|
|
- 2017
双组分颗粒团聚过程中组分混合程度的预测
|
Abstract:
摘要 多组分颗粒凝并是颗粒长大过程的主要物理机制之一。对于双组分凝并，混合程度χ为一重要衡量标准，是预测组分分布的关键参数。针对双组分颗粒团聚的非稳态过程研究混合程度随时间的变化关系，采用颗粒群平衡模拟异权值快速Monte Carlo方法，进行模拟，最终预测出χ与初始条件的关系，得到自由分子区布朗凝并与连续区布朗凝并的指数形式预测公式，并进行验证，从而可以通过合理选择初始参数，优化控制组分分布和实现颗粒定向功能制备。
| [1] | Chauhan S S, Chakraborty J, Kumar S. On the solution and applicability of bivariate population balance equations for mixing in particle phase[J]. Chemical Engineering Science, 2010, 65(13):3914-3927. |
| [2] | Zhao H, Zheng C. A population balance-Monte Carlo method for particle coagulation in spatially inhomogeneous systems[J]. Computers & Fluids, 2013, 71:196-207. |
| [3] | Xu Z, Zhao H, Zheng C. Fast Monte Carlo simulation for particle coagulation in population balance[J]. Journal of Aerosol Science, 2014, 74:11-25. |
| [4] | Friedlander S K. Smoke, dust, and haze:fundamentals of aerosol dynamics[M].New York:Oxford University Press, 2000. |
| [5] | Hofmann S, Raisch J. Solutions to inversion problems in preferential crystallization of enantiomers. Part Ⅱ:Batch crystallization in two coupled vessels[J]. Chemical Engineering Science, 2013, 88:48-68. |
| [6] | Lee K, Kim T, Rajniak P, et al. Compositional distributions in multicomponent aggregation[J]. Chemical Engineering Science, 2008, 63(5):1293-1303. |
| [7] | Zhao H, Kruis F E, Zheng C. Monte Carlo simulation for aggregative mixing of nanoparticles in two-component systems[J]. Industrial & engineering chemistry research, 2011, 50(18):10652-10664. |
| [8] | Marshall C L, Rajniak P, Matsoukas T. Numerical simulations of two-component granulation:comparison of three methods[J]. Chemical Engineering Research and Design, 2011, 89(5):545-552. |
| [9] | Zhao H, Kruis F E. Dependence of steady-state compositional mixing degree on feeding conditions in two-component aggregation[J]. Industrial & Engineering Chemistry Research, 2014, 53(14):6047-6055. |
| [10] | Zhao H, Kruis F E, Zheng C. A differentially weighted Monte Carlo method for two-component coagulation[J]. Journal of Computational Physics, 2010, 229(19):6931-6945. |
| [11] | Zhao H, Kruis F E, Zheng C. Reducing statistical noise and extending the size spectrum by applying weighted simulation particles in Monte Carlo simulation of coagulation[J]. Aerosol Science and Technology, 2009, 43(8):781-793. |
| [12] | Zhao H, Zheng C. A new event-driven constant-volume method for solution of the time evolution of particle size distribution[J]. Journal of Computational Physics, 2009, 228(5):1412-1428. |
| [13] | Zhao H, Zheng C. Two-component Brownian coagulation:Monte Carlo simulation and process characterization[J]. Particuology, 2011, 9(4):414-423. |
| [14] | Hao X, Zhao H, Xu Z, et al. Population balance-Monte Carlo simulation for gas-to-particle synthesis of nanoparticles[J]. Aerosol science and technology, 2013, 47(10):1125-1133. |
| [15] | Hosseini A, Bouaswaig A E, Engell S. Novel approaches to improve the particle size distribution prediction of a classical emulsion polymerization model[J]. Chemical Engineering Science, 2013, 88:108-120. |
| [16] | Riemer N, West M, Zaveri R, et al. Estimating black carbon aging time-scales with a particle-resolved aerosol model[J]. Journal of Aerosol Science, 2010, 41(1):143-158. |
| [17] | Fox R O. Higher-order quadrature-based moment methods for kinetic equations[J]. Journal of Computational Physics, 2009, 228(20):7771-7791. |
| [18] | Barrasso D, Ramachandran R. A comparison of model order reduction techniques for a four-dimensional population balance model describing multi-component wet granulation processes[J]. Chemical Engineering Science, 2012, 80:380-392. |
| [19] | Lushnikov A. Evolution of coagulating systems:Ⅲ. Coagulating mixtures[J]. Journal of Colloid and Interface Science, 1976, 54(1):94-101. |
| [20] | Matsoukas T, Kim T, Lee K. Bicomponent aggregation with composition-dependent rates and the approach to well-mixed state[J]. Chemical Engineering Science, 2009, 64(4):787-799. |
| [21] | Matsoukas T, Lee K, Kim T. Mixing of components in two-component aggregation[J]. AIChE journal, 2006, 52(9):3088-3099. |
