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Journal article

An innovative online VFA monitoring system for the anerobic process, based on headspace gas chromatography

From

Residual Resource Engineering, Department of Environmental Engineering, Technical University of Denmark1

Department of Environmental Engineering, Technical University of Denmark2

Urban Water Engineering, Department of Environmental Engineering, Technical University of Denmark3

A new method for online measurement of volatile fatty acids (VFA) in anerobic digesters has been developed based on headspace gas chromatography (HSGC). The method applies ex situ VFA stripping with variable headspace volume and gas analysis by gas chromatography-flame ionization detection (GC-FID).

In each extraction, digester sample was acidified with H(3)PO(4) and NaHSO(4), then heated to strip the VFA into the gas phase. The gas was sampled in a low friction glass syringe before injected into the GC for measurement. The system has been tested for online monitoring of a lab-scale CSTR reactor treating manure for more than 6 months and has shown good agreement with off-line analysis.

The system is capable of measuring individual VFA components. This is of advantage since specific VFA components such as propionic and butyric acid can give extra information about the process status. Another important advantage of this sensor is that there is no filtration, which makes possible application in high solids environments.

The system can thus be easily applied in a full-scale biogas reactor by connecting the system to the liquid circulation loop to obtain fresh sample from the reactor. Local calibration is needed but automatic calibration is also possible using standard addition method. Sampling duration is 25-40 min, depending on the washing duration, and sensor response is 10 min.

This is appropriate for full-scale reactors, since dynamics within most biogas reactors are of the order of several hours.

Language: English
Publisher: Wiley Subscription Services, Inc., A Wiley Company
Year: 2007
Pages: 712-721
ISSN: 10970290 and 00063592
Types: Journal article
DOI: 10.1002/bit.21131
ORCIDs: Boe, Kanokwan and Angelidaki, Irini

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