Monte Carlo simulation of particle breakage process during grinding

The Monte Carlo method is quite useful in the modeling of particulate systems. It is used here to simulate the particle brekage process during grinding that can be represented by a population balance equation. The simulation technique is free from discretization of time or size. The results of simulation under restricted conditions of grinding compare very well with the available analytical solution of the population balance equation. The procedure is extended to simulate the grinding process in its entirety. This method provides an alternative to the modeling of the grinding process where the governing population balance equation cannot be readily solved.     

Dynamic prediction of the bivariate molecular weight-copolymer composition distribution using sectional-grid and stochastic numerical methods

In the present study, a two-dimensional fixed pivot technique (2-D FPT) and an efficient Monte Carlo (MC) algorithm are described for the calculation of the bivariate molecular weight-copolymer composition (MW-CC) distribution in batch free-radical copolymerization reactors. A comprehensive free-volume model is employed to describe the variation of termination and propagation rate constants as well as the variation of the initiator efficiency with respect to the monomer conversion. Simulations are carried out, under different reactor conditions, to calculate the individual monomer conversions, the leading moments of the ‘live’ and ‘dead’ polymer chain length distributions as well as the dynamic evolution of the distributed molecular properties (i.e., molecular weight distribution (MWD), copolymer composition distribution (CCD) and joint MW-CC distribution). The validity of the numerical calculations is examined via a direct comparison of the simulation results, obtained by the two numerical methods, with experimental data on the styrene-methyl methacrylate batch free-radical copolymerization. Additional comparisons between the 2-D FPT and the MC methods are carried out for different polymerization conditions. It is clearly shown that both numerical methods are capable of predicting the distributed molecular and copolymer properties, with high accuracy, up to very high monomer conversions. It is also shown that the proposed dynamic MC algorithm is less computationally demanding than the 2-D FPT.     

Population balances for particulate processes—a volume approach

Population balance models have been used in chemical engineering since the 1960s and have evolved to become the most important tools for design and control of particulate processes. In this paper we show that the intrinsic particle parameter that determines changes in the process and should thus be included in the population balance is the particle volume. The basic population that is modeled should be the mass distribution, or the volume distribution if the density is constant. The population balance thus describes the change of the volume distribution of volume with time. Furthermore, we suggest that the “birth” and “death” terms that are often used to describe discrete events in particulate processes can almost always be replaced by a rate of change term.To design and control existing and future processes, a multi-dimensional population balance model is required. We propose a volume-based model in which the particle properties that are modeled are the volumes of solid, liquid, and air, respectively. In the most general case the model will consist of a properties vector and a distribution tensor. Depending on the complexity of the process, one or more of the properties may be omitted from the model. This is shown in three examples of increasing complexity: comminution, sintering, and granulation.     

Compositional distributions in multicomponent aggregation

We consider the granulation of two components, a “solute” (the component of interest) and an excipient. We specifically focus on cases such that the aggregation kernel is independent of the composition of the aggregating granules. In this case, theory predicts that the distribution of components is a Gaussian function such that the mean concentration of solute in granules of a given size is equal to the overall mass fraction of solute in the system, and the variance is inversely proportional to the granule size. To study these effects, we perform numerical simulations of the bicomponent population balance equation using a constant aggregation kernel as well as a kernel based on the kinetic theory of granular flow (KTGF). If the solute and excipient are initially present in the same size (monodisperse initial conditions), both kernels produce identical distributions of components. With different initial conditions, the KTGF kernel leads to better mixing of components, manifested in the form of narrower compositional distributions. These behaviors are in agreement with the predictions of the theory of aggregative mixing. We further demonstrate that the overall mixedness of the system is controlled by the initial degree of segregation in the feed and show that the size distribution in the feed can be optimized to produce the narrowest possible distribution of components during granulation.     

Part IV: Dynamic evolution of the particle size distribution in particulate processes. A comparative study between Monte Carlo and the generalized method of moments

The present work provides a comparative study on the numerical solution of the dynamic population balance equation (PBE) for batch particulate processes undergoing simultaneous particle aggregation, growth and nucleation. The general PBE was numerically solved using three different techniques namely, the Galerkin on finite elements method (GFEM), the generalized method of moments (GMOM) and the stochastic Monte Carlo (MC) method. Numerical simulations were carried out over a wide range of variation of particle aggregation and growth rate models. The performance of the selected techniques was assessed in terms of their numerical accuracy and computational requirements. The numerical results revealed that, in general, the GFEM provides more accurate predictions of the particle size distribution (PSD) than the other two methods, however, at the expense of more computational effort and time. On the other hand, the GMOM yields very accurate predictions of selected moments of the distribution and has minimal computational requirements. However, its main disadvantage is related to its inherent difficulty in reconstructing the original distribution using a finite set of calculated moments. Finally, stochastic MC simulations can provide very accurate predictions of both PSD and its corresponding moments while the MC computational requirements are, in general, lower than those required for the GFEM.     

Analysis of four Monte Carlo methods for the solution of population balances in dispersed systems

Monte Carlo (MC) constitutes an important class of methods for the numerical solution of the general dynamic equation (GDE) in particulate systems. We compare four such methods in a series of seven test cases that cover typical particulate mechanisms. The four MC methods studied are: time-driven direct simulation Monte Carlo (DSMC), stepwise constant-volume Monte Carlo, constant number Monte Carlo, and multi-Monte Carlo (MMC) method. These MC's are introduced briefly and applied numerically to simulate pure coagulation, breakage, condensation/evaporation (surface growth/dissolution), nucleation, and settling (deposition). We find that when run with comparable number of particles, all methods compute the size distribution within comparable levels of error. Because each method uses different approaches for advancing time, a wider margin of error is observed in the time evolution of the number and mass concentration, with event-driven methods generally providing better accuracy than time-driven methods. The computational cost depends on algorithmic details but generally, event-driven methods perform faster than time-driven methods. Overall, very good accuracy can be achieved using reasonably small numbers of simulation particles, O(10³), requiring computational times of the order 10²−10³ s on a typical desktop computer.     

MODELING VENTURI SCRUBBER PERFORMANCE FOR PARTICULATE COLLECTION AND PRESSURE DROP

A computational model of gas-particle flows has been extended to predict venturi scrubber performance as measured by particle collection efficiency and pressure drop. The concept of regarding particle and liquid phases as sources of momentum and energy to the gaseous phase was incorporated into the model's computational scheme. Predicted pressure drop results showed good agreement with available experimental data, particularly when uniform liquid distribution across the venturi cross-section was achieved. Our model was also more successful in predicting particle collection efficiency than several other models previously reported in the literature. Differences between model predictions and experimental results were chiefly caused by maldistribution of injected liquid into the test scrubbers.     

A generalized framework for solving dynamic optimization problems using the artificial chemical process paradigm: Applications to particulate processes and discrete dynamic systems

The solution of optimal control problems (OCPs) becomes a challenging task when the analyzed system includes non-convex, non-differentiable, or equation-free models in the set of constraints. To solve OCPs under such conditions, a new procedure, LARES-PR, is proposed. The procedure is based on integrating the LARES algorithm with a generalized representation of the control function. LARES is a global stochastic optimization algorithm based on the artificial chemical process paradigm. The generalized representation of the control function consists of variable-length segments, which permits the use of a combination of different types of finite elements (linear, quadratic, etc.) and/or specialized functions. The functional form and corresponding parameters are determined element-wise by solving a combinatorial optimization problem. The element size is also determined as part of the solution of the optimization problem, using a novel two-step encoding strategy. These building blocks result in an algorithm that is flexible and robust in solving optimal control problems. Furthermore, implementation is very simple.The algorithm's performance is studied with a challenging set of benchmark problems. Then LARES-PR is utilized to solve optimal control problems of systems described by population balance equations, including crystallization, nano-particle formation by nucleation/coalescence mechanism, and competitive reactions in a disperse system modeled by the Monte Carlo method. The algorithm is also applied to solving the DICE model of global warming, a complex discrete-time model.     

Population balance modeling. Promise for the future

Population balance modeling has received an unprecedented amount of attention during the past few years from both academic and industrial quarters because of its applicability to a wide variety of particulate processes. In this article, a fresh look is taken of the basic issues of the application of population balances towards strengthening the approach as well as widening the scope of their applications with regard to formulation, computational methods for solution, inverse problems, control of particle populations and stochastic modeling.     

Particulate Matter Removal from a Gas Stream using High‐Voltage Discharge Plasma

Abstract

This investigation examines the use of a high‐voltage discharge plasma technology to remove particulate matter from an air stream. Concentrations of the particulate matter were measured at the inlet and the outlet of the discharge plasma with the help of an optical particle counter to determine the particle removal efficiency. The experimental results indicate that the particle removal efficiency of the discharge plasma increased with the discharge voltage. The particle removal efficiency rose as high as 93.1% for 0.3 μm particles as the discharge voltage was increased to 20 kV at an operating frequency of 60 Hz. The influence of the operating frequency on the particle removal efficiency was neglected at discharge voltages of 8 kV and 10 kV when the operating frequencies ranged from 60 Hz to 180 Hz. Furthermore, the particle removal efficiency increased with the reflected power when the discharge voltage was varied. A non‐linear multivariable regression model was fitted to the experimental data. The good fit of the regression model makes it possible to estimate the particle removal efficiency of the high‐voltage discharge plasma.     

Single-particle method for stochastic simulation of coagulation processes

A Monte Carlo stochastic simulation algorithm based on a single-particle method is suggested to describe steady-state particle coagulation processes. The method does not require any information on nearby particles; instead a fictitious coalescence partner with a given size is generated. The main drawback that limited applicability of this method in the past was that for each control volume the particle size distribution function had to be sampled and stored. In the present study we applied a discrete representation of the distribution function that requires only small memory resources and allows fast updating.     

Simulating the early stage of high-shear granulation using a two-dimensional Monte-Carlo approach

A two-dimensional (2-D) model of a granulation process is presented in this paper. It aims to simulate an entire granulation batch without the use of an initial experimental or fictitious 2-D density function, by taking the experimental operating conditions into account. The mass of liquid and solid in the granules are the two predicted internal variables. The 2-D population balance equation is solved by a Constant Number Monte-Carlo method. This is a stochastic technique tracking the evolution of a population, whilst performing the calculations with a fixed number of particles. This is achieved by reducing or increasing the sample volume when an event results in a net production or a net decrease in the number of particles, respectively. An original multi-population approach is developed to describe the early stage of the process, where small numbers of granules are formed amongst a large number of primary particles. It consists of separating the primary particles from the granule population. A specific intensive variable is introduced to keep track of the repartition of masses. The overall density function is reconstructed a posteriori from the combination of the two populations. This approach allows the simulation to commence from the initial addition of liquid at the start of the process, rather than to start from an early granule size distribution. The early stage of the granulation process, frequently referred as nucleation, can therefore be studied numerically. Four different mechanisms are implemented. Nucleation and re-wetting describe the addition of liquid to the system. The interactions between liquid and solid phases are modelled by a layering process. An aggregation model is also included to simulate the growth of particles undergoing frequent collisions. Finally, the relevance of this new model is demonstrated by confronting the simulations to real experimental data.     

Stochastic simulation of population balance models with disparate time scales: Hybrid strategies

The Monte Carlo methods have been an effective tool for the numerical solution of population balance models (PBMs). They are particularly useful for complex multidimensional problems. Less attention has been paid to solving population balance models where some species are away from the thermodynamic limit (very dilute or finite) and other species can be considered deterministic (high concentration). These types of problem often result in a stochastic system with rates spanning orders of magnitude for different mechanisms. Using the exact Monte Carlo solution to solve these types of problem is very inefficient because of the simulation time spent sampling fast events. These fast events are associated with species with large populations for which a single event does not change the population appreciably. This frequent sampling of fast events becomes a bottleneck during a simulation in which many single MC steps are required to make an appreciable change in the population.In this work, a hybrid solution strategy is developed to effectively solve this type of problem. The method implements the self-consistent fast/slow partitions used to solve stochastic equations in chemical kinetics. One strategy is found on the capacity of a coarse-grained Markov model called particle ensemble random product (PERP) to accelerate the simulation of fast events of PBMs (Chem. Eng. Sci. 63, 7649–7664; Chem. Eng. Sci. 63, 7665–7675). A second strategy approximates the fast events using mass conservation equations. These models are coupled with the exact MC simulation of slow events. Two extreme cases of heterocoagulation are studied to demonstrate these hybrid strategies.     

Fast Monte Carlo methodology for multivariate particulate systems—I: Point ensemble Monte Carlo

A fast Monte Carlo methodology for particulate processes is introduced. The proposed methodology combines concepts from discrete population balance equations and dynamic Monte Carlo simulations of chemical kinetics to construct a new jump Markov model that approximates the population balance dynamics. The Markov model consists of a definition of a new type of reaction channel, in which the reaction product is a stochastic process by itself. One feature of this model is that, although a coarse view of the process is taken, it still conserves the history of individual particles. This is a very important aspect for effective modeling of multivariate models, especially when part of the goal is to study the evolution of the internal states of the particles (i.e., composition, phase behavior, etc.).Numerical experiments show that this algorithm can improve the computational load of the exact method by orders of magnitude without sacrificing computational accuracy. The methodology is useful especially in stochastic optimization applications where many function calls (simulations) are required. Possible applications are optimization and dynamic optimization using an artificial chemical process algorithm, genetic algorithm, or simulated annealing among others.     

蒙特卡洛优化法在炼焦配煤中的应用

应用蒙特卡洛优化法配煤,只需做一定量的试验就可确定目标函数或约束条件函数中的某些系数,并根据需要,建立数学模型,编制程序,求出最优解。     

利用Monte Carlo方法求解粒数衡算方程分析影响气泡分布的各种因素

精馏塔板上气泡尺寸分布是计算精馏塔板效率以及设计和操作精馏塔的关键参数 ,为了对它进行更好的预测 ,文中以Kolmogoroff各向同性湍流理论为基础并结合概率理论 ,利用计算机图形处理技术对气泡的聚并和破裂现象进行观察研究 ,分析气泡的聚并和破裂机理 ,采用MonteCarlo模拟技术求解粒数衡算方程 ,对影响精馏塔板上气泡尺寸分布的各种因素进行研究分析。求解结果与实验数据相吻合并显示气泡尺寸分布为对数正态分布 ,这与其他研究者的结论相一致     

Direct simulation Monte Carlo for simultaneous nucleation, coagulation, and surface growth in dispersed systems

A Monte Carlo simulation technique is developed to describe dispersed phase systems. The method is formulated for simultaneous coagulation, nucleation and surface growth, but can be extended to include other processes. These processes are considered in an initially constant simulation volume. The changes in the particle ensemble are determined by a random choice procedure, while the particle number in the simulation volume changes according to the chosen events. Every time, when the particle number in the simulation volume increases or decreases by a factor of two of its initial value, the simulation volume is halved or doubled, respectively. Therefore, this method is called a stepwise constant-volume Monte Carlo simulation. It allows to use only several thousands simulation particles, even if the particle number concentration experiences changes of several orders of magnitude. The simulations are validated through a comparison with the exact mathematical solutions for several simple cases. An example of simultaneous nucleation and coagulation in the free-molecular region demonstrates, that the stepwise constant-volume Monte Carlo simulations lead to more accurate results than the constant-number Monte Carlo simulations.     

Two methods for sensitivity analysis of coagulation processes in population balances by a Monte Carlo method

We consider two stochastic simulation algorithms for the calculation of parametric derivatives of solutions of a population balance equation, namely, forward and adjoint sensitivity methods. The dispersed system is approximated by an N-particle stochastic weighted ensemble. The infinitesimal deviations of the solution are accounted for through infinitesimal deviation of the statistical weights that are recalculated at each coagulation. In the forward method these deviations of the statistical weights immediately give parametric derivatives of the solution. In the second method the deviations of the statistical weights are used to calculate a finite-mode approximation of the linearized version of the population balance equation. The linearized equation allows for the calculation of the eigenmodes and eigenvalues of the process, while the parametric derivatives of the solution are given by a Lagrange formalism.