文章摘要
聚硅酸盐混凝剂处理压裂返排液的效果及影响因素
Effect and influence factors of poly-silicic-cation coagulant in fracturing flowback fluids treatment
投稿时间: 2017-06-24  最后修改时间: 2017-08-30
DOI:
中文关键词: 聚硅酸盐  压裂返排液  混凝  响应面  交互作用
英文关键词: poly-silicic-cation  fracturing flowback fluids  coagulation  response surface  interaction
基金项目:国家自然科学基金“半固化小球藻混养处理压裂返排液的污染物去除及代谢调控机理研究”(项目编号51504192),陕西省自然科学基础研究计划“微藻处理压裂返排液的污染物去除机理及产能耦合机制研究”(项目编号2016JQ5102),陕西省高校科协青年人才托举计划项目“页岩气压裂返排液处理与回用关键技术研究”(项目编号20160119),陕西省教育厅专项科研计划项目“页岩气压裂返排液处理与回用技术研究”(项目编号17JK1605)。
作者单位E-mail
李冉 西安石油大学石油工程学院 陕西西安 710065 rli@xsyu.edu.cn 
潘杰 西安石油大学石油工程学院 陕西西安 710065  
张丽 西安石油大学石油工程学院 陕西西安 710065  
屈肖 西安石油大学石油工程学院 陕西西安 710065  
杨江 西安石油大学石油工程学院 陕西西安 710065  
秦文龙 西安石油大学石油工程学院 陕西西安 710065  
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中文摘要:
      采用自制聚硅酸盐混凝剂(PSiC)对压裂返排液进行混凝预处理,利用Box-Behnken设计建立连续变量曲面模型进行SiO2浓度、聚合pH、Si/(Al+Fe)摩尔比三因素三水平实验设计,考察三种因素对混凝效果的影响,并确定出合适的制备参数和工艺。实验结果表明,Si/(Al+Fe)摩尔比对PSiC混凝效果和稳定性影响较大,当SiO2浓度、Si/(Fe+Al)比以及pH值分别取3.39%、1.3、1.8时,PSiC具有最佳性能,处理压裂返排液时的浊度和COD去除率分别为88.52%和70.35%。利用统计软件Design Expert对实验数据进行了多元回归拟合,得到SiO2浓度、聚合pH、Si/(Al+Fe)摩尔比与浊度去除率之间的二次多项回归模型方程,通过回归分析,证明模型拟合性良好。通过三维立体图像揭示三种因素交互作用规律,结果表明SiO2浓度和Si/(Fe+Al)比交互作用不显著,SiO2浓度和pH有一定的交互作用,Si/(Fe+Al)比和pH交互作用较显著。
英文摘要:
      The poly-silicic-cation coagulant (PSiC) was prepared to treat fracturing flowback fluids in this paper. Through Box-Behnken experiment design and response surface analysis, optimization grouping of SiO2 concentration, polymerization pH, Si/(Al+Fe) molar ratio these 3 main influence factors was realized, a quadratic response surface model and optimum level values were obtained. The results show that coagulation performances and stability were significantly influenced by the Si/(Fe+Al) molar ratio. It was found that the optimization grouping of the main influence factors for coagulation were as following: SiO2 concentration being 3.39%,pH being 1.3 and Si/(Al+Fe) molar ratio being 1.8, and under the optimum conditions, the removal rate of turbidity and COD were 88.52% and 70.35% respectively. The quadratic polynomial regression equation model of SiO2 concentration, polymerization pH, Si/(Al+Fe) molar ratio and turbidity removal rate was predicted by multivariable regression fitting method. A regression analysis was utilized to evaluate the fitness of the model. Three- dimensional images were used to reveal interaction between three factors, the results indicate that interaction between SiO2 concentration and Si/(Al+Fe) molar ratio are not significant, there is some kind of interaction between SiO2 concentration and pH, interaction between Si/(Al+Fe) molar ratio and pH are significant.
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