预测部分化时的空气化时间和化物积聚率变化:一个包含氧化率动态的模型
Wentao Zhou1, Qiong Zhang1, Bo Wang1
1National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Engineering Research Center of Beijing, Beijing University of Technology, Beijing 100124, China.
Water research
|October 17, 2024
概括
本研究提出了一种两步化模型,用于预测部分化 (PN) 过程中的空气化时间和化物积累率. 该模型准确预测操作参数,有助于精确管理氧化氨和氧化的细菌.
科学领域:
- 环境工程 环境工程
- 废水处理 废水处理
- 生物反应器建模模型
背景情况:
- 部分化 (PN) 过程对于废水处理中的去除至关重要.
- 波动的运行条件在预测通风时间和酸盐积累率 (NAR) 中带来了挑战.
- 对氨氧化率 (AOR) 和酸盐氧化率 (NOR) 的准确建模对于工艺控制至关重要.
研究的目的:
- 开发一种两步化模型,用于在主流PN工艺中在可变条件下预测通风时间和NAR.
- 评估静态和动态模型在模拟AOR和NOR中的性能.
- 为优化通风控制和微生物种群管理提供见解.
主要方法:
- 使用实验室规模的测序批量反应器 (SBR) 来评估在不同固体保留时间 (SRT) 和总体体积负荷 (TVNL) 下的AOR和NOR.
- 开发了用于稳态预测的静态模型和用于氨氧化细菌 (AOB) 和氧化细菌 (NOB) 不稳态生长动态的动态模型.
- 使用指数式拟合量化了特定生长率 (μ) 和食物与微生物比率 (F/M) 之间的关系.
主要成果:
- 静态模型准确地预测了稳定状态的AOR,NOR和通风时间.
- 动态模型在不稳定状态条件下成功捕捉了AOB和NOB的增长动态.
- 验证实验证实了动态模型在预测NAR和通风时间趋势方面的准确性.
结论:
- 对AOR和NOR变化的准确建模对于预测PN过程中的通风时间和NAR至关重要.
- 开发的动态模型为空气控制和精确管理AOB和NOB种群提供了宝贵的见解.
- 这项研究有助于提高主流部分化系统的运行效率和稳定性.
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