Fault detection with moving window PCA using NIRS spectra for monitoring the anaerobic digestion process.

Principal component analysis (PCA) is a popular method for process monitoring. However, most processes are time-varying, thus older samples are not representative of the current process status. This led to the introduction of adaptive-PCA based monitoring, such as moving window PCA (MWPCA). In this study, near-infrared spectroscopy (NIRS) responses to digester failures were evaluated to develop a spectral data processing tool. Tests were performed with a spectroscopic probe (350-2,500 nm), using a 35 L mesophilic continuously stirred tank reactor. Co-digestion experiments were performed with pig slurry mixed with several co-substrates. Different stresses were induced by abruptly increasing the organic load rate, changing the feedstock or stopping the stirring. Physicochemical parameters as well as NIRS spectra were acquired for lipid, organic and protein overloads experiments. MWPCA was then applied to the collected spectra for a multivariate statistical process control. MWPCA outputs, Hotelling T2 and residuals Q statistics showed that most of the induced dysfunctions can be detected with variations in these statistics according to a defined criterion based on spectroscopic principles and the process. MWPCA appears to be a multivariate statistical method that could help in decision support in industrial biogas plants.

[1]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[2]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .

[3]  W. Gujer,et al.  Conversion processes in anaerobic digestion , 1983 .

[4]  A. E. Greenberg,et al.  Standard methods for the examination of water and wastewater : supplement to the sixteenth edition , 1988 .

[5]  R. Barnes,et al.  Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra , 1989 .

[6]  J. E. Jackson.  A User's Guide to Principal Components , 1991 .

[7]  Theodora Kourti,et al.  Statistical Process Control of Multivariate Processes , 1994 .

[8]  Barry M. Wise,et al.  Development and Benchmarking of Multivariate Statistical Process Control Tools for a Semiconductor Etch Process: Improving Robustness through Model Updating , 1997 .

[9]  Weihua Li,et al.  Recursive PCA for adaptive process monitoring , 1999 .

[10]  J. Lalman,et al.  Effects of C18 long chain fatty acids on glucose, butyrate and hydrogen degradation. , 2002, Water research.

[11]  S. Joe Qin,et al.  Statistical process monitoring: basics and beyond , 2003 .

[12]  T Conte,et al.  Dynamic evaluation of a fixed bed anaerobic digestion process in response to organic overloads and toxicant shock loads. , 2004, Water science and technology : a journal of the International Association on Water Pollution Research.

[13]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

[14]  O. C. Pires,et al.  Anaerobic biodegradation of oleic and palmitic acids: evidence of mass transfer limitations caused by long chain fatty acid accumulation onto the anaerobic sludge. , 2005, Biotechnology and bioengineering.

[15]  J. Roger,et al.  Robustness of models developed by multivariate calibration. Part II: The influence of pre-processing methods , 2005 .

[16]  Fuli Wang,et al.  Two‐dimensional dynamic PCA for batch process monitoring , 2005 .

[17]  H Spanjers,et al.  Instrumentation in anaerobic treatment--research and practice. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[18]  P. Peu,et al.  A new method for continuous assessment of CO2 released from dough baked in ventilated ovens , 2007 .

[19]  K. Esbensen,et al.  Transflexive Embedded near Infrared Monitoring for Key Process Intermediates in Anaerobic Digestion/Biogas Production , 2007 .

[20]  Y. Roggo,et al.  A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. , 2007, Journal of pharmaceutical and biomedical analysis.

[21]  Xiao Bin He,et al.  Variable MWPCA for Adaptive Process Monitoring , 2008 .

[22]  E. Ferreira,et al.  Activated sludge process monitoring through in situ near-infrared spectral analysis. , 2008, Water science and technology : a journal of the International Association on Water Pollution Research.

[23]  K. S. Creamer,et al.  Inhibition of anaerobic digestion process: a review. , 2008, Bioresource technology.

[24]  E. Hartung,et al.  Use of near infrared spectroscopy in monitoring of volatile fatty acids in anaerobic digestion. , 2009, Water science and technology : a journal of the International Association on Water Pollution Research.

[25]  Lijuan Xie,et al.  Quantification of glucose, fructose and sucrose in bayberry juice by NIR and PLS , 2009 .

[26]  Jyh-Cheng Jeng,et al.  Adaptive process monitoring using efficient recursive PCA and moving window PCA algorithms , 2010 .

[27]  J. Steyer,et al.  State indicators for monitoring the anaerobic digestion process. , 2010, Water research.

[28]  Kim H. Esbensen,et al.  Monitoring of anaerobic digestion processes: A review perspective , 2011 .

[29]  P. Hobbs,et al.  Evaluation of near infrared spectroscopy and software sensor methods for determination of total alkalinity in anaerobic digesters. , 2011, Bioresource technology.

[30]  J. Roger,et al.  Correction of moisture effects on near infrared calibration for the analysis of phenol content in eucalyptus wood extracts , 2008, Annals of Forest Science.

[31]  Osamu Yamanaka,et al.  A Monitoring Technique Using Multivariate Statistical Process Control Method for Performance Improvement with Application to Wastewater Treatment Plant Operation , 2012 .

[32]  C. Laroche,et al.  Predicting the biochemical methane potential of wide range of organic substrates by near infrared spectroscopy. , 2013, Bioresource technology.

[33]  Richard Dinsdale,et al.  Integration of NIRS and PCA techniques for the process monitoring of a sewage sludge anaerobic digester. , 2013, Bioresource technology.

[34]  Honggen Zhang,et al.  Development and validation of a GC–FID method for quantitative analysis of oleic acid and related fatty acids☆ , 2015, Journal of pharmaceutical analysis.

[35]  Mia Hubert,et al.  Parameter selection guidelines for adaptive PCA‐based control charts , 2016 .

[36]  B. Rong,et al.  Detection of nucleation during cooling crystallization through Moving Window PCA applied to in situ infrared data , 2017 .

[37]  Statistical process monitoring: basics and beyond , .