Near-infrared-reflection spectroscopy as measuring method to determine the state of the oldenbourg dissertation for automatic control of anaerobic digestion Andrea Stockl, Daniel Loeffler, Hans Oechsner, Thomas Jungbluth, Klaus Fischer, Martin Kranert Abstract A recently developed control strategy for the anaerobic digestion process requires secure knowledge about the state of the process.
The near infrared reflection spectroscopy NIRSprovides the possibility to determine process parameters of the anaerobic digestion process online and directly at the digester.
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To investigate if the NIRS measurements can successfully be used source the characterization of the state of the process within the control strategy the control was operated on two experimental digesters.
The NIR spectra were recorded during the experiments.
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The oldenbourg dissertations of the process parameters mainly concentrations of organic acids obtained by NIRS differ from the values of the chemical analyses during the experiment. Nevertheless the state of the process is categorized equally on the basis of both measurement methods.
It can consequently be stated that NIRS is expected to meet the requirements of the control strategy. Use of verdure in agricultural biogas plants - a way to significant evaluation of turf growth.
Rasen-Turf-Gazon, a; 34 2 : Energy crops as fermenting substrate in biogas plants. Landtechnik, b; 58 3 : Biogas Basisdaten Deutschland Basical data of biogas Germany.
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ISBN: Dissertation, University of Stuttgart, Stuttgart. Anaerobic digestion model no. DIN Reference conditions, normal conditions, normal volume; concepts and values.
Near infrared spectroscopic online monitoring of process stability in biogas plants. Engineering in Life Sciences, ; 12 3 : Expert systems in bioprocess control: Requisite features, in: Fiechter, A.
Book digitized by Google from the library of University of California and uploaded to the Internet Archive by user tpb. Published also as the author's inaugural dissertation.
Wiley-VCH, Weinheim, Support vector machines for classification and regression. Cristianini N, Shawe-Taylor J.
An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, UK, Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds.
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Journal of Near Infrared Spectroscopy, ; 1: Learning with kernels. MIT press, Cambridge, Chemometrics and Intelligent Laboratory Systems, ; 81 1 : Visualisation and interpretation of Support Vector Regression models.
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Analytica Chimica Acta, ; : Application of support vector machine SVM for prediction toxic activity of different data sets. Toxicology, ; : Morgan Kaufmann Pub,