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。
In view of the long lag, nonlinearity,multiple input fac tor, uncertainty, time-varying and fuzzy charac-teristics of the dosing process of tap water production,an automatic control system for coagulant dosage of water-works 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 con-sumption statistics,fluctuation assessment, and alarm settings. The system has been applied to Zongguan water-works successfully. Online monitoring of operation parameters and full automation has been achieved, which pro-vides safeguard for the plant’s safe production. The system also saved dosage consumption,and reduced labor in-tensity of operators.
作者
饶小康
贾宝良
鲁立
RAO Xiao- kang JIA Bao- liang LU Li(Instrumentation and Automation Department,Yangtze River Scientific Research Institute,Wuhan 430010, China)
出处
《长江科学院院报》
CSCD
北大核心
2017年第5期135-140,共6页
Journal of Changjiang River Scientific Research Institute
基金
长江科学院技术开发和成果转化基金项目(CKZS2014004/YQ)
