基于区间二型模糊神经网络的出水氨氮软测量

摘要

针对污水处理过程出水氨氮(ammonia nitrogen,NH4-N)难以实时检测的问题,提出了一种基于区间二型模糊神经网络(interval type-2 fuzzy neural networks,IT2FNN)的软测量方法,建立了出水NH4-N的软测量模型,实现了出水NH4-N的实时检测.首先,采集和预处理相关过程变量的实际运行数据,通过主元分析法筛选出与出水NH4-N相关性较强的过程变量.其次,利用IT2FNN建立所选变量与出水NH4-N的软测量模型,通过梯度下降算法对模型相关参数进行修正.最后,将基于IT2FNN的出水NH4-N软测量模型应用于实际污水处理过程.实验结果表明,提出的出水NH4-N软测量方法不仅能够实现污水处理过程出水NH4-N的实时检测,而且具有较高的检测精度.%A soft-sensor method for online detection of effluent ammonia nitrogen (NH4-N) in waste water treatment process was proposed on the basis of interval type-2 fuzzy neural networks (IT2FNN). First, actual operation data related to pre-treatment process variables was collected and process variables having strong correlation to effluent NH4-N were selected by principal component analysis (PCA) technique. Second, a self-sensor model between principal component variables and effluent NH4-N was establishedvia IT2FNN and model parameters were adjusted by gradient algorithm. Finally, the proposed soft-sensor method was used in a real waste water treatment process (WWTP). The experimental results show that the new method can predict effluent NH4-N online with better accuracy than traditional methods.