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Monitoring of biogas test plants—a process analytical technology approach
Author(s) -
HolmNielsen Jens Bo,
Esbensen Kim H.
Publication year - 2011
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1344
Subject(s) - biogas , partial least squares regression , anaerobic digestion , bioenergy , sampling (signal processing) , process analytical technology , environmental science , calibration , bioreactor , chemometrics , population , process engineering , mathematics , microbiology and biotechnology , statistics , bioprocess , computer science , chemistry , biofuel , engineering , waste management , biology , machine learning , demography , organic chemistry , filter (signal processing) , methane , chemical engineering , sociology , computer vision
Most studies reported in the literature have investigated near infrared spectroscopy (NIR) in laboratory‐scale or minor pilot biogas plants only; practically no other studies have examined the potential for meso‐scale/full‐scale on‐line process monitoring. The focus of this study is on a meso‐scale biogas test plant implementation of process analytical technologies (PAT) to develop multivariate calibration/prediction models for anaerobic digestion (AD) processes. A 150 L bioreactor was fitted with a recurrent loop at which NIR spectroscopy and attendant reference sampling were carried out. In all realistic bioreactor scales, it is necessary to obtain a fairly constant level of volatile fatty acid (VFA) concentration, which furthers a stable biogas production. Uncontrolled VFA contents have a significant negative impact on biogas production; VFA concentrations should not exceed 5–6000 mg/L lest the microorganism population may suffer fatal reductions. On‐line control and management of VFA concentration levels are therefore critical in order to be able to speed up or slow down the AD processes which produce the desired sustainable bioenergy for combined heat and power production. By calibrating pilot plant NIR spectra to laboratory VFA reference concentrations at the experimental locality Bygholm , it was possible to develop calibration models by partial least squares (PLS) regression, which displayed acceptable to very good prediction performances for total VFA as well as for three other essential individual acids based on test set validations. The average statistics assessing prediction performance, accuracy (slope) and precision (explained variance r 2 ), were both 0.92, which must be considered excellent for this type of significantly heterogeneous systems. The meso‐ to full‐scale feasibility has thus been proven, which has important positive implications for robust, sufficient and reliable, low‐cost PAT monitoring systems in the biogas arena and other continuous fermentation processes. Copyright © 2011 John Wiley & Sons, Ltd.