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

2020 ◽  
Vol 81 (2) ◽  
pp. 367-382
Author(s):  
L. Awhangbo ◽  
R. Bendoula ◽  
J. M. Roger ◽  
F. Béline
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Organic Load ◽  
Pig Slurry ◽  
Moving Window ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Load Rate ◽  
Continuously Stirred Tank Reactor ◽  
Adaptive Pca

Abstract 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.

Detecting and Identifying Spectral Anomalies Using Wavelet Processing

1998 ◽  
Vol 52 (10) ◽  
pp. 1348-1352 ◽  
Author(s):  
Chris L. Stork ◽  
David J. Veltkamp ◽  
Bruce R. Kowalski
Keyword(s):  
Wavelet Transform ◽  
Near Infrared ◽  
Principal Component ◽  
Basis Set ◽  
Multivariate Statistical ◽  
General Applicability ◽  
Statistical Process ◽  
Automated Method ◽  
Infrared Measurements ◽  
Wavelength Scale

An automated method integrating wavelet processing and techniques from multivariate statistical process control (MSPC) is presented, providing a means for the simultaneous localization, detection, and identification of disturbances in spectral data. A defining property of the wavelet transform is its ability to map a one-dimensional chemical spectrum into a two-dimensional function of wavelength and scale. Therefore, unlike the traditional MSPC approach where disturbance detection is carried out in the original wavelength domain by using a single principal component analysis (PCA) model, detection employing wavelet transform processing results in the generation of multiple models within the wavelength-scale domain. Provided that the spectral disturbance can be localized within a subregion of the wavelength-scale domain through an advantageous choice of basis set, the method described allows the identification of the underlying disturbance. The utility of the proposed method in localizing, detecting, and identifying spectral disturbances is demonstrated by using real near-infrared measurements, suggesting its general applicability in spectroscopic monitoring of chemical processes.

scholarly journals A FT-NIR Process Analytical Technology Approach for Milk Renneting Control

Foods ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Silvia Grassi ◽  
Lorenzo Strani ◽  
Cristina Alamprese ◽  
Nicolò Pricca ◽  
Ernestina Casiraghi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Process Analytical Technology ◽  
Reference Method ◽  
Principal Component ◽  
Acquisition Time ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Skimmed Milk ◽  
Analytical Technology ◽  
Fundamental Rheology

The study proposes a process analytical technology (PAT) approach for the control of milk coagulation through near infrared spectroscopy (NIRS), computing multivariate statistical process control (MSPC) charts, based on principal component analysis (PCA). Reconstituted skimmed milk and commercial pasteurized skimmed milk were mixed at two different ratios (60:40 and 40:60). Each mix ratio was prepared in six replicates and used for coagulation trials, monitored by fundamental rheology, as a reference method, and NIRS by inserting a probe directly in the coagulation vat and collecting spectra at two different acquisition times, i.e., 60 s or 10 s. Furthermore, three failure coagulation trials were performed, deliberately changing temperature or rennet and CaCl2 concentration. The comparison with fundamental rheology results confirmed the effectiveness of NIRS to monitor milk renneting. The reduced spectral acquisition time (10 s) showed data highly correlated (r > 0.99) to those acquired with longer acquisition time. The developed decision trees, based on PC1 scores and T2 MSPC charts, confirmed the suitability of the proposed approach for the prediction of coagulation times and for the detection of possible failures. In conclusion, the work provides a robust but simple PAT approach to assist cheesemakers in monitoring the coagulation step in real-time.

Multivariate statistical approaches for an early detection of foaming in a refinery SCOT unit

2018 ◽  
Vol 66 (8) ◽  
pp. 665-679
Author(s):  
Hassan Enam Al Mawla ◽  
Andreas Kroll
Keyword(s):  
Principal Component Analysis ◽  
Early Detection ◽  
Time Pressure ◽  
Principal Component ◽  
Component Analysis ◽  
Differential Pressure ◽  
Foam Formation ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Proper Actions

Abstract The formation of foam in amine units is an issue that plant operators and field personnel are confronted with on a regular basis. The inability to take proper actions in due time may result in plant downtime and increased emissions. Steep rises in differential pressure indicate foam formation, and are monitored manually in practice. Antifoaming agent is added in order to reduce foaming, but this is usually carried out under time pressure. Hence, plant operating authorities have expressed a strong interest in a data-driven solution capable of providing an early warning against foaming. The classical univariate alarm associated with differential pressure can be ineffective for foaming detection due to high misdetection rates and its lateness of detection. Modern univariate approaches based on pattern recognition techniques may not be suitable either for an early detection, as no universally distinctive features of differential pressure are observed prior to foaming in the present study. In this contribution, the multivariate statistical process monitoring approach based on principal component analysis (PCA) is applied to the early detection of foaming in a continuously operated Shell Claus Off-gas Treating (SCOT) unit of a major refinery in Germany. The results are extended to facilitate fully automated and adaptive modeling based on exponentially weighted recursive principal component analysis (EWRPCA).

scholarly journals False Alarms Rate Reduction Using Filtered Monitoring Indices

2017 ◽  
Vol 2 (1) ◽  
pp. 40-50
Author(s):  
M. AMMICHE ◽  
A. KOUADRI
Keyword(s):  
Fuzzy Logic ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Principal Component ◽  
Real Data ◽  
False Alarms ◽  
Detection Accuracy ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Number Of False Alarms

False alarms are the major problem in fault detection when using multivariate statistical process monitoring such as principal component analysis (PCA), they affect the detection accuracy and lead to make wrong decisions about the process operation status. In this work, filtering the monitoring indices is proposed to enhance the detection by reducing the number of false alarms. The filters that were used are: Standard Median Filter (SMF), Improved Median Filter (IMF) and fuzzy logic based filter. Signal to Noise Ratio (SNR), False Alarms Rate (FAR) and the detection time of the fault were used as criteria to compare their performance and their filtering action influence on monitoring. The algorithms were applied to cement rotary kiln data; real data, to remove spikes and outliers on the monitoring indices of PCA, and then, the filtered signals were used to supervise the system. The results, in which the fuzzy logic based filter showed a satisfactory performance, are presented and discussed.

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