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Predicting Organic Acids in Biogas Plants using UV/vis Spectrometry Online-Measurements

Christian Wolf, Daniel Gaida, Michael Bongardschristian.wolf@fh-koeln.de

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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.