SIA OpenIR  > 信息服务与智能控制技术研究室
污水处理过程污泥膨胀预测方法研究
其他题名Research on Sludge Bulking Prediction Methods of Wastewater Treatment Process
王磊1,2
导师苑明哲 ; 于广平
分类号X502
关键词污泥膨胀 粗糙集 灰色模型 趋势识别 专家系统
索取号F25/W34/2004
页数63页
学位专业控制理论与控制工程
学位名称硕士
2013-05-28
学位授予单位中国科学院沈阳自动化研究所
学位授予地点沈阳
作者部门信息服务与智能控制技术研究室
摘要污水处理厂作为水污染防治体系的核心,扮演着十分重要的角色,减少污水处理厂异常工况特别是严重异常工况的发生,保证其持续稳定的运行,对环境保护和水资源可持续利用都有着重要意义。污泥膨胀是污水处理厂面临的最严重异常工况之一,导致污泥沉淀性能恶化,严重影响出水水质,从而破坏污水处理厂的正常运行。 本文首先介绍了污泥膨胀预测的研究背景及意义,然后对主要污泥膨胀预测方法进行评述,然后基于运行参数定量和定性信息,结合专家知识构建污水处理过程污泥膨胀预测专家系统。 本文对反映污泥膨胀的污泥体积指数建立了软测量方法,采用粗糙集对辅助变量约简,然后用径向基神经网络构造软测量模型,以补全污泥体积指数数据集。然后用灰色马尔科夫模型对其进行了预测,从定量上获取了污泥膨胀重要信息。 在趋势分析技术发展的基础上,对离线趋势提取方法加以改进,使之能够更好的适应在线趋势提取。该方法首先采用外推式在线分割算法实现了对在线数据的合理分割,然后用最小二乘法对片段数据进行拟合,提高了趋势提取效率。通过示例表明,本文提出的在线趋势提取算法可有效提取过程数据的趋势信息,并且计算复杂度较低,能应用于工业在线过程,为专家系统趋势信息需求提供支持。 然后设计了污泥膨胀专家系统的结构:首先介绍了系统污泥膨胀知识的获取过程,并利用模糊技术对知识进行了模糊化,构造故障树,获得了相关规则。同时基于Clips引入不确定性推理技术设计了污泥膨胀专家系统的推理机,设计了推理策略。 最后采用Clips作为专家系统内核,并成功将Clips嵌入到Visual C++中,实现了Visual C++和Clips的混合编程。并用辽宁某污水处理厂工艺运行数据进行了离线分析,结果表明此系统可以预测污泥膨胀发生。
其他摘要As the core of water pollution prevention and control system, wastewater treatment plants play a very important role. Reducing the occurrence of the abnormal conditions of wastewater treatment plants, especially the serious ones, and guarantee the continuous, stable operation is of great significance to environment protection and the sustainable utilization of water resource. Sludge bulking is one of the most serious abnormal conditions that wastewater treatment plants are facing, causing the deterioration of sludge's sedimentation properties, thereby greatly affecting effluent quality and destroying the normal operation of wastewater treatment plants. This paper has, in the beginning, introduced the research background and meanings of sludge bulking prediction, and then evaluated the main methods of sludge bulking prediction, and, based on the quantitative and qualitative operation parameters and by combining expert knowledge, established the sludge bulking prediction expert system for wastewater treatment. This paper has applied soft-sensing technology for the sludge volume index that reflects sludge bulking, using rough set to reduce the instrumental variables then construct the soft-sensing model with RBF neural network to complete the dataset of sludge volume index, and then, employed the gray Markov model to predict the dataset to collect the important information of sludge bulking in the quantitative respect. Based on the development of trend analysis technology, the extracting method of off-line trend was improved to make it better adapted to on-line trend extraction. This method has firstly realized the reasonable segmentation of on-line data by applying the algorithm of extrapolation for on-line segmentation of data, then fitted fragmental data by using least square method, which has enhanced trend extraction efficiency. Examples showed that the on-line trend extraction algorithm proposed in this paper can effectively extract the trend information of process data, and its computational complexity is relatively low, enabling it to be employed in industrial on-line process and provide support for the trend information demand of the expert system. Then the structure for sludge bulking expert system was designed: first explained the acquisition process of the sludge bulking knowledge of the system, and blurred the knowledge by utilizing the fuzzy technology, constructed the fault-tree and acquired relevant rules. Meanwhile, based on Clips, uncertainty reasoning technology was introduced to design the reasoning machine for the sludge bulking expert system and reasoning strategies. Finally, this paper has applied Clips as the inner core of the expert system, and successfully integrated Clips into Visual C++, and realized the mixed programming of Visual C++ and Clips, and conducted off-line analysis for the process operation data of a wastewater treatment plant in Liaoning. And the results showed that the system is able to predict sludge bulking.
语种中文
产权排序1
文献类型学位论文
条目标识符http://ir.sia.cn/handle/173321/10752
专题信息服务与智能控制技术研究室
作者单位1.中国科学院沈阳自动化研究所
2.中国科学院大学
推荐引用方式
GB/T 7714
王磊. 污水处理过程污泥膨胀预测方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2013.
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