速发展的新工艺和新过程开发的需求。实际上，基于平均化方法的多流体方程组需要若干子模型封闭，如相间作用力、聚并/破碎核函数以及湍流模型等；这些子模型决定了多流体模型的模拟准确性。从介科学角度发展介尺度物理模型为解决这些问题提供了新的思路。模型可以解析多相流非均匀结构演化的控制机制，进而改进或重构子模型。总结了基于介尺度稳定条件的两类介尺度封闭模型：一类用于封闭相间动量传递，如介尺度曳力；另一类用于封闭离散相特征参数的演化，如介尺度群体平衡模型，计算气泡或液滴尺寸。进而综述了这些模型在流化床、鼓泡塔、气升式环流反应器、搅拌槽、转定子乳化器等多相流设备中的应用，并展望了未来发展方向和关键科学问题。     

微流控乳液模板法构建功能微颗粒过程中介尺度结构定向调控的研究进展

功能微颗粒材料因其微型化和多功能化等优点而在诸多领域具有广泛的应用。微流控技术可控制备的多样化乳液液滴体系为功能微颗粒材料的创新设计与可控制备提供了优良而独特的模板。深入研究乳液模板法构建功能微颗粒材料过程中介尺度结构的形成与演变规律，以及液滴界面介尺度结构与乳液动力学行为、界面传质与反应耦合对微颗粒介尺度结构的影响规律等，对于实现乳液模板结构调控与新型功能微颗粒材料创新制备具有重要意义。本文主要综述了微流控乳液模板法构建功能微颗粒过程中介尺度结构定向调控的研究进展，着重涵盖了两方面内容：（1）微流控法可控制备乳液模板的过程中，液滴界面两亲分子聚集态介尺度结构的调控与液滴运动、吞并、融合、相界面定向演变等动力学行为之间的相互影响关系和调控机制，以及上述调控对液滴形貌、结构和组成的影响规律；（2）乳液模板制备功能微颗粒的过程中，界面传质、反应，及两者耦合对微颗粒介尺度结构的定向调控，以期为新型功能微颗粒材料的高效制备与性能强化提供科学指导。     

反应传递多尺度耦合的拟颗粒模拟

反应与传递过程的耦合是众多化工过程的重要特征之一。随着过程精准调控要求的不断提高和过程强化与微化工等领域的迅速发展,传统上宏观与微观分离的反应与传递描述方式遇到了诸多挑战。拟颗粒模拟耦合软球和硬球两类分子模拟方法的优势,显著简化了分子间作用模型,极大提高了计算效率,为描述宏微观之间的介尺度上反应与传递紧密耦合的复杂现象提供了一种有效手段。本文简要回顾该方法提出的背景,阐述其基本思想,并展示和分析其在气固多相吸附和催化、气液微流动等问题中的应用前景。     

磁场对湿颗粒流化床系统中介尺度结构影响机制研究

湿颗粒系统在自然界及工业过程非常普遍,例如喷雾造粒、反应器中矿物黏结、催化以及制药等,这其中含有大量典型介尺度结构如颗粒聚团、结块以及气泡等结构,这些结构的存在导致颗粒系统的流动及热质传递特性发生明显改变。针对鼓泡流化床湿颗粒系统中颗粒聚团以及气泡等介尺度结构,应用离散单元模型并引入外加磁场,研究磁场作用下湿颗粒系统中介尺度结构的演化机制,探究磁场力、液桥力、接触力对气泡演化过程的影响。研究发现,在不考虑磁场的条件下,颗粒易形成聚团并存在气泡边界不规则等现象,引入外加匀强磁场后,磁场力对鼓泡流化床内气泡结构存在破坏和抑制作用。     

基于双尺度耦合模拟的甲烷化催化剂多级孔结构探究

以固定床甲烷化反应器中的催化剂颗粒为研究对象,建立了床层-颗粒双尺度耦合模型。针对甲烷化过程反应迅速、催化剂颗粒中内扩散影响显著的特点,探讨了颗粒大孔平均孔径、介孔平均孔径、大孔孔隙率和介孔孔隙率对甲烷化反应结果的影响,提出了多级孔结构催化剂的优化设计,以实现甲烷化过程的强化。模拟结果表明:双尺度耦合模型能较好地在床层尺度下探讨催化剂颗粒的微观孔隙结构对反应结果的影响规律;针对直径为5.4mm的球形颗粒,在模拟的生产条件下,反应结果对大孔孔隙率和平均介孔直径较为敏感;采用具有大孔和介孔的双模态催化剂颗粒可获得较高的甲烷产率。     

散斑能见度光谱法测量筒仓内颗粒流的颗粒温度

筒仓内颗粒流在化工生产中广泛应用,准确描述颗粒流的动力学规律对于调控化工反应过程中的混合和传输效率极为重要。颗粒温度是影响颗粒流的重要参数之一,为此搭建了基于线阵CCD相机的散斑能见度光谱测量装置,选取均值粒径分别为0.94和1.55 mm的球形颗粒进行实验。通过测量卸料过程中筒仓内颗粒流的时变颗粒温度,发现了离散颗粒运动在介尺度条件下具有稳定性。进一步,对比两种粒径颗粒的颗粒温度值,观察到稳态流动中大粒径颗粒具有更高的能量耗散,从而建立了宏观质量流率与介观颗粒温度之间的联系。通过分析筒仓内颗粒温度场的分布特征,发现了孔口附近的离散颗粒存在定向有序的运动。最后,根据筒仓内颗粒流堵塞过程中的颗粒温度变化曲线,揭示了颗粒流堵塞的弛豫变化规律。实验结果揭示的筒仓内颗粒流的运动规律,为完善化工生产中颗粒材料的存储与运输提供了参考数据。     

基于重置温度方法的双参数介尺度气固传热模型构建

颗粒聚团等介尺度结构在气固两相流中普遍存在,这些介尺度非均匀结构直接影响气固流动特性及气固接触效率,进而影响气固相间传热、传质及化学反应过程。在更适合工业大尺度气固传热模拟的粗网格方法中缺乏准确度高、简单易用且可以考虑介尺度非均匀结构影响的传热模型。采用计算流体力学-离散单元法（CFD-DEM）研究了气固两相流相间传热,为了保证气固相间持续传热,采用了两种维持气固相间传热温差的方法,并讨论了两种方法的优缺点。方法一：给气相能量方程添加热源项;方法二：每间隔一段时间重置气相温度,重置温度后气固两相自由传热,两种方法中均保持固相温度不变。结果表明聚团界面位置的局部单位体积气固传热量最大,重置温度方法在稀相和界面位置的局部单位体积传热量与总单位体积传热量之比大于热源项方法,而在浓相位置的局部单位体积传热量与总单位体积传热量之比小于热源项方法。通过过滤CFD-DEM计算数据,为重置温度方法构建了双参数（过滤固含率、过滤尺度）传热系数修正因子模型,通过先验分析评价了模型的表现,研究表明所构建模型在过滤网格尺度为5~40倍颗粒直径范围内优于文献中已有的双参数模型。     

Multiscale molecular modeling in nanostructured material design and process system engineering

Atomistic-based simulations such as molecular mechanics, molecular dynamics, and Monte Carlo-based methods have come into wide use for material design. Using these atomistic simulation tools, we can analyze molecular structure on the scale of 0.1–10 nm. However, difficulty arises concerning limitations of the time and length scale involved in the simulation. Although a possible molecular structure can be simulated by the atom-based simulations, it is less realistic to predict the mesoscopic structure defined on the scale of 100–1000 nm, for example the morphology of polymer blends and composites, which often dominates actual material properties. For the morphology on these scales, mesoscopic simulations such as the dynamic mean field density functional theory and dissipative particle dynamics are available as alternatives to atomistic simulations. It is therefore inevitable to adopt a mesoscopic simulation technique and bridge the gap between atomistic and mesoscopic simulations for an effective material design. Furthermore, it is possible to transfer the simulated mesoscopic structure to finite elements modeling tools for calculating macroscopic properties for the systems of interest.In this contribution, a hierarchical procedure for bridging the gap between atomistic and macroscopic modeling passing through mesoscopic simulations will be presented and discussed. The concept of multiscale (or many scale) modeling will be outlined, and examples of applications of single scale and multiscale procedures for nanostructured systems of industrial interest will be presented. In particular the following industrial applications will be considered: (i) polymer-organoclay nanocomposites of a montmorillonite–polymer–surface modifier system; (ii) mesoscale simulation for diblock copolymers with dispersion of nanoparticels; (iii) polymer–carbon nanotubes system and (iv) applications of multiscale modeling for process systems engineering.     

Dynamics of gas–solid fluidised beds with non-spherical particle geometry

Fluidised beds play an important role in physical and chemical engineering processing. Understanding the granular motion within these beds is essential for design, optimisation and control of such processes. Motion on the particle scale is difficult to measure experimentally, making computational simulations invaluable for determining the dynamics within such systems. Computational models which have had the greatest success at capturing the full range of dynamics are coupled discrete element model and Navier–Stokes solvers, based on a pressure-gradient-force formulation. However, most discrete element models assume spherical geometry for the particles. Particle shape in many important industrial processes, such as catalysis and pyrolysis, is often non-spherical. We present a re-formulation of the pressure-gradient force model, based on a modified pressure correction method, coupled to a discrete element model with non-spherical grains. The drag relations for the coupling are modified to take into account the grain shape and cross-sectional area relative to the local gas flow. We show that grain shape has a significant effect on the dynamics of the fluidised bed, including increased pressure gradients within the bed and lower fluidisation velocities when compared to beds of spherical particles. A model is presented to explain these effects, showing that they are due to both decreased porosity within the bed as well as the relative particle cross-sectional area creating a greater net drag over the bed. Our findings will be of interest from an applied standpoint as well as showing fundamental effects of particle shape on coupled fluid and granular flow.     

Chemical Engineering Education in the Next Century

This paper summarizes the work of the EFCE Working Party Education (WPE) over the last decade and attempts to identify effective educational solutions to meet the challenges caused by the rapid rate of change in technology and society world‐wide. The paper uses the results of the 1994 WPE survey of curricula in European Chemical Engineering Universities to identify a first degree level core curriculum. The problem of how to adapt the discipline to meet technological and societal changes without losing its identity is addressed. Basic sciences, chemical engineering science, integrated systems design and holistic thinking are emphasized as essential elements of the discipline. The paper discusses how Safety, Health and Environment (SHE), biotechnology, computerized models, product design, sustainability and other new subjects have been incorporated into chemical engineering curricula since the original survey. A simple model of the education process is presented to indicate how students might obtain a chemical engineering understanding and mindset. The paper explains how chemical engineering evolved from its origins in the petrochemical, heavy chemical and nuclear industries, to its current wide range of applications in industries, such as fine chemicals, food, pharmaceuticals, software, and cybernetics. It is suggested that the impact of changes arising from industry, new technology and society has driven the chemical engineering discipline to a point where it is now ripe for re‐invention. The effects of rapid industrial, technological and societal change on chemical engineering education are studied against the backdrop of a discipline on the threshold of a significant change. The paper concludes by identifying curriculum development, personal development and life‐long learning as three important factors for educating chemical engineers for a successful future.     

Techniques for the reconstruction of a distribution from a finite number of its moments

The reconstruction of a distribution knowing only a finite number of its moments is an extremely important but in practice still unsolved question for many fields of science (chemical and process engineering, electronic engineering, nuclear physics, image analysis, biotechnology…). Several methods have been proposed and corresponding mathematical formulations have been introduced in the literature during the last decades. Nevertheless, all these are generally limited to particular, often simple cases and require specific assumptions. It is indeed extremely difficult from a theoretical point of view (it is necessary, however, not sufficient, that all moments are available for a correct reconstruction) as well as from a practical point of view (ill-posed inverse problem) to find an accurate and relatively fast method which can be applied to all scientific areas. In the present paper, different possible methods (prescribed functions, discrete method, spline-based reconstruction) allowing such a reconstruction are explained, compared in terms of efficiency and accuracy, and validated for chemical engineering applications using examples with different degrees of difficulty.     

Comparison of fibre optical measurements and discrete element simulations for the study of granulation in a spout fluidized bed

Spout fluidized beds are frequently used for the production of granules or particles through granulation. The products find application in a large variety of applications, for example detergents, fertilizers, pharmaceuticals and food. Spout fluidized beds have a number of advantageous properties, such as a high mobility of the particles, which prevents undesired agglomeration and yields excellent heat transfer properties.A discrete element model is used describing the dynamics of the continuous gas phase and the discrete droplets and particles. For each element momentum balances are solved. The momentum transfer among each of the three phases is described in detail at the level of individual elements.The results from the discrete element model simulations are compared with local measurements of time time-averaged particle volume fractions as well as particle velocities by using a novel fibre optical probe in a fluidized bed of 400 mm I.D. Simulations and experiments were carried out for three different cases using Geldart B type aluminium oxide particles: a freely bubbling fluidized bed; a spout fluidized bed without the presence of droplets and a spout fluidized bed with the presence of droplets. It is found that the experimental and numerical results agree in a qualitative manner.It is demonstrated how the discrete element model can be used to obtain information about the interaction of the discrete phases, i.e. the growth zone in a spout fluidized bed. Additional analysis of the numerical results indicates that liquid breakthrough does not take place for the studied test case.     

Selection of an appropriate time integration scheme for the discrete element method (DEM)

With increasing computer power simulation methods addressing discrete problems in a broad range of scientific fields become more and more available. The discrete element method is one of these discontinuous approaches used for modeling granular assemblies. Within this method the dynamics of a system of particles is modeled by tracking the motion of individual particles and their interaction with their adjacencies over time. For the interaction of particles, force models need to be specified. The resulting equations of motion are of coupled ordinary differential configuration, which are usually solved by explicit numerical schemes. In large-scale systems like avalanches, planetary rings, hoppers or chemical reactors vast numbers of particles need to be addressed. Therefore, integration schemes need to be accurate on the one hand, but also numerically efficient on the other hand. This numerical efficiency is characterized by the method's demand for memory and CPU-time. In this paper a number of mostly explicit numerical integration schemes are reviewed and applied to the benchmark problem of a particle impacting a fixed wall as investigated experimentally by Gorham and Kharaz [Gorham, D. A., & Kharaz, A. H. (2000). The measurement of particle rebound characteristics. Powder Technology, 112(3), 193–202]. The accurate modeling which includes the correct integration of the equations of motion is essential. In discrete simulation methods the accuracy of properties on the single particle level directly influence the global properties of the granular assembly like velocity distributions, porosities or flow rates, whereas their correct knowledge is often of key interest in engineering applications. The impact experiment is modeled with simple force displacement approaches which allow an analytical solution of the problem. Aspects discussed are the dependency of the step size on the accuracy of certain collision properties and the related computing time. The effect of a fixed time step is analyzed. Guidelines for the efficient selection of an integration scheme considering the additional computational cost by contact detection and force calculation are presented.     

Evaluating the problem-solving skills of graduating chemical engineering students

Research has shown that engineering students may not be learning to solve the kinds of complex problems they will be required to solve as practicing engineers (“authentic problems”). Though it is widely believed that we teach engineering problem-solving throughout the undergraduate chemical engineering curriculum, this has not been tested. In this study we use a new instrument for measuring the authentic problem-solving skills of graduating seniors in chemical engineering at two different universities in the context of chemical process design. We find large variations across different areas of process design problem solving as to how expert-like students are in general, and variations between the two institutions. Students were able to identify the same safety issues as experts, but they were conspicuously “nonexpert” in other areas, such as in identifying the important features of a design problem. By examining the respective curricula at the two institutions, we are able to show how the variations both within and across institutions in the specific problem-solving skills students master matches with the practice they get during their undergraduate careers. The results imply that more thoroughly integrating practice in authentic design and problem-solving decisions into the undergraduate curriculum would result in students graduating with capabilities more comparable to those of skilled engineers.     

Prediction of polymer pellet conveying behavior in microinjection molding machine

The higher requirements in dosage accuracy and material strength for products with micro features have made the solid conveying process in microinjection molding machines very important. Problems such as starve feeding and process instability will adversely affect quality more in microinjection molding than bigger parts. Studies have been carried out on the conveying of discrete solid polymer pellets in the plasticizing unit of microinjection molding machines using a newly developed discrete element modeling method to simulate polymer particle movements in the screw channel. The model takes into consideration the influences of adhesion and gravity. The effect of inclination of the conveying screw on the speed of solid conveying is investigated with both simulation and experimental approaches. The results of simulations agree with the results of experiments qualitatively. POLYM. ENG. SCI. 46:1608–1612, 2006. © 2006 Society of Plastics Engineers     

Algebraic surrogate-based process optimization using Bayesian symbolic learning

Here, we propose a strategy for the global optimization of process flowsheets, a fundamental problem in process systems engineering, based on algebraic surrogates that are built from rigorous simulations via Bayesian symbolic regression. The applied method provides a closed-form expression that can be optimized to global optimality using state-of-the-art solvers, where BARON or ANTIGONE were the solvers of choice. When predicting unseen test data, the algebraic models show a similar accuracy level compared to traditional surrogates based on Gaussian processes. However, they can be more easily optimized to global optimality due to their analytical closed-form structure, which allows the user to apply well-established global deterministic solvers. We show the capabilities of our approach in several case studies, ranging from process units to full flowsheets. The performance of our approach is assessed by comparing the CPU time for model building, the prediction accuracy of the identified model, and the CPU time for the subsequent optimization with a proven benchmark.