研究报告

  • 窦微笑,周振,周凯鹏,魏海娟,蒋路漫,李晶,裘湛,胡大龙.污泥水磷和有机物同步混凝去除的多目标优化技术研究[J].环境科学学报,2016,36(12):4400-4406

  • 污泥水磷和有机物同步混凝去除的多目标优化技术研究
  • Multi-objective optimization of phosphorus and organic substances removal from reject water by coagulation
  • 基金项目:国家自然科学基金(No.51408352)
  • 作者
  • 单位
  • 窦微笑
  • 上海电力学院环境与化学工程学院, 上海 200090
  • 周振
  • 上海电力学院环境与化学工程学院, 上海 200090
  • 周凯鹏
  • 上海电力学院环境与化学工程学院, 上海 200090
  • 魏海娟
  • 上海城投污水处理有限公司, 上海 201203
  • 蒋路漫
  • 上海电力学院环境与化学工程学院, 上海 200090
  • 李晶
  • 上海城投污水处理有限公司, 上海 201203
  • 裘湛
  • 上海城投污水处理有限公司, 上海 201203
  • 胡大龙
  • 上海电力学院环境与化学工程学院, 上海 200090
  • 摘要:通过引入多响应值的归一化评分法进行了污泥水磷和有机物同步混凝去除的多目标优化,并利用响应面(RSM)技术考察了Al/P比、聚丙烯酰胺(PAM)投加量和悬浮固体(SS)浓度对污染物去除的单独效应和联合效应.结果表明,复合投加聚合氯化铝和PAM能同步去除污泥水中磷和有机物,并改善沉降效果.归一化后单目标RSM优化显示,对污泥水中磷和有机物同步去除的贡献为Al/P比>SS浓度>PAM投加量.在最优条件Al/P比为3、PAM浓度为1.22 mg·L-1、SS浓度为3.58 g·L-1的条件下,正磷和总有机碳去除率分别为93.1%和53.9%.与多响应变量优化相比,引入归一化评分法有效解决了变量间数值量级差异的问题,使结果的分析计算变得简单方便.
  • Abstract:The multi-objective optimization for phosphorus and organic substances removal from reject water by coagulation was conducted using normalized scoring method for multiple response variables. Separate and combined effects of Al/P ratio, polyacrylamide (PAM) dosage and initial suspended solids (SS) concentration on phosphorus and organic substances removal were analyzed by the response surface methodology (RSM). The results showed that adding aluminum polychloride along with PAM effectively removed phosphorus and organic substances from reject water, and improved settlability of SS in reject water. Normalized single-objective RSM optimization showed that Al/P ratio was the dominant factor for simultaneous removal of phosphorus and organic substances, followed by initial SS concentration and PAM dosage. The optimized removal efficiencies of phosphorus and total organic carbon were 93.1% and 53.9%, respectively, under the condition of Al/P ratio of 3.0, PAM dosage of 1.22 mg·L-1 and SS concentration of 3.58 g·L-1. Compared with optimization based on multiple response variables, the introduction of normalized scoring method solved the magnitude differences between response variables, and simplified the analysis and calculation.

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