Near-infrared reflectance spectroscopy for rapid prediction of biochemical methane potential of wastewater wasted sludge

• Published in: The Science of the total environment Vol. 912; p. 169640
• Main Authors: Lu, Dan, Yan, Wangwang, Le, Chencheng, Low, Siok Ling, Tao, Guihe, Zhou, Yan
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 20.02.2024
• Subjects: Anaerobic digestion; Biochemical methane potential (BMP); Near-infrared reflectance spectroscopy (NIRS); Prediction model; Waste sludge; Biochemical methane potential (BMP); Near-infrared reflectance spectroscopy (NIRS); Anaerobic digestion; Prediction model; Waste sludge
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2023.169640

The information of biochemical methane potential (BMP) of wasted sludge is essential to ensure the stable operation of sludge management processes. However, conventional anaerobic digestion (AD) approach for BMP test is time-consuming and labour-intensive. Currently, the technique of Near Infrared Spectroscopy (NIRS) is gaining prominence in the biogas production within AD process. Previous studies mostly focused on predicting BMP values for fibrous plant biomass and solid waste, with only a limited number of studies attempting to apply NIRS to obtain BMP values across a wide array of wasted sludge types. To obtain BMP values for this diverse range of wasted sludge efficiently and accurately, it is imperative to develop precise models for assessing BMP values using NIRS. In this study, the possibility of using NIRS to predict the BMP values of wasted sludge was evaluated. A total of 70 sludge samples from different sources were investigated to develop a BMP-prediction model by correlating the measured BMP values with the obtained NIR spectra. As a result, a reliable and successful BMP-prediction model was established with the determination coefficient of 0.90, residual prediction deviation of 3.50 and low root mean square error of prediction of 36.8 mL CH4/g VS. This BMP-prediction model is satisfactory for predicting BMP values of various types of sludge. It could provide support for plant operators to make decisions rapidly, thereby improving the process efficiency and optimizing sludge management procedures. [Display omitted] •A rapid and reliable BMP-prediction model using NIRS was developed.•A wide range of types of wasted sludge was included in the prediction model.•Rt2 and RMSEP values of sludge BMP predicting model were 0.90 and 36.8 mL CH4/g VS.•The NIRS-assisted BMP prediction achieved satisfactory and successful results.