摘要
针对污水处理过程在运行工况频繁波动的情况下,单一水质软测量模型精度下降的问题,提出了污水水质集成软测量建模方法.模型由3层结构组成:基于模糊聚类-极限学习机(ELM,extreme learning machine)的预测子模型位于最底层,第2层采用自适应加权融合方法将子模型预测值进行集成,最上层采用基于信息熵的元学习机制管理融合权值.ELM的快速学习特点使模型具有较好的实时性能,自适应加权融合方法和元学习机制提高了模型泛化性,元学习机制跟踪污水处理过程运行状况的动态变化趋势.仿真结果表明,在多工况条件下,污水水质COD(chemical oxygen demand,化学需氧量)集成软测量模型具有较好的精度.
A soft-sensor of water quality for wastewater treatment plants,which is based on an integrated model,is presented.The proposed soft-sensor aims to address the difficulty in using a single model to represent the characteristics of wastewater treatment processes with varying operating regimes.The soft-sensor is composed of three layers,in which a predictive sub-model based on FCM-ELMs are the bottom layer,adaptive weighted fusion method fusing predictive values of the sub-model are the middle layer,and a meta-learning mechanism based on information entropy updating fusion weights is the top layer.The meta-learning mechanism can track the dynamic trend of operating conditions of wasterwater treatment plants.The quick learning advantage of ELM results in the soft-sensor showing excellent real-time performance.The adaptive weighted fusion method and meta-learning mechanism improve the model generalization.Simulation results show that the integrated model for COD is more accurate than other models.
出处
《信息与控制》
CSCD
北大核心
2014年第2期248-252,共5页
Information and Control
基金
中国博士后科学基金面上资助项目(2013M530953
2013M532118)
国家自然科学基金资助项目(61034008
61004051)
关键词
污水处理
软测量
自适应加权融合
元学习
wastewater treatment
soft-sensor
adaptive weighted fusion
meta-learning
