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    Predicting organic acid concentration from UV/vis spectrometry measurements – A comparison of machine learning techniques


    Wolf, Christian and Gaida, Daniel and Stuhlsatz, Andre and Ludwig, Thomas and McLoone, Sean F. and Bongards, Michael (2011) Predicting organic acid concentration from UV/vis spectrometry measurements – A comparison of machine learning techniques. Transactions of the Institute of Measurement and Control, 19. ISSN 0142-3312

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    Abstract

    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.

    Item Type: Article
    Keywords: Anaerobic digestion, classification, feature extraction, GerDA; LDA; neural networks; online measurement; organic acids; random forest; RVM; SVM; UV/vis spectroscopy;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 3868
    Depositing User: Sean McLoone
    Date Deposited: 17 Sep 2012 13:21
    Journal or Publication Title: Transactions of the Institute of Measurement and Control
    Publisher: SAGE Publications
    Refereed: Yes
    URI:

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