山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (3): 133-142.doi: 10.6040/j.issn.1672-3961.0.2019.009
• 其他 • 上一篇
Hongbin LIU1,2(),Qiyue WU1,Liu SONG1
摘要:
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。
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