超光谱成像技术用于描述消化和可视化无氧消化生物废物中的物理杂质
Wei Peng1,2, Lu Fan1, Hua Zhang1,2
1Institute of Waste Treatment and Reclamation, Tongji University, Shanghai 200092, PR China.
Environmental science & technology
|August 30, 2024
概括
近红外超光谱成像 (NIR-HSI) 提供了一种环保,高通量方法,用于监测无氧消化 (AD) 指标并识别浮动颗粒. 这项技术可以快速描述消化物的化学成分和物理干扰.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 生物技术是生物技术.
背景情况:
- 传统的无氧消化 (AD) 监测依赖于耗时的湿化学分析.
- 手动监测物理干扰,如漂浮颗粒 (FG) 是低效的.
- 需要快速,高通量和环保的AD监测方法.
研究的目的:
- 开发和验证用于AD监测的近红外高光谱成像 (NIR-HSI) 方法.
- 创建一个机器学习模型来描述消化物化学成分 (TN,TOC,TAN,COD).
- 开发一个目标检测算法来识别AD反应堆中的FG.
主要方法:
- 用732个消化样本进行模型开发和验证.
- 采用NIR-HSI技术进行光谱数据采集.
- 开发了用于数据分析的机器学习和目标检测算法.
主要成果:
- 使用干燥消化剂,实现了TOC (R2test=0.82) 和TN (R2test=0.86) 的良好模型性能.
- 成功识别了FG的独特光谱特征,使其能够被HSI系统检测.
- 证明了监测关键化学指标 (TN,TOC,TAN,COD) 和物理干扰的能力.
结论:
- 与机器学习相结合的NIR-HSI是一种可行的,环保和高通量AD监测方法.
- 这项技术在AD工厂的工业应用中具有显著的潜力.
- 开发的方法可以提高AD流程管理的效率和有效性.
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