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作 者:刘鸿斌 吴启悦[1] 宋留 LIU Hongbin;WU Qiyue;SONG Liu(Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources,Nanjing Forestry University,Nanjing 210037,Jiangsu,China;State Key Laboratory of Pulp and Paper Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China)
机构地区:[1]南京林业大学江苏省林业资源高效加工利用协同创新中心,江苏南京210037 [2]华南理工大学制浆造纸工程国家重点实验室,广东广州510640
出 处:《山东大学学报(工学版)》2020年第3期133-142,共10页Journal of Shandong University(Engineering Science)
基 金:制浆造纸工程国家重点实验室开放基金资助项目(201813);南京林业大学高层次人才科研启动基金(GXL029)。
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。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.
关 键 词:废水处理 出水水质 变量选择 变量投影重要性 偏最小二乘
分 类 号:X703[环境科学与工程—环境工程]
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