Fractional Monte Carlo time steps for the simulation of coagulation for parallelized flowsheet simulations

Abstract The event-driven acceptance rejection (AR) method is a computationally very advantageous Monte Carlo (MC) simulation technique for the solution of population balance equations (PBE) of coagulating systems. In the scope of the event-driven simulation approach, the simulation time is stepwise increased by a simulation time step τ, which is given be the simulated particle properties. Within this time step τ, exactly one coagulation event takes place. The method is therefore not applicable in situations, for which specific time points have to be reached by the simulation, or the time step has to be reset to a smaller value. We propose a methodology termed ‘fractional MC step’ which allows to reset the simulation time step of the AR method to any arbitrary smaller value than the one initially proposed. The proposed method is validated by simulations of coagulation for different initial conditions and comparison with results gained from the discrete sectional method. The potential increase of the stochastic noise is investigated by comparisons with the results from conventional MC simulation techniques. The advantages of a parallel implementation are briefly discussed.

[1]  Jianming Wei Comparison of computational efficiency of inverse and acceptance –rejection scheme by Monte Carlo methods for particle coagulation on CPU and GPU , 2014 .

[2]  Prodromos Daoutidis,et al.  Numerical solution of multi-variable cell population balance models. III. Finite element methods , 2001 .

[3]  R. I. A. PATTERSON,et al.  The Linear Process Deferment Algorithm: A new technique for solving population balance equations , 2006, SIAM J. Sci. Comput..

[4]  J. Seinfeld,et al.  Sectional representations for simulating aerosol dynamics , 1980 .

[5]  Chuguang Zheng,et al.  Accelerating population balance-Monte Carlo simulation for coagulation dynamics from the Markov jump model, stochastic algorithm and GPU parallel computing , 2015, J. Comput. Phys..

[6]  M. L. Laucks,et al.  Aerosol Technology Properties, Behavior, and Measurement of Airborne Particles , 2000 .

[7]  Jianming Wei,et al.  A parallel Monte Carlo method for population balance modeling of particulate processes using bookkeeping strategy , 2014 .

[8]  D. Ramkrishna,et al.  On the solution of population balance equations by discretization—II. A moving pivot technique , 1996 .

[9]  Antonios Armaou,et al.  Constant number Monte Carlo simulation of population balances with multiple growth mechanisms , 2010 .

[10]  Toivo T. Kodas,et al.  Aerosol Processing of Materials , 1998 .

[11]  Jianming Wei,et al.  A GPU-based parallelized Monte-Carlo method for particle coagulation using an acceptance–rejection strategy , 2013 .

[12]  Murkus Kraft,et al.  Modelling of Particulate Processes , 2005 .

[13]  M. Song,et al.  An alternative to the concept of the interval of quiescence (IQ) in the Monte Carlo simulation of population balances , 1999 .

[14]  Robert I. A. Patterson,et al.  Stochastic weighted particle methods for population balance equations with coagulation, fragmentation and spatial inhomogeneity , 2015, J. Comput. Phys..

[15]  Shawn P. Shields,et al.  Kinetics and Mechanisms of Aggregative Nanocrystal Growth , 2014 .

[16]  D. Ramkrishna,et al.  Population balance modeling: current status and future prospects. , 2014, Annual review of chemical and biomolecular engineering.

[17]  Shamsul Qamar,et al.  A comparative study of high resolution schemes for solving population balances in crystallization , 2006, Comput. Chem. Eng..

[18]  Ian T. Cameron,et al.  Process systems modelling and applications in granulation: A review , 2005 .

[19]  Stefan Heinrich,et al.  Novel system for dynamic flowsheet simulation of solids processes , 2017 .

[20]  F. Einar Kruis,et al.  Direct simulation Monte Carlo method for particle coagulation and aggregation , 2000 .

[21]  Daniele Marchisio,et al.  Solution of population balance equations using the direct quadrature method of moments , 2005 .

[22]  S. Katz,et al.  Some problems in particle technology: A statistical mechanical formulation , 1964 .

[23]  Haibo Zhao,et al.  Analysis of four Monte Carlo methods for the solution of population balances in dispersed systems , 2007 .

[24]  S. Friedlander Smoke, Dust, and Haze: Fundamentals of Aerosol Dynamics , 2000 .

[25]  Alejandro L. Garcia,et al.  A Monte Carlo simulation of coagulation , 1987 .

[26]  Vanni,et al.  Approximate Population Balance Equations for Aggregation-Breakage Processes. , 2000, Journal of colloid and interface science.

[27]  Themis Matsoukas,et al.  Constant-number Monte Carlo simulation of population balances , 1998 .

[28]  Doraiswami Ramkrishna,et al.  Population Balances: Theory and Applications to Particulate Systems in Engineering , 2000 .

[29]  Sotiris E. Pratsinis,et al.  A discrete-sectional model for particulate production by gas-phase chemical reaction and aerosol coagulation in the free-molecular regime , 1990 .

[30]  F. Einar Kruis,et al.  Direct simulation Monte Carlo for simultaneous nucleation, coagulation, and surface growth in dispersed systems , 2004 .

[31]  Population Balance-Monte Carlo Simulation for Gas-to-Particle Synthesis of Nanoparticles , 2013 .

[32]  J. Wei,et al.  GPU-accelerated Monte Carlo simulation of particle coagulation based on the inverse method , 2013, J. Comput. Phys..

[33]  Kurt Liffman,et al.  A direct simulation Monte-Carlo method for cluster coagulation , 1992 .

[34]  Frank Stenger,et al.  Agglomeration and breakage of nanoparticles in stirred media mills : a comparison of different methods and models , 2006 .

[35]  Roberto Irizarry,et al.  Fast Compartmental Monte Carlo Simulation of Population Balance Models: Application to Nanoparticle Formation in Nonhomogeneous Conditions , 2012 .

[36]  Robert I. A. Patterson,et al.  Stochastic weighted particle methods for population balance equations with coagulation, fragmentation and spatial inhomogeneity , 2015, J. Comput. Phys..

[37]  F. Einar Kruis,et al.  Computational fluid dynamics based stochastic aerosol modeling: Combination of a cell-based weighted random walk method and a constant-number Monte-Carlo method for aerosol dynamics , 2012 .

[38]  Sotiris E. Pratsinis,et al.  Time-Lag for Attainment of the Self-Preserving Particle Size Distribution by Coagulation , 1994 .

[39]  Chuguang Zheng,et al.  Reducing Statistical Noise and Extending the Size Spectrum by Applying Weighted Simulation Particles in Monte Carlo Simulation of Coagulation , 2009 .

[40]  Gregor Kotalczyk,et al.  A Monte Carlo method for the simulation of coagulation and nucleation based on weighted particles and the concepts of stochastic resolution and merging , 2017, J. Comput. Phys..

[41]  S. Friedlander,et al.  The self-preserving particle size distribution for coagulation by brownian motion☆ , 1966 .

[42]  Doraiswami Ramkrishna,et al.  Simulation of particulate systems using the concept of the interval of quiescence , 1977 .

[43]  Markus Kraft,et al.  A new numerical approach for the simulation of the growth of inorganic nanoparticles , 2006 .