首页> 外文OA文献 >La production de nitrites lors de la d\'enitrification des eaux us\'ees par biofiltration - Strat\'egie de contr\^ole et de r\'eduction des concentrations r\'esiduelles
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La production de nitrites lors de la d\'enitrification des eaux us\'ees par biofiltration - Strat\'egie de contr\^ole et de r\'eduction des concentrations r\'esiduelles

机译:La nit de nitrites lors de la d'enitrification des eaux us's ees   par生物过滤 - strat \'egie de contr \ ^ ole et de r \'eduction des   浓度r \'esiduelles

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摘要

The recent popularity of post-denitrification processes in the greater Parisarea wastewater treatment plants has caused a resurgence of the presence ofnitrite in the Seine river. Controlling the production of nitrite during thepost-denitrification has thus become a major technical issue. Research studieshave been led in the MOCOPEE program (www.mocopee.com) to better understand theunderlying mechanisms behind the production of nitrite during wastewaterdenitrification and to develop technical tools (measurement and controlsolutions) to assist on-site reductions of nitrite productions. Prior studieshave shown that typical methanol dosage strategies produce a varyingcarbon-to-nitrogen ratio in the reactor, which in turn leads to unstablenitrite concentrations in the effluent. The possibility of adding a model-freecontrol to the actual classical dosage strategy has thus been tested on theSimBio model, which simulates the behavior of wastewater biofilters. Thecorresponding "intelligent" feedback loop, which is using effluent nitriteconcentrations, compensates the classical strategy only when needed. Simulationresults show a clear improvement in average nitrite concentration level andlevel stability in the effluent, without a notable overcost in methanol.
机译:最近在较大的Parisarea废水处理厂中进行的反硝化过程的普及,导致塞纳河中亚硝酸盐的存在重新出现。因此,在后反硝化过程中控制亚硝酸盐的产生已成为主要的技术问题。 MOCOPEE计划(www.mocopee.com)进行了研究,以更好地了解废水脱硝过程中亚硝酸盐生产背后的基本机制,并开发技术工具(测量和控制解决方案)以帮助现场减少亚硝酸盐产量。先前的研究表明,典型的甲醇剂量策略会在反应器中产生变化的碳氮比,进而导致废水中亚硝酸盐的浓度不稳定。因此,已在SimBio模型上测试了将无模型控件添加到实际经典剂量策略的可能性,该模型可模拟废水生物滤池的行为。使用废水中亚硝酸盐浓度的相应“智能”反馈回路仅在需要时才补偿经典策略。仿真结果表明,废水中平均亚硝酸盐浓度水平和水平稳定性明显改善,而甲醇没有明显的成本过高。

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