变量选择在废水处理过程软测量建模中的应用

化学需氧量与悬浮固形物含量是造纸工业废水排放中需要重点监测的指标,建立有效的废水出水水质预测模型是优化控制废水中污染物排放量的有效方法。由于实际工业废水处理过程的复杂性,可测变量之间存在强相关性,利用偏最小二乘法提取变量的投影重要性信息进行变量选择,将选择后的最优变量子集作为软测量模型的输入,建立出水水质的最优预测模型。以最小二乘支持向量机模型为例,基于变量选择的最小二乘支持向量机模型对出水化学需氧量进行预测时均方根误差降低了15.2%,相关系数提高了14.4%;对于出水悬浮固形物模型,均方根误差降低了20.5%,相关系数提高了16.1%。结果表明在建模时进行变量选择可以降低模型的复杂度和提高模型的泛化能力。

Application of variable selection in soft sensor modeling of wastewater treatment processes

Chemical oxygen demand and suspended solid were important monitoring indices of effluent discharge in paper-making industry. An effective model of effluent quality of wastewater treatment processes was of key importance to monitoring and controlling pollution emission. Concerning the strong correlations among the input variables and the complicated characteristics of wastewater treatment processes in paper-making industry, partial least squares (PLS) method was applied to extract information of variables importance in projection (VIP) for variable selection (VS). Then the optimal variables were chosen as new input variables for soft sensor models to predict the effluent qualities of a papermaking wastewater treatment process. Compared to the LSSVM model, the root mean square error (RMSE) of VS-based LSSVM model was reduced by 15.2%, and the correlation coefficient (r) was increased by 14.4%. For the effluent SS, the value of RMSE was decreased by 20.5%, and the value of r was increased by 16.1%. The results showed that the proposed method not only reduced the model complexity, but also enhanced the model generalization capacity.