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题名: 球磨机水泥生料粉磨过程先进控制系统研究与应用
其他题名: Research and Application of Advanced Process Control of Ball Mill Cement Raw Material Grinding Process
作者: 吴星刚
导师: 于海斌 ; 苑明哲
分类号: TP273
关键词: 球磨机 ; 生料粉磨 ; 仿真环境 ; 先进控制 ; 粒子群优化
索取号: TP273/W86/2010
学位专业: 机械电子工程
学位类别: 博士
答辩日期: 2010-01-31
授予单位: 中国科学院沈阳自动化研究所
学位授予地点: 中国科学院沈阳自动化研究所
作者部门: 工业信息学研究室
中文摘要: 水泥生料(本文中简称生料)粉磨过程作为水泥生产流程中的一个重要环节,其主要任务是利用粉磨设备将成分、硬度、粒度不同的多种原料混合后通过撞击、挤压、摩擦等作用破碎成粒度、成分均符合后续工序要求的物料。由于球磨机粉磨过程本身所具有的严重的非线性、时变性、多变量耦合、大时滞等特性,以及机理不十分明确和关键工艺参数难以在线测量等因素,导致该过程难以实现稳定的自动控制,对先进控制系统设计和应用而言是一个极具挑战性的问题。本文针对这些问题,进行了系统机理分析、过程模拟仿真、关键变量软测量、过程回路优化控制和先进控制系统软件的设计和应用等方面的研究,取得了如下成果: 1)建立了球磨机生料粉磨过程的模拟仿真环境。在对球磨机生料粉磨过程进行综合分析的基础上,建立一套球磨机生料粉磨过程的粒子人口平衡模型(Particle Population Balance Model,PBM),并根据所确定的模型,借助MATLAB的SIMULINK仿真软件创建仿真模型,对球磨机内部流场以及球磨机生料粉磨过程进行动态仿真,分析不同的球磨机结构以及不同工艺流程对产品质量和系统效率的影响,并进行了过程工艺参数的分析和优化评估。 2)提出了球磨机生料粉磨过程关键变量的软测量建模方法。提出了基于粒子群优化模糊神经网络的软测量建模与校正方法,给出了模糊神经网络的结构以及利用粒子群优化算法进行网络参数训练的具体方法和步骤。基于提出的方法,实现了对成品生料的质量参数实时、连续地估计和预测,包括成品生料的粒度和成分。现场数据的预测结果表明,该预测方法同BP网络模型相比具有更好的泛化性能。 3)提出了球磨机生料粉磨过程的先进控制和优化方法。球磨机生料粉磨过程负荷模糊控制;球磨机生料粉磨过程成品生料质量的推断控制,包括生料配料过程的推断预测控制和成品生料粒度的推断前馈控制;生料配料过程的多目标优化方法,利用基于空间距离的多目标粒子群优化算法实现生料目标率值的优化设定。实现了球磨机生料粉磨过程的稳定控制,提高了粉磨产品的质量。 4)进行了生料粉磨过程先进控制系统的设计和开发。采用多层分布式软件设计方法进行开发和集成,降低了开发和调试的难度,提高了先进控制系统的兼容性和灵活性,为软件的应用提供了方便。
英文摘要: As an important part of cement production process, the main task of raw material grinding process is to obtain both hardness and particle size qualified raw materials through impact, extrusion and friction effect, and ready for the follow-up processes. As the ball mill grinding process has the complexity, severe non-linear, time-varying, multivariable coupling, large delay, and the mechanism is not very clear as well as the lack of on-line measurement of key process parameters etc., led to the process difficult to achieve stability in automatic control. All these make the advanced control system design a challenging task. In this paper, research issues including systematic mechanism analysis, process simulation, key variables soft-sensing, optimal process loop control and advanced process control system software design, are discussed. The detail works are as follows. 1) A simulation analysis environment of ball mill raw material grinding process is established. A particle population balance model (PBM) is built based on the comprehensive analysis of ball mill raw material grinding process. With the simulation analysis environment established using MATLAB/SIMULINK software, study on dynamic simulation of the internal flow field and Particle size distribution of materials inside mill has been performed. Optimized evaluation has been given of impact of structures, process flow and process parameters on product quality and mill system efficiency. 2) Soft sensor modeling method of the key variables of ball mill raw material grinding process is proposed. The soft sensor models are based on fuzzy neural network trained by particle swarm optimization algorithm, and has realized the on-line, real-time and continuous estimation of product quality, including product particle size and composition. The verification results show that the proposed model has better generalization performance in comparison with the BP network model. 3) Advanced process control and optimization methods of ball mill raw material grinding process are proposed. Fuzzy control strategy of ball mill load, inferential control methods including product modulus value inference predictive control and product particle size inference feed forward control, and optimal setting method based on multi-objective PSO of modulus set value in cement raw materials grinding process is also proposed. All these effort realized the accurate and stable control of ball mill raw material grinding process, improved the quality of product. 4) Software of advanced process control and optimization system are designed and developed based on multilayer distributed component software structure. The difficulty of development and debugging is reduced, and compatibility and flexibility of advanced control system software and convenience for system applications is improved.
语种: 中文
产权排序: 1
内容类型: 学位论文
URI标识: http://ir.sia.cn/handle/173321/9367
Appears in Collections:工业信息学研究室_学位论文

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Recommended Citation:
吴星刚.球磨机水泥生料粉磨过程先进控制系统研究与应用.[博士学位论文].中国科学院沈阳自动化研究所.2010
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