Editorial pubs.acs.org/IECR
Preface to Multiscale Structures and Systems in Process Engineering Special Issue†
T
problems in chemical processing at three levels, viz. material, reactor, and system, as illustrated in Figure 1, we can find that
oday the tempo of change in science and technology is increasing exponentially, creating a correspondingly urgent need for scientists and engineers seeking new perspectives for traditional problems, some of which may be longstanding, or posing new questions and offering new answers. Chemical engineering came into being at the turn of last century through the grouping of similar physical operations in industry into the unit operations, and then was upgraded in the middle of last century to a higher scientific level through a systematic study of the transport phenomena of momentum, mass, and energy, creating the interdisciplinary domain of chemical reaction engineering. Now, chemical engineering has been extended to a broader spectrum of activities, viz., process engineering, from the production of delicate materials (e.g., foods, pharmaceuticals, cosmetics, and functional nanomaterials) to the processing of bulk materials (e.g., chemicals, minerals, coal, and petroleum). Gradually, we recognize that multiscale structures are ubiquitous, not only in nature but also in process engineering, and multiscale systems display divergent behaviors with fascinating complexity and remarkable uniqueness. The spatiotemporal behaviors at different scales are strongly coupled because of high nonequilibrium constraints and the compromise among different dominant mechanisms. Understanding and modeling the multiscale structures and behavior have been necessary yet also a common challenge for different fields. Slowly and reluctantly, we found that the current knowledge base is insufficient to deal with engineering problems, and we have to extend our minds to tackle the complex phenomena and mechanisms related to multiscale structures. Fortunately, “Multiscale Structures and Systems” supplies such a unifying platform for comprehending and harnessing these complex systems. In response to this common challenge and trend, the Chinese Academy of Sciences (CAS), Royal Academy of Engineering of UK (RAE), Institution of Chemical Engineers (IChemE), and University of New South Wales (UNSW) organized the fourth International Conference on Multiscale Structures and Systems in Process Engineering (http://www. mesoscience.org) in Beijing, China, September 26−28, 2012. The conference follows the success of the three previous conferences: the first one in Beijing, China (2007), with its theme being “designing structured materials for functionality”; the second in Bangalore, India (2008), with its theme being “processes and forces for creation of designer materials with multiscale structures”; and the third one in Beijing, China (2010), with its theme being “modeling, simulation and virtual experiments”. The accumulated enthusiasm, increasing interest, and extended discussion stimulated by the three previous conferences on multiscale structures and systems in various aspect of process engineering then naturally lead us to think about what is the best theme for the fourth conference. It should be something in common and/or critical to solving multiscale problems. When we try to pursue solutions to the © 2013 American Chemical Society
Figure 1. Challenges at three mesoscales in the multilevel hierarchy of chemical processes.1,2
we have already known much about the boundary scales of each level, whereas less is understood on the mesoscales and how to manipulate them.1,2 Thus, “Mesoscales: the key to multi-scale problems” is emerging as the theme for this conference. The fourth conference brought ∼80 leading academic and industrial research scientists from around the world for fascinating discussions on this new theme. Collected in this special issue are some scientific papers presented at the conference. Strictly speaking, not all of them are directly dedicated to the conference theme, because it is still an emerging paradigm. But one may notice the trend that the authors in this special issue are attempting to investigate or analyze their problems from this perspective. In fact, following the hierarchy depicted in Figure 1, papers in this issue could be reasonably grouped into three levels: (1) Material level: Chen et al.3 presented a critical review on the design and application of sensitive and selective ratiometric nanoprobes based on fluorescence resonance energy transfer (FRET), and on the development of multiple probes encouraged by the surface chemistry of nanomaterial. Huang et al.4 presented a Kinetic Monte Carlo (KMC) model to simulate the silicon thin-film deposition process in manufacturing thin-film solar cells. Pan et al.5 discussed the structure and mechanical properties of consumer-friendly PMMA microcapsules through membrane emulsification and finite element modeling. (2) Reactor level: Deen and Kuipers6 applied the direct numerical simulation (DNS) technique to study mass-transfer Special Issue: Multiscale Structures and Systems in Process Engineering Received: June 10, 2013 Accepted: June 12, 2013 Published: August 21, 2013 11225
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Editorial
solution, and computer implementation of a multiscale model, highlighting the need to formulate a common conceptual and computational framework for multiscale modeling. Wei et al.25 performed steady-state and dynamic process simulations for the pressure-swing distillation systems for separation of dimethyl carbonate/methanol and then proposed an optimized separation configuration. A conference covering topics of such diversity and unity cannot be achieved without the enduring support and promotion of many people, academic organizations, and industries. For this conference, we would like to gratefully acknowledge the sponsorship of the National Natural Science Foundation of China (NSFC), CAS, PetroChina, NICE, Unilever, BP, TOTAL and NVIDIA; the support of the Scientific Committee, co-chaired by J. H. Li (CAS) and A. B. Yu (UNSW) and consisting of V. D. Akker (Delft University of Technology), A. Bell (University of California at Berkeley), M. J. Marks (University of Adelaide), J. S. Curtis (University of Florida), L. S. Fan (The Ohio State University), T. Gauthier (IFP Energies Nouvelles), S. Heinrich (Hamburg University of Technology), Y. Hu (East China University of Science and Technology), S. T. Johansen (SINTEF Materials and Chemistry), J. A. M. Kuipers (Eindhoven University of Technology), M. S. Kwauk (Institute of Process Engineering, CAS), G. P. Lian (Unilever), G. B. Marin (Ghent University), W. Meier (DECHEMA), W. Peukert (University of Erlangen), Pradip (TATA), P. Ricoux (TOTAL SA), J. C. Schouten (Eindhoven University of Technology), P. Schwarz (CSIRO), J. Seville (University of Surrey), M. Syamlal (U.S. National Energy Technology Laboratory), K. Sundmacher (MPI Dynamics of Complex Technical Systems), Y. Tsuji (Osaka University), J. Werther (Hamburg University of Technology), R. Williams (University of Birmingham), and Q. Yuan (Institute of Chemical Physics, CAS); the effort of the Organizing Committee, coordinated by N. Yang (Institute of Process Engineering, CAS) and consisting of D. P. Gao (Beijing University of Chemical and Technology), L. Y. Chu (Sichuan University), X. Feng (Xi’an Jiaotong University), W. Ge (Institute of Process Engineering, CAS), W. Q. Jin (Nanjing University of Technology), B. G. Li (Zhejiang University), C. J. Liu (Tianjin University), H. Z. Liu (Institute of Process Engineering, CAS), K. Liu (National Institute of Clean-andLow-Carbon Energy), G. S. Luo (Tsinghua University), X. J. Peng (Dalian University of Technology), X. H. Qian (East China University of Science and Technology), Y. Qian (South China University of Technology), H. W. Sun (National Natural Science Foundation), C. M. Xu (China University of Petroleum), and S. J. Zhang (Institute of Process Engineering, CAS). Finally, we would also like to thank the delegates for their attendance at and contributions to this conference, the research students and staff of the CAS Institute of Process Engineering and China Society of Particuology for their assistance in conference running, and Professor Donald Paul, the Editor-in-Chief, Industrial & Engineering Chemistry Research, for his help to produce this special issue.
phenomena in porous media for the purpose of developing more-refined closures for coarse-grained models such as coupled Computational Fluid Dynamics (CFD)−Discrete Element Method (DEM) modeling or Two-Fluid Model (TFM). Dosta et al.7 presented a multiscale simulation strategy for fluidized agglomeration, involving the combined use of simulation techniques such as Population Balance Model (PBM) at a macroscale and the CFD-DEM at a microscale. The wetting, drying, and growth of particles based on heat and mass transfer were recognized as mesoscale phenomena to provide the breakage rate and aggregation kernels for PBM to link models at different scales. Guo et al.8 developed an integrated multiscale model to describe the wire-plate-type electrostatic precipitator, aiming to understand the underlying physics and to develop a computer tool for process design and control. Jin et al.9 investigated the effect of mean drift velocity on particle clustering through the combination of the pseudospectral method and Lattice Boltzmann method (LBM). Lan et al.10 utilized the so-called MP-PIC method to model the solid back-mixing with special reference to the effect of particle clusters in the riser of a circulating fluidized bed. Zou et al.11 incorporated a modified agglomerate-force balance model into the TFM to simulate the fluidization of cohesive fine particles. Qiu12 presented a method combining the Smoothed Particle Hydrodynamics (SPH) and DEM to simulate liquid-particle flow. Ullah et al.13 generalized the fluidization diagram by taking into account the effects of mesoscale structures on drag force closure. Dong et al.14 simulated the particle flow and sieving behavior on a vibrating screen, demonstrating that the sieving performance at the process equipment scale can be linked to the particle−deck collisions obtained from the DEM simulations facilitated by the well-established probability theory. Jin15 proposed a multiscale framework for modeling gasification of biomass particles in a fluidized bed, while Zhao et al.16 conducted a large-scale 3D numerical simulation of the socalled MTO fluidized-bed reactor. Kamali and van den Akker17 extended the pseudo-potential LBM for modeling the gas− liquid flow with high density ratios and the presence of tube walls, with the potential function and coupling strength mimicking the effect of mesoscopic physical interactions. Zhang et al.18 presented a simulation study of bubble dynamics in aluminum smelting process based on the Volume of Fluids (VOF) method, showing the microscale contribution to the complex macroscale behavior in this complicated operation. Shu and Yang19 systematically investigated the bubble dynamics with a Multiple-Relaxation-Time (MRT) LBM, progressively probing the behavior of a single bubble, a bubble pair, and a bubble swarm. Liu et al.20 used the high-speed photography and fractal analysis to examine the multiphase self-organization in a gas−liquid−solid circulating fluidized bed. Quillatre et al.21 investigated the multiscale problem of gas explosion phenomena in a reduced-scale vented combustion chamber through large eddy simulation. Gel et al.22 demonstrated how a comprehensive uncertainty quantification method can be adopted for quantifying the uncertainties in multiphase CFD models. (3) System level: Baliban et al.23 proposed a rigorous optimization-based framework to describe the complex synthesis of duckweed to gasoline, diesel, or kerosene, demonstrating that there is a threshold price above which the duckweed refinery will no longer be economically competitive. Yang24 outlined the common concepts, methods, and tools in multiscale modeling and the key aspects in the construction,
Ning Yang
Institute of Process Engineering, Chinese Academy of Sciences, People’s Republic of China
Aibing Yu*
University of New South Wales, Australia
Jinghai Li
Chinese Academy of Sciences, People’s Republic of China 11226
dx.doi.org/10.1021/ie4018463 | Ind. Eng. Chem. Res. 2013, 52, 11225−11227
Industrial & Engineering Chemistry Research
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Editorial
(18) Zhang, K.; Feng, Y.; Schwarz, P.; Wang, Z.; Cooksey, M. Computational fluid dynamics (CFD) modelling of bubble dynamics in aluminium smelting process. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie303464a. (19) Shu, S.; Yang, N. Direct Numerical Simulation of Bubble Dynamics Using Phase-Field Model and Lattice Boltzmann Method. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie303486y. (20) Liu, J.; Liu, M.; Hu, Z. Fractal Structure in Gas−Liquid−Solid Circulating Fluidized Beds with Low Solid Holdups of Macroporous Resin Particles. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie3030906. (21) Quillatre, P.; Vermorel, O.; Poinsot, T.; Ricoux, P. Large Eddy Simulation of Vented Deflagration. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie303452p. (22) Gel, A.; Li, T.; Gopalan, B.; Shahnam, M.; Syamlal, M. Validation and Uncertainty Quantification of a Multiphase CFD Model. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie303469f. (23) Baliban, R. C.; Elia, J. A.; Floudas, C. A.; Xiao, X.; Zhang, Z.; Li, J.; Cao, H.; Ma, J.; Qiao, Y.; Hu, X. Thermochemical Conversion of Duckweed Biomass to Gasoline, Diesel, and Jet Fuel: Process Synthesis and Global Optimization. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie3034703. (24) Yang, A. On the Common Conceptual and Computational Frameworks for Multiscale Modeling. Ind. Eng. Chem. Res.2013, DOI: 10.1021/ie303123s. (25) Wei, H.; Wang, F.; Zhang, J.; Liao, B.; Zhao, N.; Xiao, F.; Wei, W.; Sun, Y. Design and Control of Dimethyl Carbonate−Methanol Separation via Pressure-Swing Distillation. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie3034976.
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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DEDICATION We would like to dedicate this special issue to Professor Mooson Kwauk, who passed away about one month after the conference. He initiated the multiscale research at the CAS Institute of Process Engineering and attended most of the presentations at the conference at the age of 92. His leaving is a great loss and sadness for the research community. †
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REFERENCES
(1) Li, J., Ge, W., Kwauk, M. Meso-scale phenomena from compromiseA common challenge, not only for chemical engineering. Arxiv preprint arXiv:0912.5407, 2009. (2) Li, J.; Ge, W.; Wang, W.; Yang, N.; Liu, X.; Wang, L.; He, X.; Wang, X.; Wang, J.; Kwauk, M. From Multiscale Modeling to MesoScience; Springer: Berlin, Germany, New York, 2013. (3) Chen, G.; Song, F.; Xiong, X.; Peng, X. Fluorescent Nanosensors Based on Fluorescence Resonance Energy Transfer (FRET). Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie303485n. (4) Huang, J.; Orkoulas, G.; Christofides, P. D. Simulation and Control of Porosity in a Three-Dimensional Thin-Film Solar Cell. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie4003359. (5) Pan, X.; Mercadé-Prieto, R.; York, D.; Preece, J. A.; Zhang, Z. Structure and Mechanical Properties of Consumer-Friendly PMMA Microcapsules. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie303451s. (6) Deen, N. G.; Kuipers, J. A. M. Direct Numerical Simulation of Fluid Flow and Mass Transfer in Dense Fluid-Particle Systems. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie303411k. (7) Dosta, M.; Antonyuk, S.; Heinrich, S. Multiscale Simulation of Agglomerate Breakage in Fluidized Beds. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie400244x. (8) Guo, B.; Yang, S.; Xing, M.; Dong, K.; Yu, A.; Guo, J. Toward the Development of an Integrated Multiscale Model for Electrostatic Precipitation. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie303466g. (9) Jin, G.; Wang, Y.; Zhang, J.; He, G. Turbulent Clustering of Point Particles and Finite-Size Particles in Isotropic Turbulent Flows. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie303507d. (10) Lan, X.; Shi, X.; Zhang, Y.; Wang, Y.; Xu, C.; Gao, J. Solids Back-mixing Behavior and Effect of Meso-scale Structure in CFB Risers. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie3034448. (11) Zou, Z.; Li, H.; Zhu, Q.; Wang, Y. Experimental Study and Numerical Simulation of Bubbling Fluidized Beds with Fine Particles in Two and Three Dimensions. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie303105v. (12) Qiu, L. Numerical Modeling of Liquid-Particle Flows by Combining SPH and DEM. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ ie303001f. (13) Ullah, A.; Wang, W.; Li, J. Generalized Fluidization Revisited. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie3034653. (14) Dong, K. J.; Wang, B.; Yu, A. B. Modeling of Particle Flow and Sieving Behavior on a Vibrating Screen: From Discrete Particle Simulation to Process Performance Prediction. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie3034637. (15) Jin, G. D. A Multiscale Coupling Framework for Modeling of Large-Size Biomass Particle Gasification in Fluidized Beds. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie400420p. (16) Zhao, Y. F.; Li, H.; Ye, M.; Liu, Z. M. 3D Numerical Simulation of a Large Scale MTO Fluidized Bed Reactor. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie303467k. (17) Kamali, M. R.; Van den Akker, H. E. A. Simulating Gas−Liquid Flows by Means of a Pseudopotential Lattice Boltzmann Method. Ind. Eng. Chem. Res. 2013, DOI: 10.1021/ie303356u. 11227
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