Research of sludge compost maturity degree modeling method based on classify support vector machine for sewage treatment

Jingwen Tian, Meijuan Gao, Hao Zhou
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Abstract

Because of the complicated interaction of the sludge compost components, it makes the judging system of sludge compost maturity degree appear the non-linearity and uncertainty. According to the physical circumstances of sludge compost, a sludge compost maturity degree modeling method based on support vector machine (SVM) is presented. We select the index of sludge compost maturity degree and take the high temperature duration, moisture content, volatile solids, the value of fecal bacteria, and germination index as the judgment parameters. We construct the structure of SVM network that used for the maturity degree judgment of sludge compost, and use the genetic algorithm (GA) to optimize SVM parameters. With the ability of strong self-learning and well generalization of SVM, the modeling method can truly judge the sludge compost maturity degree by learning the index information of sludge compost maturity degree. The experimental results show that this method is feasible and effective.
基于分类支持向量机的污水处理污泥堆肥成熟度建模方法研究
由于污泥堆肥组分之间复杂的相互作用,使得污泥堆肥成熟度判断系统呈现出非线性和不确定性。根据污泥堆肥的物理情况,提出了一种基于支持向量机(SVM)的污泥堆肥成熟度建模方法。选取污泥堆肥成熟程度指标,以高温持续时间、含水率、挥发性固形物、粪菌值、发芽指数作为判断参数。构建了用于污泥堆肥成熟度判断的支持向量机网络结构,并利用遗传算法对支持向量机参数进行优化。该建模方法具有较强的自学习能力和较好的支持向量机泛化能力,通过学习污泥堆肥成熟度指标信息,能够真实判断污泥堆肥成熟度。实验结果表明,该方法是可行和有效的。
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