Hyperspectral Imaging Technique to Characterize Digestate and Visualize Physical Impurities in Anaerobically Digested...

Area of Science:

• Environmental Science
• Analytical Chemistry
• Biotechnology

Background:

• Conventional anaerobic digestion (AD) monitoring relies on time-consuming wet chemical analyses.
• Manual monitoring of physical disturbances like floating granules (FGs) is inefficient.
• There is a need for rapid, high-throughput, and eco-friendly AD monitoring methods.

Purpose of the Study:

• To develop and validate a near-infrared hyperspectral imaging (NIR-HSI) methodology for AD monitoring.
• To create a machine-learning model for characterizing digestate chemical composition (TN, TOC, TAN, COD).
• To develop a target detection algorithm for identifying FGs in AD reactors.

Main Methods:

• Utilized 732 digestate samples for model development and validation.
• Employed NIR-HSI technology for spectral data acquisition.
• Developed machine learning and target detection algorithms for data analysis.

Main Results:

• Achieved good model performance for TOC (R²test=0.82) and TN (R²test=0.86) using dried digestates.
• Successfully identified unique spectral features of FGs, enabling their detection by the HSI system.
• Demonstrated the capability to monitor key chemical indicators (TN, TOC, TAN, COD) and physical disturbances.

Conclusions:

• NIR-HSI combined with machine learning is a viable, eco-friendly, and high-throughput method for AD monitoring.
• This technology has significant potential for industrial application in AD plants.
• The developed methodology can improve the efficiency and effectiveness of AD process management.