选煤信息化系统的构建及其智能控制技术的探究与应用

发布时间：2018-04-27 21:43

本文选题：信息化平台 + 重介质选煤　；参考：《曲阜师范大学》2017年硕士论文

【摘要】：煤炭是一种不可再生能源,随着生产生活的消耗,其储量迅速下降。但是其作为国家基础性能源的地位没有变,国家众多产业还是以其为基础。但是目前就国内情况来说,我国煤炭利用率不高,选煤厂信息化水平较低,选煤精度较差。尤其是在重介质选煤密度控制问题上,采用常规PID控制,易出现大滞后、非线性,现有的选煤工艺已经不能满足市场的要求,并且整个生产过程中信息化存在“孤岛效应”。本文旨在实现选煤过程的信息化控制,设计重介质选煤智能技术控制系统,来提高选煤的产量和精度。本文以选煤厂为工业背景,介绍了智能控制技术在选煤信息化系统中的实际应用,叙述了选煤工艺流程,针对选煤厂的信息滞后问题,设计了以工业以太网为核心的综合信息化平台。该平台将储运集控中心、原煤集控中心、中心调度室、重介水洗车间、储运配电室和重介选矸等分别安装以太网交换机并铺设光缆,形成一副巨大的工业环网,由调度中心统一管理调控。实现关键工艺节点参数在线检测、信息化数据传输,选煤过程控制自动化以及生产数据综合到综合信息化平台上,实现数据的整合。从而为管理人员提供重要数据参考,来对现场设备的运行状态自动检测,实现选煤系统信息化控制。重介质旋流器中的悬浮液密度是影响选煤精度和质量的关键因素,不同密度波动下能生成含杂率不同的精煤,需要控制密度在恒定状态下才能分离出最优的产品。为了实现重介选煤过程的数据自动调控和密度恒定控制目标,本文重点研究了重介质密度控制系统,利用计算法建立了数学模型,近似为二阶纯迟延函数。设计了重介质密度控制器,该控制器基于PSO和BP神经网络算法,结合PSO算法的选优能力和BP神经网络的自学习能力,将控制重介质旋流器悬浮液密度的PID参数进行了优化,实现了控制参数的实时在线整定。通过仿真结果显示,该算法响应速度快、稳态误差小、鲁棒性和可调性强,能很好的解决重介选煤过程中出现的大滞后、非线性问题。其算法实现过程主要是通过OPC技术实现对工艺过程的数据采集,然后借助MATLAB软件工具实现PSO_BPPID优化算法,最终把运算后的数据送给监测平台进而传送给现场控制装置。此过程中监测平台与控制装置借助于实时以太网技术实现简单的数据传输。通过对选煤过程的优化设计,大大提高了选煤过程中的可靠性和稳定性,设备故障频率明显降低,控制精度和生产效率明显提高,同时以太网技术的引入也大大提高了煤矿生产过程的信息化建设。
[Abstract]:Coal is a kind of non-renewable energy, with the consumption of production and life, its reserves decline rapidly. But its status as the national basic energy has not changed, the country's many industries are based on it. But at present, the utilization ratio of coal is not high, the information level of coal preparation plant is low, and the precision of coal preparation is poor. Especially in the density control of heavy medium coal preparation, the conventional PID control is easy to appear large lag, nonlinear, the existing coal preparation technology can not meet the requirements of the market, and the entire production process information exists in the "island effect". The purpose of this paper is to realize the information control of coal preparation process and to design the intelligent control system of heavy medium coal preparation to improve the production and precision of coal preparation. Taking the coal preparation plant as the industrial background, this paper introduces the practical application of intelligent control technology in the coal preparation information system, and describes the coal preparation process, aiming at the information lag problem of the coal preparation plant. A comprehensive information platform based on industrial Ethernet is designed. The platform will install Ethernet switch and lay optical cable in storage and transportation centralized control center, raw coal centralized control center, central dispatching room, heavy medium washing workshop, storage and distribution room and heavy medium gangue respectively, forming a huge industrial ring network. By the dispatch center unified management control. The key process node parameters on-line detection, information data transmission, coal preparation process control automation and production data integration on the integrated information platform are realized, and the data integration is realized. So it can provide important data reference for managers to automatically detect the running state of field equipment and realize the information control of coal preparation system. The density of suspensions in heavy medium hydrocyclone is the key factor to affect the precision and quality of coal preparation. Under different density fluctuation, the fine coal with different impurity content can be produced, and the optimal product can be separated only when the density is controlled in a constant state. In order to realize automatic data control and constant density control in heavy medium coal preparation process, the density control system of heavy medium is studied in this paper, and the mathematical model is established by using the calculation method, which is approximate to the second order pure delay function. A dense medium density controller is designed. Based on the algorithm of PSO and BP neural network, the parameters of PID which control the density of suspensions of heavy medium cyclone are optimized by combining the optimization ability of PSO algorithm and the self-learning ability of BP neural network. Real-time online tuning of control parameters is realized. The simulation results show that the algorithm has the advantages of high response speed, small steady-state error, strong robustness and tunability, and can solve the problem of large lag and nonlinearity in the process of heavy coal preparation. The realization process of the algorithm is mainly to realize the data acquisition of the process process through OPC technology, and then realize the PSO_BPPID optimization algorithm by means of the MATLAB software tool. Finally, the data after calculation is sent to the monitoring platform and then transferred to the field control device. In this process, the monitoring platform and control device realize simple data transmission by means of real-time Ethernet technology. Through the optimization design of coal preparation process, the reliability and stability of coal preparation process are greatly improved, the frequency of equipment fault is obviously reduced, and the control precision and production efficiency are obviously improved. At the same time, the introduction of Ethernet technology has greatly improved the information construction of coal mine production process.
【学位授予单位】：曲阜师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TD94;TP273

【参考文献】