Wolf, Christian and Gaida, Daniel and Stuhlsatz, Andre and Ludwig, Thomas and McLoone, Sean and Bongards, Michael
Predicting organic acid concentration
from UV/vis spectrometry
measurements – A comparison
of machine learning techniques.
Transactions of the Institute of Measurement and Control, 19.
The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic
digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly
and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis
spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge
at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured
absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA),
support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance
compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that
whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained
with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.
||Anaerobic digestion, classification, feature extraction, GerDA; LDA; neural networks; online measurement; organic acids; random forest; RVM; SVM; UV/vis spectroscopy;
||Science & Engineering > Electronic Engineering
||17 Sep 2012 13:21
|Journal or Publication Title:
||Transactions of the Institute of Measurement and Control
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