磨矿粒度动态过程的一种快速Monte Carlo仿真方法

卢绍文; 余策

doi:10.3724/SP.J.1004.2014.01903

磨矿粒度动态过程的一种快速Monte Carlo仿真方法

doi: 10.3724/SP.J.1004.2014.01903

卢绍文^1, ,,
余策²

1.
东北大学流程工业综合自动化国家重点实验室沈阳 110819;
2.
中国电子科技集团公司第36研究所嘉兴 314033

基金项目:

国家自然科学基金（61240012），中央高校基本科研业务费专项资金（N120408003），国家科技支撑计划课题（2012BAF19G01）资助

详细信息

作者简介:
卢绍文东北大学流程工业综合自动化国家重点实验室副教授.2006年获伦敦大学皇后玛丽学院电子工程学博士学位.主要研究方向为工业过程建模与仿真，多尺度随机建模方法和可视化方法.本文通信作者.E-mail：lusw@mail.neu.edu.cn

通讯作者:
卢绍文东北大学流程工业综合自动化国家重点实验室副教授.2006年获伦敦大学皇后玛丽学院电子工程学博士学位.主要研究方向为工业过程建模与仿真，多尺度随机建模方法和可视化方法.本文通信作者.E-mail：lusw@mail.neu.edu.cn

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出版历程
- 收稿日期: 2013-06-19
- 修回日期: 2014-01-10
- 刊出日期: 2014-09-20

A Fast Monte Carlo Algorithm for Dynamic Simulation of Particle Size Distribution of Grinding Processes

LU Shao-Wen^{1
, ,},
YU Ce²

1.
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819;
2.
The 36th Research Institute of China Electronics Technology Group Corporation, Jiaxing 314033

Funds:

Supported by National Natural Science Foundation of China (61240012), Fundamental Research Funds for the Central Universities (N120408003), and National Key Technology Research and Development Program (2012BAF19G01)

摘要

摘要: 磨矿是降低矿物粒度的工业过程，产品粒度是磨矿过程的关键质量指标. 由于磨矿粒度难以在线检测且磨矿生产过程具有综合复杂特性，难以采用传统控制方法实现磨矿粒度的控制. 因此，建立磨矿粒度和关键工艺参数的动态模型对于磨矿运行控制和优化具有重要意义. 采用总量平衡原理获得磨矿粒度的微分方程模型多数情况下无法获得解析解. 而基于Monte Carlo （MC）方法的磨矿粒度模型能够精确模拟磨矿粒度分布的动态变化，但是其仿真效率低难以实用. 本文针对这一问题提出一种新的MC仿真方法：在定总量方法的基础上引入新的颗粒移除机制，在移除过程中动态地分配各个粒级颗粒数目并保持破裂前后各个粒级颗粒所占总颗粒数的百分比不变，避免颗粒移除过程中由于粒级差异导致的抽样误差，且避免MC仿真速度随着仿真推进下降的问题. 仿真实验验证表明，本方法能够在保证一定精度前提下显著提高磨矿粒度MC仿真的计算速度. 最后，通过一个实例介绍了本文仿真模型在磨矿优化控制中的应用.
- 磨矿过程 /
- 优化运行控制 /
- 粒度分布模型 /
- MonteCarlo仿真方法
Abstract: Grinding is an industrial process of reducing the particle size of ore, and the product particle size distribution (PSD) is the key quality index of the grinding process. Due to the difficulty of measuring PSD online and the comprehensive complexity of the grinding process, a PSD of the grinding process is difficult to control with traditional control methods. It is therefore important to establish the PSD model of grinding process to facilitate the optimization of its operation and control. Traditionally, the population balance principle is used to establish the PSD model, but it cannot arrive at an analytical solution in most cases. A Monte Carlo (MC) based model can accurately simulate the PSD dynamics, however it is too inefficient for practical use. In view of this problem, this paper proposes a new MC method which is developed based on the constant number MC (CNMC) method. This method develops a new particle removal mechanism to reduce the sampling noise caused by the CNMC removal operation. This can avoid the problem that the simulation speed decreases sharply with the simulation time. The simulation results validate that the proposed method can speed up the simulation while maintaining good accuracy. In the last, an application of the proposed model to the simulation of optimal control for a grinding process is introduced as an example.
- Grinding process /
- optimal operational control /
- particle size distribution (PSD) model /
- Monte Carlo (MC) simulation

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参考文献(18)

[1]	Chen Bing-Chen. Mathematical Models of Mineral Processing. Shenyang: Northeastern University Press, 1990. (陈炳辰. 选矿数学模型. 沈阳: 东北大学出版社, 1990.)
[2]	Chai T Y. Optimal operational control for complex industrial processes. In: Proceedings of the 8th IFAC Symposium on Advanced Control of Chemical Processes. Singapore: The International Federation of Automatic Control, 2012. 722-731
[3]	Chai Tian-You. Challenges of optimal control for plant-wide production processes in terms of control and optimization theories. Acta Automatica Sinica, 2009, 35(6): 641-649(柴天佑. 生产制造全流程优化控制对控制与优化理论方法的挑战. 自动化学报, 2009, 35(6): 641-649)
[4]	Zhou P, Chai T, Wang H. Intelligent optimal-setting control for grinding circuits of mineral processing process. IEEE Transactions on Automation Science and Engineering, 2009, 6(4): 730-743
[5]	Sbárbaro D. Dynamic Simulation and Model-based Control System Design for Comminution Circuits. London: Springer, 2010.
[6]	Schug B W, Nees M R, Gamarano T V. Process Simulation for Improved Plant Design through p&id Validation, Technical Report, Andritz Automation Inc., USA, 2012.
[7]	King R P. Modeling and Simulation of Mineral Processing Systems. Oxford: Butterworth-Heinemann, 2001.
[8]	Su Jun-Wei, Gu Zhao-Lin, Xu X Y. Advances of solution methods of population balance equation for disperse phase system. Scientia Sinica Chimica, 2010, 40(2): 144-160(苏军伟, 顾兆林, Xu X Y. 离散相系统群体平衡模型的求解算法. 中国科学-化学, 2010, 40(2): 144-160)
[9]	Mishra B K. Monte Carlo Method for the Analysis of Particle Breakage. London: Elsevier, 2007. 637-660
[10]	Gillespie D T. Stochastic simulation of chemical kinetics. Annual Review of Physical Chemistry, 2007, 58(1): 35-55
[11]	Khalili S, Lin Y, Armaou A, Matsoukas T. Constant number Monte Carlo simulation of population balances with multiple growth mechanisms. AIChE Journal, 2010, 56(12): 3137-3145
[12]	Mishra B K. Monte Carlo simulation of particle breakage process during grinding. Powder Technology, 2000, 110(3): 246-252ewpage
[13]	Lee K, Matsoukas T. Simultaneous coagulation and break-up using constant-N Monte Carlo. Powder Technology, 2000, 110(1-2): 82-89
[14]	Zhao Hai-Bo, Zheng Chu-Guang, Xu Ming-Hou. Multi-Monte Carlo method for simultaneous coagulation and breakage of nanoparticles. Proceedings of the Chinese Society for Electrical Engineering, 2005, 25(16): 96-101(赵海波, 郑楚光, 徐明厚. 用多重蒙特卡罗算法研究超细微颗粒物同时发生的凝并和破碎. 中国电机工程学报, 2005, 25(16): 96-101)
[15]	Kostoglou M. Handbook of Powder Technology. London: Elsevier, 2007. 793-835
[16]	Rajamani K, Pate W T, Kinneberg D J. Time-driven and event-driven Monte Carlo simulations of liquid-liquid dispersions: a comparison. Industrial and Engineering Chemistry Fundamentals, 1986, 25(4): 746-752
[17]	Dai Wei, Chai Tian-You. Data-driven optimal operational control of complex grinding processes. Acta Automatica Sinica, 2014, 40(9): 2005-2014 (代伟, 柴天佑. 数据驱动的复杂磨矿过程运行优化控制方法. 自动化学报, 2014, 40(9): 2005-2014)
[18]	Nageswararao K, Wiseman D M, Napier-Munn T J. Two empirical hydrocyclone models revisited. Minerals Engineering, 2004, 17(5): 671-687