【作者】 管秋；

【导师】 王万良； 陈胜勇；

【作者基本信息】 浙江工业大学 ， 计算机应用技术， 2005， 硕士

【摘要】 随着近现代工业的迅猛发展,作为人类赖以生存的水资源遭到了极大的破坏,如何控制水资源的进一步恶化,采取行之有效的污水处理方法已成为人类迫在眉睫的头等大事。 对于污水的监测和处理存在着许多方法。目前污水处理厂广泛使用序批式活性污泥法(SBR法)。由于污水中有机物、N和P的检测实时性差,使实际污水处理缺乏有效的控制参数。目前实际采用的方法是按某一既定的反应时间控制污水处理的运行,而且处理质量缺乏可靠性,可能出现处理出水不能达标的问题。实时、准确、经济地测量污水处理指标,从而实现污水处理过程的闭环控制,以节约能源和时间,保证质量,是当前污水处理企业迫切需要解决的问题。 本文从工程应用角度,将软测量方法应用于污水水质指标的实时检测,为污水水质指标的实时检测提供了新的思路。污水处理过程是一种典型的复杂动态生物反应工程系统,具有非线性、时变性、随机性和不确定性等特点,难以建立软测量模型中主导变量和辅助变量的关系。本文将人工神经网络软测量方法应用于污水水质指标的实时检测,充分利用了神经网络很强的非线性逼近能力和学习能力,取得了很好的效果。 本文首先归纳总结了现有的污水处理方法,系统分析了污水水质指标(BOD、COD、N、P等)和在线检测的参数(pH值、氧化还原电位ORP、溶解氧DO)之间的关系,提出了SBR法的软测量模型。该模型以BOD、COD、N、P为软测量的输出主导变量,以pH值、氧化还原电位ORP、溶解氧DO为输入的辅助变量。然后,根据建立污水处理软测量模型的需要,系统研究了污水处理过程参数的仪表检测和实验测定方法。接着,结合SBR法的特点和神经网络建模的特点,分别建立了基于BP和RBF的污水指标软测量模型,并用污水处理的工程实验测定数据,对网络进行了训练和仿真。结果表明,人工神经网络软测量的方法能很好地实现出水指标的实时估计,为实现闭环控制奠定了基础,具有重要的理论意义和工程应用价值。 本文最后给出了今后的研究方向:用遗传算法优化神经网络结构,优化连接权和学习规则的进化;用智能控制方法和技术实现污水处理的闭环控制。更多还原

【Abstract】 With the rapidly development of modern industry, the water resource was destroyed more and more seriously, which is human’s subsistence, so the process control of wastewater treatment has been the focus of study in recent years.Many methods have been put forward concerning wastewater treatment. The methods of Sequencing Batch Reactor (SBR) is used more and more widely because of its so many advantages. But, because the wastewater quality parameters (such as BOD, COD, N, P and so on) can’t be detected on-line or real-time measured worse, its lead to the short of effective control parameters and affects the control effect and economic benefit deeply. Wastewater treatment is controlled by settled-time at present. Thus, how to gain the wastewater quality parameters on-line exactly and economically to carry out close-loop control has been the key to improve the control effectiveness for enterprises.Because SBR is a typical complex dynamic engineering system of biology, it is nonlinear, time-variable, stochastic, uncertain and difficult to establish mathematics model between Primary variable and Secondary variable. This paper applied soft-measurement technology based on neural network to online-measurement of wastewater treatment according to engineering application, made the best of the capability of networks’ nonlinear approach and learning and got a good result.Firstly, this paper summed up the principle and treatment of wastewater at present. Secondly, this paper analyzed the relation between treatment specification and the parameters that could be measured on-line systematically, and proposed the soft-sensing modeling method. Thirdly, in terms of the need to modeling the soft-sensing model of wastewater, studied parameters measurement method by instrument and experiment respectively. Fourthly, according to the characteristics of SBR and modeling based on neural network, the soft-sensing method based on BP (back-propagation) and RBF (radial basis function) neural networks is proposed to solve this problem accordingly. The models were trained by the data from the experiments of wastewater treatment; the results of network training coincide with those of wastewater treatment in practice. Therefore, it can be safely said that the soft-sensing system of wastewater treatment based on BP and RBF neural networks is capable of effectively resolving the problem quality parameters real-time estimation in wastewater treatment.更多还原