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在多个通风模式和COD/N比率下,动态预测工业活化污泥反应堆中的氧化排放量

Tianyu Lei¹, Jaime Whale-Obrero², Sille B Larsen²

¹Process and Systems Engineering Centre (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 228 A, 2800 Kgs. Lyngby, Denmark.

|March 8, 2025

概括

一个新的决策支持工具 (DST) 准确预测工业废水处理厂的氧化 (N2O) 排放. 该工具优化了运营,大大减少了温室气体排放,提高了整体工厂性能.

科学领域:

环境工程环境工程
废水处理技术废水处理技术
温室气体排放监测温室气体排放监测

背景情况:

数字工具对于量化废水处理厂 (WWTP) 温室气体 (GHG) 排放至关重要,以实现净零目标.
在工业环境中对模型预测的验证研究很少,特别是在废水质量,经济和排放因素方面.
工业废水对准确的排放预测和减缓提出了独特的挑战.

研究的目的:

开发一个决策支持工具 (DST) 用于动态预测工业活性污泥反应堆 (ASR) 中的氧化 (N2O) 排放.
将生物和物理化学过程纳入其中,包括覆盖反应堆的专用气液质量转移程序.
通过全面的工业数据验证DST,并测试其在各种运营策略中的有效性.

主要方法:

开发一个DST,将生物和物理化学过程与独特的气液质量转移程序集成在一起.
使用北欧最大的工业WWTP的高频 (分钟级) 和每日数据进行验证.
在不同的通风模式,影响COD/N比率和操作策略中测试DST.

主要成果:

DST准确地复制了每天的COD/去除,硫转化和沉 (在六周内偏差8.6%).
物种和溶解氧的高频动态与NRMSEs在0.11和0.16之间被捕获.
排放因子 (EFs) 与有影响的COD/N比率有很强的相关性 (R2高达0.9),受氧供应和通风持续时间的影响 (EFs 0.2%1.4%).
脱化 (DEN) 被确定为主要的N2O生产途径,而化剂-脱化 (ND) 的贡献较小.
从DST中获得的优化策略实现了高达71%的N2O排放减少 (1.4%至0.4%),每年可能减轻超过15,000的CO2-e.

结论:

开发的DST有效预测工业WWTP中的N2O排放量和关键性能指标.
DST提供了对影响N2O生产的操作参数的宝贵见解,并允许针对性的减缓策略.
该工具显示了优化WWTP运营的巨大潜力,以实现显著的温室气体减排和环境效益.

关键词:

气候行动是一个气候行动.温室气体减排温室气体减排数学模型是一个数学模型.净零是零的净零.情景分析就是一种情景分析.可持续发展可持续性可持续性

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