文章摘要
李爱秀,翟中葳,丁飞飞,杜连柱,张克强.鸟粪石沉淀法回收猪场沼液氮磷工艺参数优化模拟研究[J].农业环境科学学报,2018,37(6):1270-1276.
鸟粪石沉淀法回收猪场沼液氮磷工艺参数优化模拟研究
Simulation of optimization of process parameters of nitrogen and phosphorus recovery in biogas slurry derived from swine manure by struvite precipitation method
投稿时间:2018-01-28  
DOI:10.11654/jaes.2018-0157
中文关键词: 氮磷回收率  猪场沼液  响应面设计  最优参数
英文关键词: nitrogen and phosphorus recovery  swine manure  response surface methodology  optimal parameters
基金项目:中国农业科学院科技创新工程;天津市农业科技成果转化项目(201601290);现代农业(奶牛)产业技术体系建设专项资金(CARS-36)
作者单位E-mail
李爱秀 农业部环境保护科研监测所, 天津 300191  
翟中葳 农业部环境保护科研监测所, 天津 300191  
丁飞飞 吉林农业大学资源与环境学院, 长春 130117  
杜连柱 农业部环境保护科研监测所, 天津 300191  
张克强 农业部环境保护科研监测所, 天津 300191 kqzhang68@126.com 
摘要点击次数: 2000
全文下载次数: 1772
中文摘要:
      针对规模化养殖场沼液难以农田消纳的问题,探索化学方法回收沼液中氮磷的工艺条件和优化参数。本研究选择猪场沼液为研究对象,采用鸟粪石沉淀法,进行单因素影响试验,选取pH、镁氮比、磷氮比为自变量,以氮磷回收率为响应值,通过Box-Behnken响应面试验设计对工艺参数进行优化。结果显示:猪场沼液氮磷回收的最佳工艺为:pH 10,镁氮比为1.1,磷氮比为0.6,氨氮回收率为65.21%,磷酸盐回收率为89.47%,实际值氨氮回收率为65.01%,磷酸盐回收率为90.81%,差值分别为0.20%、1.34%,回归模型拟合较好。
英文摘要:
      The present study focuses on exploring the process conditions of nitrogen and phosphorus recovery from biogas slurry and optimizing the process parameters using a chemical method to alleviate the difficulties faced by large-scale farms in the digestion of biogas slurry. The biogas slurry of swine manure was assessed. By using the struvite precipitation method, the influence of factors such as pH, magnesium:nitrogen ratio, and phosphorus:nitrogen ratio on the nitrogen and phosphorus recovery rate was investigated and optimized using the Box-Behnken response surface methodology design. The optimum conditions for the recovery of nitrogen and phosphorus in biogas slurry from swine manure were a pH of 10, a magnesium:nitrogen ratio of 1.1, and a phosphorus:nitrogen ratio of 0.6. Under the above optimized conditions, the recovery rates for ammonia and phosphate were 65.21% and 89.47%, respectively. In the actual experiments, the recovery rates for ammonia and phosphate were 65.01% and 90.81%, respectively, with difference values of 0.20% and 1.34%, respectively, indicating good fit with the regression model.
HTML    查看全文   查看/发表评论  下载PDF阅读器