[1]王冬生,李世华,周杏鹏.基于PSO-RBF神经网络模型的原水水质评价方法及应用[J].东南大学学报(自然科学版),2011,41(5):1019-1023.[doi:10.3969/j.issn.1001-0505.2011.05.024]
 Wang Dongsheng,Li Shihua,Zhou Xingpeng.Assessment method of raw water quality based on PSO-RBF neural network model and its application[J].Journal of Southeast University (Natural Science Edition),2011,41(5):1019-1023.[doi:10.3969/j.issn.1001-0505.2011.05.024]
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基于PSO-RBF神经网络模型的原水水质评价方法及应用()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
41
期数:
2011年第5期
页码:
1019-1023
栏目:
自动化
出版日期:
2011-09-20

文章信息/Info

Title:
Assessment method of raw water quality based on PSO-RBF neural network model and its application
作者:
王冬生李世华周杏鹏
(东南大学复杂工程系统测量与控制教育部重点实验室,南京 210096)
Author(s):
Wang DongshengLi ShihuaZhou Xingpeng
(Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education, Southeast University,
Nanjing 210096, China)
关键词:
原水水质评价RBF神经网络粒子群优化算法前馈控制
Keywords:
raw water quality assessment RBF neural network particle swarm optimization algorithm feed-forward control
分类号:
TP183;X824
DOI:
10.3969/j.issn.1001-0505.2011.05.024
摘要:
针对自来水生产过程的原水水质评价问题,提出了一种基于PSO-RBF神经网络模型的原水水质评价方法.首先,根据水厂生产经验和历史数据分析,制定面向自来水生产过程的原水水质评价标准.然后,采用粒子群优化(PSO)算法训练的RBF神经网络模型,对苏州市相城水厂的进厂原水水质实施在线评价.最后,将进厂原水水质在线评价结果作为前馈量,增加相城水厂药剂(矾和臭氧)投加过程的前馈控制环节,使得药剂投加量能够根据原水水质的变化及时做出调整.实际应用效果表明,与改进前的反馈控制过程相比,过程出水水质更加平稳,提高了自来水生产过程应对原水水质变化的能力.
Abstract:
In consideration of the assessment problem of raw water quality oriented to drinking water treatment process, an assessment method of raw rater quality based on the PSO-RBF neural network model is proposed. First, on the basis of productive experiences and analysis of historical data in the waterworks, an assessment standard oriented to the process of drinking water treatment is established. Then, the radial basis function (RBF) neural network model trained by the particle swarm optimization (PSO) algorithm is used for the on-line assessment of raw water quality in the Xiangcheng Waterworks of Suzhou city. Finally, feed-forward control elements are added to the pharmaceutical (alum and ozone) dosing control processes of Xiangcheng Waterworks, using the on-line assessment result as the feed-forward compensation. The results of the practical operation show that the produced water quality becomes more stable, and the adaptation ability of drinking water treatment to the variation of raw water quality is improved.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介:王冬生(1983—),男,博士生;周杏鹏(联系人),男,教授,博士生导师,zxpseu@126.com.
基金项目:江苏省科技支撑计划资助项目(BS2007113).
引文格式: 王冬生,李世华,周杏鹏.基于PSO-RBF神经网络模型的原水水质评价方法及应用[J].东南大学学报:自然科学版,2011,41(5):1019-1023.[doi:10.3969/j.issn.1001-0505.2011.05.024]
更新日期/Last Update: 2011-09-20