磨矿破碎过程粒度分布的分布式参数蒙特卡洛动力学模拟及加速方法

A Distributed Parameter Kinetic Monte Carlo Simulation Algorithm of Grinding Process and Its Acceleration

本文针对磨矿破碎过程,提出一种分布式参数蒙特卡洛动力学方法的粒度分布预测模型和模拟算法.该算法采用了分段思想,将磨机沿着轴向分为若干个虚拟的子磨机;根据破裂、前向和后向移动三类微观事件定义了倾向函数和系统状态矩阵,并设计了分布式算法的调度策略.此外,针对蒙特卡洛动力学算法效率低的问题,提出了基于τ-leap的磨矿过程分布式参数蒙特卡洛模拟加速算法.为了解决分布式参数更新过程中状态不一致的问题,创新性地提出了一种基于缓冲区的同步方法.通过对仿真案例的分析表明,本文提出的分布式参数蒙特卡洛动力学算法具有较高的精度,提出的基于τ-leap的加速算法能够显著提高计算效率,同时保持较好的精度.

In this paper, we propose a prediction model for the particle size distribution of grinding process and a kinetic Monte Carlo simulation algorithm. The algorithm is based on the idea of dividing the mill into several virtual grinding machines along the axial direction. The preference function and the system state matrix are defined according to three kinds of microcosmic events, breakage, forward movement and back movement, and the scheduling strategy of distributed algorithm is designed. In addition, in view of the low efficiency of the kinetic Monte Carlo algorithm, a Monte Carlo simulation acceleration algorithm based on τ-leap is proposed. In order to solve the problem of inconsistent state when updating the distributed parameters, a new buffer based method is proposed. Case study based on simulation shows that the distributed parameter Monte Carlo dynamic algorithm proposed in this paper has high precision, and the proposed algorithm based on τ-leap can significantly improve the computational efficiency while maintaining good accuracy.