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Modeling, Experimentation, and Control of Autotrophic Nitrogen Removal in Granular Sludge Systems

机译:颗粒污泥系统中自养脱氮的模拟,实验与控制

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摘要

Complete autotrophic nitrogen removal (CANR) is a novel process that can increase the treatment capacity for wastewaters containing high concentrations of nitrogen and low organic carbon to nitrogen ratios, through an increase of the volumetric removal rate by approximately five times. This process is convenient for treating anaerobic digester liquor, landfill leachate, or special industrial wastewaters, because costs related to the need for aeration and carbon addition are lowered by 60% and 100%, respectively, compared to conventional nitrification denitrification treatment. Energy and capital costs can further be reduced by intensifying the process and performing it in a single reactor, where all processes take place simultaneously, e.g. in a granular sludge reactor, which was studied in this project. This process intensification means on the other hand an increased complexity from an operation and control perspective, due to the smaller number of actuators available.In this work, an integrated modeling and experimental approach was used to improve the understanding of the process, and subsequently use this understanding to design novel control strategies, providing alternatives to the current ones available. First, simulation studies showed that the best removal efficiency was almost linearly dependent on the volumetric oxygen to nitrogen loading ratio. This finding among others, along with experimental results from start-up of lab-scale reactors, served as the basis for development of three single-loop control strategies, having oxygen supply as the actuator and removal efficiency as the controlled variable. These were investigated through simulations of an experimentally calibrated and validated model. A feedforward-feedback control strategy was found to be the most versatile towards the disturbances at the expense of slightly slower dynamic responses and additional complexity of the control structure. The functionality of this strategy was tested experimentally in a lab-scale reactor, where it showed the ability to reject disturbances in the incoming ammonium concentrations. However, during high ammonium loadings, when the capacity of the present sludge was reached, an oscillatory response was observed. Proper tuning of the controller is therefore of essential importance.In this thesis, it was demonstrated that proactive use of model simulations, in an integrated methodology with experimentation, resulted in improved process understanding and novel control ideas. This will contribute to moving this promising technology from a case-by-case ad hoc approach to a more systematic knowledge based approach.
机译:完全自养氮去除(CANR)是一种新颖的方法,可通过将体积去除率提高大约五倍来提高对高浓度氮和低有机碳与氮比率的废水的处理能力。与传统的硝化反硝化处理相比,此工艺可方便地处理厌氧消化液,垃圾渗滤液或特殊工业废水,因为与曝气和添加碳有关的成本分别降低了60%和100%。通过加强该工艺并在单个反应器中进行该工艺可以进一步降低能量和资本成本,在该反应器中所有工艺同时进行,例​​如通过加热进行。在颗粒污泥反应堆中进行了研究。另一方面,由于可用的执行器数量较少,因此这种过程强化意味着从操作和控制角度来看增加了复杂性。在这项工作中,使用集成的建模和实验方法来提高对过程的理解,并随后使用这种了解可以设计出新颖的控制策略,为当前可用策略提供替代方案。首先,模拟研究表明,最佳去除效率几乎线性依赖于氧气与氮气的体积负载比。除其他外,这一发现以及实验室规模反应器启动的实验结果,为开发三种单回路控制策略提供了基础,这些策略以供氧为驱动器,去除效率为控制变量。通过对经过实验校准和验证的模型进行仿真来研究这些问题。发现前馈-反馈控制策略是针对干扰的最通用的方法,其代价是动态响应稍慢并且控制结构的额外复杂性。此策略的功能已在实验室规模的反应器中进行了实验测试,在该反应器中显示了抑制传入铵浓度干扰的能力。但是,在高铵含量期间,当达到当前污泥的容量时,观察到振荡响应。因此,正确调整控制器至关重要。在本文中,证明了在与实验相集成的方法中主动使用模型仿真可以提高对过程的理解和新颖的控制思想。这将有助于将这一有前途的技术从个案特设方法转变为更系统的基于知识的方法。

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