长江科学院院报 ›› 2017, Vol. 34 ›› Issue (5): 135-140.DOI: 10.11988/ckyyb.20160194

• 信息技术应用 • 上一篇    下一篇

基于人工神经网络算法的水厂混凝投药控制系统研究与开发

饶小康, 贾宝良, 鲁立   

  1. 长江科学院 仪器及自动化研究所,武汉 430010
  • 收稿日期:2016-03-07 修回日期:2016-04-11 出版日期:2017-05-01 发布日期:2017-05-17
  • 作者简介:饶小康(1985-),男,湖北黄冈人,工程师,硕士,主要从事水利水电工程施工数字化研究,(电话)18140555722(电子信箱)283139246@qq.com。
  • 基金资助:
    长江科学院技术开发和成果转化基金项目(CKZS2014004/YQ)

Research and Development of Coagulation Dosage Control System for a Waterworks Based on Artificial Neural Network

RAO Xiao-kang, JIA Bao-liang, LU Li   

  1. Instrumentation and Automation Department, Yangtze River Scientific Research Institute, Wuhan 430010, China
  • Received:2016-03-07 Revised:2016-04-11 Online:2017-05-01 Published:2017-05-17

摘要: 针对自来水生产投药工艺长滞后、非线性、多输入因子、不确定性、时变性、模糊性等特点,采用人工神经网络算法对周围环境自适应和自学习,研究和开发了一套用于水厂混凝投药的自动控制系统。系统以武汉市第一大水厂——宗关水厂为例,研究了Elman神经网络算法对控制系统混凝投药效果的影响,并基于OLE-DB开放性数据访问标准实现对WinCC工控系统样本数据读取和存储的预处理。系统主要包括投药工艺、数据查询、曲线生成、配药查询、报警日志、报警统计、药耗统计、波动评价、报警设置等功能模块,在宗关水厂的成功运行实现了混凝投药工艺生产运行参数的在线监视和全自动化运行。为水厂的安全生产提供了保障,达到了节约药耗、减少人工、降低操作人员劳动强度的目的。

关键词: Elman神经网络, 自来水厂, 混凝投药, WinCC, 控制系统

Abstract: In view of the long lag, nonlinearity, multiple input factor, uncertainty, time-varying and fuzzy characteristics of the dosing process of tap water production, an automatic control system for coagulant dosage of waterworks is developed based on the self-adaption and self-learning of artificial neural network. Zongguan waterworks, the first largest waterworks in Wuhan, is taken as a case study. The influence of Elman neural network on dosage effect is researched, and the preprocessing and data storage and data reading for WinCC industrial control system are accomplished based on OLE-DB open data access standard. The system mainly consists of functional modules including dosing process, data query, curve generation, dosage query, alarm log and alarm statistics, drug consumption statistics, fluctuation assessment, and alarm settings. The system has been applied to Zongguan waterworks successfully. Online monitoring of operation parameters and full automation has been achieved, which provides safeguard for the plant’s safe production. The system also saved dosage consumption, and reduced labor intensity of operators.

Key words: Elman neural network, waterworks, coagulation dosage, WinCC, control system

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