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Predicting Organic Acids in Biogas Plants using UV/vis Spectrometry Online-Measurements
Christian Wolf, Daniel Gaida, Michael [email protected]
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Agenda
Practical Background1
Online-measurement using UV/vis spectroscopy2
Discriminant Analysis & Classification3
Results4
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• Organic acid concentrations are crucial for anaerobic digestion
processes to monitor process stability and process efficiency.
• High acid concentrations lead to acidification of the biology, which
results in complete process breakdown eventually.
• Reliable and feasible online-measurement systems are needed for
process monitoring, control and optimization purposes.
• Online-measurement of organic acids is difficult because:
• anaerobic digestion sludge contains a high concentration of solids
• conventional methods require extensive sample preparation
Practical Background
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• Measure organic acids indirectly using UV/vis spectroscopy.
• Measurement is performed using an UV/vis spectroscopic probe from
S::CAN, which measures the absorption from 200 nm – 750 nm at an
interval of 0.5 nm.
• Absorption at specific
wavelengths correlates to
organic acid concentration
of the process.
Online-measurement using UV/vis spectroscopy
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• High amount of total solids in the fermentation sludge requires a
dilution system.
• UV/vis probe is cleaned using compressed air.
Online-measurement using UV/vis spectroscopy
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Description of the classification problem and the data set• Organic acid concentrations grouped into classes better visualization for the plant operator
• Aim: predict organic acid concentrations based on the absorption measured over the whole spectrum (200 - 640 nm).
Online-measurement using UV/vis spectroscopy
Class Organicacid concentration [g/l]
completesamples
Trainingsamples
Testsamples
1 (low) 1.1, …, 1.4 228 171 57
2 (low - normal) 1.5, …, 1.8 1528 1146 382
3 (normal) 1.9, …, 2.2 1880 1410 470
4 (normal - high) 2.3, …, 2.6 731 549 182
5 (high) 2.7, …, 3.0 70 52 18
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finge
rprin
t (17
6 w
avel
engt
hs):
200.0 nm
200.5 nm
201.0 nm
201.5 nm
202.0 nm
202.5 nm
203 - 639 nm
639.5 nm
640.0 nm
class 1: low
2: low - normal
class 3: normal
4: normal - high
class 5: high
Discriminant Analysis & Classification
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Discriminant Analysis & Classification
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Discriminant Analysis & Classification
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Discriminant Analysis & Linear Classification
• Idea: use a linear classifier (simple, fast)
• Rule: without loss of information, we can project our original
featurespace onto a C – 1 dimensional space, with C being
the number of classes.
• Find the projection?
• Example of a projection:
• taking a picture
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• Projection transformation generated using a Deep Neural Network (DNN)
- a highly connected structure with multiple layers and millions of
parameters.
• The projection is nonlinear!
• In contrast to Linear Discriminant Analysis (LDA), where the projection is
linear a matrix.
• GerDA DNN topology for the problem at hand: 176-250-50-25-4
Stuhlsatz, A.; Lippel, J.; Zielke, T.: "Feature Extraction With Deep Neural Networks by a Generalized Discriminant Analysis,"
Neural Networks and Learning Systems, IEEE Transactions on, vol. 23, no. 4, pp. 596-608, April 2012
GerDA – Generalized Discriminant Analysis
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Comparison of LDA and GerDA 3D-feature extraction performance
GerDA – Generalized Discriminant Analysis
no clear feature separation clear feature separation
Linear Discriminant Analysis GerDA
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• C-SVM implementation of libSVM used.
• Soft margin optimization and a RBF Kernel employed.
• Optimization of the Kernel function parameter λ and the smoothing
parameter c performed using a simple grid search.
• Optimum values:
λ= 1.4 and c= 256.0.
• Weighting applied to class 5 to
compensate for uneven data
distribution.
Support Vector Machines - SVM
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Classification results
12,8 % 12,1 % 12 %34 %
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Classification results
• Weighted SVM achieves best overall performance on all the classes
(12 %).
• Not weighted SVM achieves reasonable results on the test data but
suffers due to the biased data set (19,1 %).
• GerDA is perfectly suited for dimension reduction and feature extraction
of multi-dimensional data sets.
• Online-measurement of organic acid concentrations using UV/vis
spectroscopy is possible and accuracy is sufficient.
Wolf, C.; Gaida, D.; Stuhlsatz, A.; Ludwig, T.; McLoone, S.; Bongards, M.: “Predicting organic acid concentration from UV/vis
spectrometry measurements – A comparison of machine learning techniques,” Transactions of the Institute of Measurement
and Control, 2011.
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Measurement of Total Solids
• Endress + Hauser: TurbiMax W CUS 41
• 90° scattered light method (NIR: 880 nm)
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Measurement of Total Solids
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Thank youfor your attention.
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