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转移学习用于生物固体的定量图像分析

Sebastian Olivier Nymann Topalian¹, Nima Nazemzadeh², Alonso Malacara-Becerra^2,3

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

Water science and technology : a journal of the International Association on Water Pollution Research

|October 15, 2025

概括

定量图像分析提高了生物固体脱水效率. 随机森林模型整合了过程,实验室和转移学习数据,提高了有机固体回收预测14%.

科学领域:

环境工程环境工程
废水处理技术废水处理技术
生物固体管理管理

背景情况:

稳定生物固体的脱水对于工业废水处理效率至关重要.
由于复杂的因素,预测在器中有机固体的回收是具有挑战性的.
定量图像分析提供了一种新的方法,可以提高对脱水过程的理解.

研究的目的:

开发一个系统的工作流程,用于定量图像分析,以预测中的有机固体回收.
整合运行条件,实验室测量和图像分析的数据,以改善预测.
评估不同建模技术 (PLS,RF) 和数据源的有效性.

主要方法:

从两个工业废水处理厂活动中收集数据.
应用定量图像分析和转移学习技术.
部分最小平方 (PLS) 和随机森林 (RF) 模型的开发和比较.

主要成果:

结合过程,实验室和转移学习数据的随机森林模型实现了最佳的恢复预测,比基线提高了14%.
粒子图像和射频预测的聚类突出了对特定晶体粒子的依赖.
随机森林模型在恢复预测中通常优于PLS模型.

结论:

定量图像分析,特别是转移学习,显著提高了生物固体脱水效率的预测.
该研究提供了一种可转移的诊断工作流程,用于优化具有异质颗粒混合物的系统中的脱水.
需要进一步精细化,以解决不同有机固体度的预测一致性.

关键词:

离心式装器的离心式装器定量图像分析定量图像分析污泥脱水处理污泥脱水处理转移学习转移学习

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