沼渣
高光谱成像
环境科学
遥感
环境化学
厌氧消化
化学
地质学
甲烷
有机化学
作者
Wei Peng,Fan Lü,Hua Zhang,Hao-Yang Xian,Fan Lü,Pinjing He
标识
DOI:10.1021/acs.est.4c06822
摘要
Methods used to monitor anaerobic digestion (AD) indicators are commonly based on wet chemical analyses, which consume time and materials. In addition, physical disturbances, such as floating granules (FGs), must be monitored manually. In this study, we present an eco-friendly, high-throughput methodology that uses near-infrared hyperspectral imaging (NIR-HSI) to build a machine-learning model for characterizing the chemical composition of the digestate and a target detection algorithm for identifying FGs. A total of 732 digestate samples were used to develop and validate a model for calculating total nitrogen (TN), total organic carbon (TOC), total ammonia nitrogen (TAN), and chemical oxygen demand (COD), which are the chemical indicators of responses to disturbances in the AD process. Among these parameters, good model performance was obtained using the dried digestates data set, where the coefficient of determination (
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