Applications of near infrared spectroscopy and hyperspectral imaging techniques in anaerobic digestion of bio-wastes: A review. (September 2022)

• Record Type: Journal Article
• Authors: Peng, Wei; Beggio, Giovanni; Pivato, Alberto; Zhang, Hua; Lü, Fan; He, Pinjing
• Is Part Of: Renewable & sustainable energy reviews. Volume 165(2022)
• Publication Date: 2022-09
• Subjects: Near-infrared spectroscopy -- Chemometrics -- Hyperspectral imaging -- Anaerobic digestion -- Bio-waste; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2022.112608
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) techniques combined with chemometric method are emerging techniques and have been studied and applied to address current challenges in anaerobic digestion (AD) plants, such as the heterogeneity of feedstocks, low methane yield, process instability and digestate management. Prior to the AD process, the rapid and accurate measurement of the feedstocks' chemical composition can predict the biochemical methane potential and discern the potential microorganism inhibitors. During the AD process, monitoring the intermediate products in the AD digesters by using NIRS or HSI techniques allows for process optimization and avoids potential AD failure. Regarding digestate management, the NIRS or HSI can be applied to determine the biological stability and evaluate the digestate quality. In this review, we summarize recent research advances in monitoring AD process parameters and quality of feeding substrate and digestate using NIRS and HSI combined with machine learning techniques. This review highlights the application of NIRS and HSI technology in the AD of organic wastes with particular emphasis on the application drawbacks and possible enhancement solutions. In general, the existing machine learning augmented NIRS can obtain satisfactory quantification results. Future researches on characterization of high-moisture heterogeneous substrate and real-time monitoring AD by HSI combined deep learning are still in demand. Graphical abstract: Image 1 Highlights: NIR and HSI can be used for substrate classification, process monitoring and digestate identification in an AD plant. NIR-HSI is a promising technique to analyze non-homogeneous samples from co-digestion AD systems. The pretreatment of high moisture samples from AD is essential prior to NIR or NIR-HIS analysis.