Mesoscience-based virtual process engineering

doi:10.1016/j.compchemeng.2019.03.042

Computers & Chemical Engineering

Volume 126, 12 July 2019, Pages 68-82

https://doi.org/10.1016/j.compchemeng.2019.03.042 Get rights and content

Highlights

•
Addressing the bottleneck for chemical engineering at different levels.
•
Describing the development, extension, and application of the EMMS model.
•
Describing the mesoscale modeling based on the EMMS principle.
•
Describing the EMMS paradigm towards virtual process engineering.
•
Proposing the new PSE paradigm based on mesoscience.

Abstract

Accounting for complex mesoscale structures was found to be the key to predicting system performance from elemental properties, and hence a bottleneck for process systems engineering. The development and generalization of the energy-minimization multiscale (EMMS) model may present a continuous attempt to provide this key link, where mesoscale structures are characterized by analyzing the compromise in competition of the dominant mechanisms in the systems studied, and then an accurate and efficient simulation paradigm is established. This paradigm enables the integration of high-fidelity realtime simulation with virtual reality technologies to create a physically realistic digital counterpart of the industrial processes, that is, virtual process engineering (VPE). VPE may present a new research and development platform for process systems engineering. In long term, the seamless integration of physical and virtual experiments, either in situ or remotely, is also possible with VPE.

Section snippets

Challenges to chemical engineering

The chemical industry can be described as a producer of materials and energies for other industries and society as a whole, using natural resources as feedstocks. In this sense, it is understandable that the principles of chemical engineering are generally applicable to a wider range of industries with similar missions, which are collectively called process industries (Li, 2000). In China, for example, these industries account for nearly 1/6 the GDP (Xiao et al., 2004), which include not only

Current status of simulations in process engineering

To understand the importance of mesoscale studies for computer simulation in chemical engineering, it must be understood that the simulation performance is determined by the interplay among models, numerical methods or algorithms, and computer software and hardware, which is difficult to be assessed by any single criterion. However, some general features of the overall status can still be described based on the vast amount of literatures:

•
Reliable physical principles, well-posed numerical

Exploring mesoscale structures: from EMMS model to EMMS paradigm

The importance of understanding mesoscale structures has come to notice for a long time (Li et al., 1988, Li and Kwauk, 1994, Li et al., 2013a). Some recent studies have attempted a systematic approach to improve the performance of process simulation in terms of accuracy, speed, and efficiency, and one of them originated from the energy-minimization multiscale (EMMS) model for gas-solid systems proposed some 30 years ago (Li et al., 1988).

Main features of virtual process engineering (VPE)

As a significant upgrading of traditional simulation and experiment, VPE is defined by the replacement of real equipment and related processes (such as structural deformation, energy and material transport, and chemical reactions) with apparently identical digital counterparts for R&D purpose. With the following main features, it may bring about new possibilities to chemical and process engineering:

High accuracy, speed and efficiency in simulation: VPE requires high computational speed for real

A roadmap for the development of VPE

With the concept, strategy, and key technologies discussed above, a preliminary implementation of VPE is currently possible and has recently been explored at IPE. The introduction to these attempts below is aimed to provide a clearer picture of the future development of VPE, and to demonstrate the typical application scenarios of VPE.

Integration of mesoscience and VPE to PSE

The discussions so far have focused on the reactor level, but in many cases the general principles they embodied are applicable to other levels. However, though all complex systems in Fig. 1 are shaped by the stability conditions resulted from the compromise in competition of dominant mechanisms (Li, 2015a, Li, 2015b, Li et al., 2016, Li et al., 2018a, Li and Huang, 2018), the dominant mechanisms themselves are level-specific. For example, they may be related to the reaction and diffusion

Conclusions & prospects

In this article, the critical challenges to modern chemical engineering are analyzed. Characterizing, modeling and elucidating mesoscale structures are identified as a central difficulty behind these challenges. The EMMS model was then revisited briefly and analyzed on how it can address this difficulty. The model also suggested a new multiscale architecture for supercomputing, the EMMS Paradigm, to keep the logical and structural consistency between the simulated systems and the simulation

Acknowledgments

The authors would like to thank all the members of the EMMS group for their contributions to the preparation of this article and for sharing some of their unpublished work, as cited in the text. The authors are grateful to the financial support received from the International Partnership Program of Chinese Academy of Sciences (grant no. 122111KYSB20170068), the National Natural Science Foundation of China (grant no. 91834303), and CAS (grant nos. QYZDJ-SSW-JSC029, XDA21030700, XDA21040400, and

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Design, scaling-up, and optimization of industrial reactors mainly depend on step-by-step experiments and engineering experience, which is usually time-consuming, high cost, and high risk. Although numerical simulation can reproduce high resolution details of hydrodynamics, thermal transfer, and reaction process in reactors, it is still challenging for industrial reactors due to huge computational cost. In this study, by combining the numerical simulation and artificial intelligence (AI) technology of machine learning (ML), a method is proposed to efficiently predict and optimize the performance of industrial reactors. A gas–solid fluidization reactor for the methanol to olefins process is taken as an example. 1500 cases under different conditions are simulated by the coarse-grain discrete particle method based on the Energy-Minimization Multi-Scale model, and thus, the reactor performance data set is constructed. To develop an efficient reactor performance prediction model influenced by multiple factors, the ML method is established including the ensemble learning strategy and automatic hyperparameter optimization technique, which has better performance than the methods based on the artificial neural network. Furthermore, the operating conditions for highest yield of ethylene and propylene or lowest pressure drop are searched with the particle swarm optimization algorithm due to its strength to solve non-linear optimization problems. Results show that decreasing the methanol inflow rate and increasing the catalyst inventory can maximize the yield, while decreasing methanol the inflow rate and reducing the catalyst inventory can minimize the pressure drop. The two objectives are thus conflicting, and the practical operations need to be compromised under different circumstance.
Physics-informed dynamic mode decomposition for short-term and long-term prediction of gas-solid flows
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Integration of physics principles with data-driven methods has attracted great attention in recent few years. In this study, a physics-informed dynamic mode decomposition (piDMD) method, where the mass conservation law is integrated with a purely data-driven DMD method, is developed for fast prediction of the spatiotemporal dynamics of solid volume fraction distribution in bubbling fluidized beds. Assessment of the prediction ability using both piDMD and DMD is performed using the CFD-DEM results as the benchmark: Both DMD and piDMD can predict the short-term behaviour of solid volume fraction reasonably well, but piDMD outperforms the DMD in both qualitative and quantitative comparisons; With respect to their long-term ability, the piDMD-based prediction of the instantaneous solid volume fraction distributions is qualitatively correct although the accuracy needs to be improved, and the predicted time-averaged radial and axial profiles are satisfactory; Whereas the DMD-based prediction of instantaneous snapshots and time-averaged results is completely nonphysical. Present study provides a fast and relatively accurate method for predicting the hydrodynamics of gas-solid flows.
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Fast and accurate simulation of industrial-scale reactors is the first and critical step towards virtual process engineering. In this article, we show for the first time that a recently proposed mesoscience-based structural model, which treats the bubble phase and the emulsion phase as the two interpenetrating continua, is able to achieve fast and accurate simulation of the hydrodynamics of four industrial-scale bubbling fluidized beds, in terms of macroscopic distribution of parameters such as the bed expansion rate, the axial and radial profiles of solid concentration and velocity. On the other hand, due to the fact that the mesoscale bubbling structures have been averaged over during the model derivation and the mesh sizes used are comparative to or larger than the bubble diameter, it is impossible to correctly capture the evolution, coalescence and breakup of bubbles. Therefore, the mesoscience-based structural model is best used as a computational fluid dynamics based method for fast engineering simulations of gas-solid fluidization.
Effects of physical properties of supercritical water on coarse graining of particle cluster
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The coarse graining of particle cluster is of great significance to the study of a fluidized bed. The effects of variations in the physical properties of supercritical water on the coarse graining of particle cluster are investigated in this work. The drag coefficient distributions of the particle cluster are not influenced by the physical properties. However, the physical properties have effects on the values of drag coefficient. The effects of physical properties are weaker in the case of large particle concentrations. Furthermore, the physical properties lead to that the effect of particle cluster wake on the drag of downstream particles being significantly different from that of constant property flow. The variation trend of drag of coarse graining particle is consistent with that of isolated particle. The physical properties lead to significant differences in the values of drag. In this paper, the dominance of the effects of physical properties in a variety of cases is confirmed. Finally, a physical properties effect model is developed accordingly.
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Computational fluid dynamics combined with discrete element method (CFD–DEM) is widely adopted to study particle–fluid multiphase systems. However, due to the global dynamical evolution and the naturally non-uniform distribution of particles, the computational load in parallel computing varies dynamically in each process, leading to significant load imbalance. Thus, a dynamic load balancing (DLB) algorithm is proposed for parallel CFD–DEM simulations in the CPU–GPU heterogeneous architecture. A type of computing load analyzing grid (AG) is developed which covers the entire simulation region. Then Kalman filtering and linear weighted method is used to solve the weight of fluid grids and particles. The domain decomposition scheme is updated according to the weights of AGs dynamically. This DLB method is tested for gas–solid fluidized beds and periodic gas–solid systems of different scales. Results show that the DLB algorithm can significantly speed up CFD–DEM simulation and the highest performance is 2.28 times faster with DLB.
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Citation Excerpt :
Gas-solid fluidized reactors are widely used in industries [1], e.g., catalytic polymerization, fluid catalytic cracking, methanol to olefins.
Particle scale information of commercial fluidized beds is important for its design and optimization, e.g., the catalyst performance evolution along time. However, it is difficult to be obtained by in-situ measurements or continuum-based numerical simulations due to the limited accuracy in space and time. Thus, the newly developed reaction model for methanol-to-olefins (MTO) is integrated into the coarse-grained discrete particle method (EMMS-DPM) to study the evolution of coke content distribution on the catalyst. The main gaseous products of the commercial MTO reactor are well predicted. Then, the operation optimization for a commercial MTO reactor is conducted by changing the annual processing capacity and the size distribution of catalysts. Results show that the reactor operation is slightly more difficult when the annual processing capacity is increased by 20%, and increasing the mass fraction of finer catalyst may result in more homogeneous MTO reaction and reduce the energy consumption.

View all citing articles on Scopus

: Declarations of interest: None.

View full text

Mesoscience-based virtual process engineering

Highlights

Abstract

Section snippets

Challenges to chemical engineering

Current status of simulations in process engineering

Exploring mesoscale structures: from EMMS model to EMMS paradigm

Main features of virtual process engineering (VPE)

A roadmap for the development of VPE

Integration of mesoscience and VPE to PSE

Conclusions & prospects

Acknowledgments

Fluid mechanical description of fluidized beds. equations of motion

Ind. Eng. Chem. Fundamentals

A thermodynamic framework for ecologically conscious process systems engineering

Comput. Chem. Eng.

Compromise through competition: a more widely applicable approach?

Engineering

Undergraduate research experiences developing virtual reality based educational modules

Virtual laboratory accidents designed to increase safety awareness

The status and prospects of virtual reality in chemical engineering

Ten steps to developing virtual reality applications for engineering education

The application of virtual reality to chemical engineering and education

Data processing performance analysis for ultrafast electron beam X-ray CT using parallel processing hardware architectures

Flow Meas. Instrum.

Direct simulation of liquid–gas–solid flow with a free surface lattice Boltzmann method

Int. J. Comput. Fluid Dyn.

Layout and material flow optimization in digital factory

Int. J. Simul. Model.

Dynamic multiscale method for gas-solid flow via spatiotemporal coupling of two-fluid model and discrete particle model

AIChE J.

Multiscale modeling of rapid granular flow with a hybrid discrete-continuum method

Powder Technol.

Molecular dynamics–continuum hybrid computations: a tool for studying complex fluid flows

Phys. Rev. E

Scaling-up and -down in a nature-inspired way

Ind. Eng. Chem. Res.

A discrete numerical model for granular assemblies

Geotechnique

Scale-up and multiphase reaction engineering

Curr. Opin. Chem. Eng.

An ultra fast electron beam x-ray tomography scanner

Meas. Sci. Technol.

Automatic synthesis of optimum heat exchanger network synthesis

AIChE J.

Multi‐scale systems engineering for energy and the environment: challenges and opportunities

AIChE J.

Lattice-gas automata for the Navier-Stokes equation

Phys. Rev. Lett.

Analytical multi-scale method for multi-phase complex systems in process engineering—bridging reductionism and holism

Chem. Eng. Sci.

Physical mapping of fluidization regimes–the EMMS approach

Chem. Eng. Sci.

Multiscale discrete supercomputing–a game changer for process simulation?

Chem. Eng. Technol.

Discrete simulation of granular and particle-fluid flows: from fundamental study to engineering application

Rev. Chem. Eng.

Meso-scale oriented simulation towards virtual process engineering (VPE)—the EMMS paradigm

Chem. Eng. Sci.

Stress, stress asymmetry and couple stress: from discrete particles to continuous fields

Granular Matter

Enterprise-wide optimization: a new frontier in process systems engineering

AIChE J.

Research challenges in process systems engineering

AIChE J.

The ETH experience: experimental database and results from the past 8 years

Comparison of non-cohesive resolved and coarse grain DEM models for gas flow through particle beds

Appl. Math. Modell.

Molecular dynamics simulation overcoming the finite size effects of thermal conductivity of bulk silicon and silicon nanowires

Model. Simul. Mater. Sci. Eng.

Steady-state modeling of axial heterogeneity in CFB risers based on one-dimensional EMMS model

Chem. Eng. Sci.

Quantifying cluster dynamics to improve EMMS drag law and radial heterogeneity description in coupling with gas-solid two-fluid method

Chem. Eng. J.

Mesoscale model for heterogeneous catalysis based on the principle of compromise in competition

Chem. Eng. Sci.

Mesoscience: exploring the common principle at mesoscales

Nat. Sci. Rev.

Mesoscale distribution of adsorbates in ZSM-5 zeolite

Chem. Eng. Sci