...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Clean >Optimization of Biological Elimination of Ammoniaand Chemical Oxygen Demand from WastewaterUsing Response Surface Methodology
【24h】

Optimization of Biological Elimination of Ammoniaand Chemical Oxygen Demand from WastewaterUsing Response Surface Methodology

机译：响应面分析法优化废水的氨氮和化学需氧量

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Using a statistical technique, i.e. response surface methodology (RSM), optimization studies were carried out for different parameters like nitrogen ammonia (N-NH_3), chemical oxygen demand (COD) and time, to optimize overall method for maximum elimination of N-NH3and COD from synthetic waste water using heterotrophic biomass conversion (HBC) process, and simultaneously minimizing the N-N_2O emission. Ammonium hydroxide and glucose were used as nitrogen and carbon source. Central composite design technique was used to optimize the parameters with RSM. In this experiment, N-NH_3 and organic nutrient concentrations were varied keeping the biomass (total volatile suspended solids) concentration invariable. The pH during HBC process showed a decreasing trend may be due to consumption of alkalinity. Analyses of variance were carried out for quadratic model and were observed to be highly significant. The model was verified through experiments and tested statistically. Under optimized conditions (N-NH_3: 210 mg/L, COD: 2.90 g/L, time: 38 h) N-NH_3 and COD removal efficiency were observed to be 82.72 and 84.78%, respectively. N-N_2O emissions were minimized to 2.96μg. The total desirability factor obtained was 0.9.
机译：使用统计技术，即响应面方法（RSM），针对氮氨（N-NH_3），化学需氧量（COD）和时间等不同参数进行了优化研究，以优化总体方法以最大程度地消除N-NH3和使用异养生物质转化（HBC）工艺从合成废水中获得的COD，同时将N-N_2O排放降至最低。氢氧化铵和葡萄糖用作氮和碳源。使用中央复合设计技术通过RSM优化参数。在该实验中，改变了N-NH_3和有机营养物的浓度，从而使生物量（总挥发性悬浮固体）的浓度保持不变。 HBC过程中的pH值呈下降趋势，可能是由于碱度的消耗。对二次模型进行了方差分析，发现方差分析具有很高的意义。通过实验验证了模型并进行了统计检验。在优化的条件下（N-NH_3：210 mg / L，COD：2.90 g / L，时间：38 h），N-NH_3和COD去除效率分别为82.72和84.78％。 N-N_2O排放最小化为2.96μg。获得的总期望因子为0.9。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号