废水处理过程的典型相关分析建模方法研究

Study on modeling methods of wastewater treatment processes with canonical correlation analysis

随着公众环保意识的增强,废水达标排放成为工业生产中至关重要的一步。传统的污水出水水质预测模型是基于静态数据模型,这样不仅忽略了过程变量中的动态有效信息,还影响了模型预测的精度,降低了模型的泛化能力。在考虑了过程变量的时变与动态特性的基础上,将时间差分方法嵌入到典型相关分析模型中,分析了时间差分阶数变化对模型预测精度的影响。与传统的典型相关分析建模方法相比,基于时间差分的典型相关分析模型对出水化学需氧量的预测均方根误差由1.5028下降至0.5645,相关系数由0.4227提高到0.8470;对于出水总氮,其均方根误差由2.3440下降到1.1926,相关系数由0.4059提高到0.7936。模型的预测精度与泛化能力均得到提高。

With the improvement of public awareness of environmental protection,the discharge of industrial wastewater became a crucial issue in industrial production.The typical water quality models were based on static models which ignored the dynamic information in process variables,resulting in the reduction in the accuracy of model prediction and the generalization ability of the models.Considering the time-varying and dynamic characteristics of process variables,a time difference model embedded into canonical correlation analysis was proposed in this paper.The effect of the order of the time difference model on the prediction accuracy was also analyzed.Compared with the traditional canonical correlation analysis,the root mean square error values of effluent chemical oxygen demand and effluent total nitrogen were reduced from 1.5028 to 0.5645 and from 2.3440 to 1.1926,respectively.The correlation coefficient values were increased from 0.4227 to 0.8470 and from 0.4059 to 0.7936,respectively.The results indicated that the prediction accuracy and generalization ability of the model were both improved.