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Selected papers from the latest issue:Process monitoring based on mode identification for multi-mode process with transitions
28 October 2011, 20:56:33
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, Available online 28 October 2011
Fuli Wang, Shuai Tan, Jun Peng, Yuqing Chang
Some industrial processes frequently change due to various factors, such as alterations of feedstocks and compositions, different manufacturing strategies, fluctuations in the external environment and various product specifications. Most multivariate statistical techniques are under the assumption that the process has one nominal operation region. The performance of it is not good when they are used to monitor the process with multiple operation regions. In this paper, we developed an effective approach for monitoring multi-mode continuous processes with the following improvements. 1). Offline mode identification algorithm is proposed to identify (i) stable modes, (ii) transitional modes between two stable modes, (iii) noise. 2). According to the data distribution, proper multivariate statistical algorithm is selected automatically to realize fault detection for each mode. 3). When online monitoring, the right model is chosen based on Mode Transformation Probability (MTP), which makes full use of the empirical knowledge hidden in offline data. This method can enhance real-time performance of online mode identification for continuous process and timely monitoring can be further realized. The proposed method is illustrated by application in furnace temperature system of continuous annealing line. The effectiveness of mode identification and fault detection is demonstrated in the results.
Source: Chemometrics and Intelligent Laboratory Systems, Available online 28 October 2011
Fuli Wang, Shuai Tan, Jun Peng, Yuqing Chang
Some industrial processes frequently change due to various factors, such as alterations of feedstocks and compositions, different manufacturing strategies, fluctuations in the external environment and various product specifications. Most multivariate statistical techniques are under the assumption that the process has one nominal operation region. The performance of it is not good when they are used to monitor the process with multiple operation regions. In this paper, we developed an effective approach for monitoring multi-mode continuous processes with the following improvements. 1). Offline mode identification algorithm is proposed to identify (i) stable modes, (ii) transitional modes between two stable modes, (iii) noise. 2). According to the data distribution, proper multivariate statistical algorithm is selected automatically to realize fault detection for each mode. 3). When online monitoring, the right model is chosen based on Mode Transformation Probability (MTP), which makes full use of the empirical knowledge hidden in offline data. This method can enhance real-time performance of online mode identification for continuous process and timely monitoring can be further realized. The proposed method is illustrated by application in furnace temperature system of continuous annealing line. The effectiveness of mode identification and fault detection is demonstrated in the results.
Highlights
► A new mode identification algorithm is firstly proposed for multi-mode continuous process to identify stable modes, transitional modes between two stable modes and noise. ► According to the data distribution, proper multivariate statistical algorithm is selected automatically to realize fault detection for each mode. ► Online mode identification for multi-mode continuous process is realized based on Mode Transformation Probability (MTP), which makes full use of the empirical knowledge hidden in offline data.The optimal mixture design of experiments: Alternative method in optimizing the aqueous phase composition of a microemulsion
26 October 2011, 20:50:53
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, Available online 25 October 2011
Zahra Jeirani, Badrul Mohamed Jan, Brahim Si Ali, Ishenny Mohd. Noor, See Chun Hwa, ...
In this paper, an optimum mixture Design of Experiment (DOE) method was used to determine the optimum aqueous phase formulation of a microemulsion. Based on the Design Expert software, a quadratic model was established as a function of the microemulsion component fractions. The model was validated experimentally using an ANOVA table. The diagnostics of the model were also investigated by using Normal Plot of Residuals and Box-Cox Plot. In addition, the effects of the microemulsion component fractions on IFT variation were also studied. Finally, the model was optimized to predict the optimum conditions that would yield minimum IFT. It was observed that the predicted and experimental IFT values at the optimum condition are in good agreement with an error of about 1.5%. The authors concluded that the optimum mixture DOE is reliable and could be used to optimize the composition of a microemulsion system such as enhanced oil recovery (EOR) process.
Source: Chemometrics and Intelligent Laboratory Systems, Available online 25 October 2011
Zahra Jeirani, Badrul Mohamed Jan, Brahim Si Ali, Ishenny Mohd. Noor, See Chun Hwa, ...
In this paper, an optimum mixture Design of Experiment (DOE) method was used to determine the optimum aqueous phase formulation of a microemulsion. Based on the Design Expert software, a quadratic model was established as a function of the microemulsion component fractions. The model was validated experimentally using an ANOVA table. The diagnostics of the model were also investigated by using Normal Plot of Residuals and Box-Cox Plot. In addition, the effects of the microemulsion component fractions on IFT variation were also studied. Finally, the model was optimized to predict the optimum conditions that would yield minimum IFT. It was observed that the predicted and experimental IFT values at the optimum condition are in good agreement with an error of about 1.5%. The authors concluded that the optimum mixture DOE is reliable and could be used to optimize the composition of a microemulsion system such as enhanced oil recovery (EOR) process.
Highlights
► A model was established as a function of the microemulsion component fractions. ► The model was validated experimentally using an ANOVA table. ► The model was optimized to predict the optimum conditions yielding minimum IFT. ► The predicted and experimental IFT at the optimum condition are in good agreement.The chemometrics approach applied to FTIR spectral data for analysis of rice bran oil in extra virgin olive oil
26 October 2011, 20:50:53
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, Available online 25 October 2011
Abdul Rohman, .B. Che Man
Among eleven studied vegetable oils, rice bran oil (RBO) has the close similarity to extra virgin olive oil (EVOO) in terms of FTIR spectra, as shown in the score plot of first and second principal components. The peak intensities at 18 frequency regions were used as matrix variables in principal component analysis (PCA). Consequently, the presence of RBO in EVOO is difficult to detect. This study aimed to use the chemometrics approach, namely discriminant analysis (DA) and multivariate calibrations of partial least square and principle component regression to analyze RBO in EVOO. DA was used for classification of EVOO and EVOO mixed with RBO. Multivariate calibrations were exploited for quantification of RBO in EVOO. The combined frequency regions of 1200 – 900 and 3020 – 3000 cmwere used for such analysis. The results showed that no misclassification was reported for classification of EVOO and EVOO mixed with RBO. Partial least square regression either using normal or first derivative FTIR spectra can be successfully used for quantification of RBO in EVOO. In addition, analysis of fatty acid composition can complement the results obtained from FTIR spectral data.
Source: Chemometrics and Intelligent Laboratory Systems, Available online 25 October 2011
Abdul Rohman, .B. Che Man
Among eleven studied vegetable oils, rice bran oil (RBO) has the close similarity to extra virgin olive oil (EVOO) in terms of FTIR spectra, as shown in the score plot of first and second principal components. The peak intensities at 18 frequency regions were used as matrix variables in principal component analysis (PCA). Consequently, the presence of RBO in EVOO is difficult to detect. This study aimed to use the chemometrics approach, namely discriminant analysis (DA) and multivariate calibrations of partial least square and principle component regression to analyze RBO in EVOO. DA was used for classification of EVOO and EVOO mixed with RBO. Multivariate calibrations were exploited for quantification of RBO in EVOO. The combined frequency regions of 1200 – 900 and 3020 – 3000 cmwere used for such analysis. The results showed that no misclassification was reported for classification of EVOO and EVOO mixed with RBO. Partial least square regression either using normal or first derivative FTIR spectra can be successfully used for quantification of RBO in EVOO. In addition, analysis of fatty acid composition can complement the results obtained from FTIR spectral data.
Highlights
► We used chemometrics for authentication of extra virgin olive oil (EVOO). ► Discriminant analysis classifies EVOO and EVOO adulterated with rice bran oil. ► Partial least square can successfully quantify rice bran oil in EVOO.A statistical data-processing methodology of Py-GC/MS data for the simulation of flash co-pyrolysis reactor experiments
26 October 2011, 20:50:53
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, Available online 25 October 2011
Tom Cornelissen, Geert Molenberghs, Maarten Jans, Jan Yperman, Sonja Schreurs, ...
Practically it is extremely difficult to collect observations following a fully sound statistical design, typically encompassing a high number of repetitions, of an intensive and elaborate experimental procedure such as flash pyrolysis reactor experiments. Pyrolysis - gas chromatography / mass spectrometry (Py-GC/MS) is an extremely useful analytical technique in order to simulate a high number of repetitive pyrolysis experiments in an acceptable time span. Combining Py-GC/MS experiments and statistical data processing, conclusions can be drawn on the pyrolysis behaviour of any input material, supplying crucial information on its respective behaviour during the actual flash pyrolysis experiments.In this research Py-GC/MS experiments combined with a tailored statistical data processing methodology strongly indicate the occurrence of synergetic interactions during the flash co-pyrolysis of willow/polyhydroxybutyrate (PHB) blends. Such interactions are also indicated by pattern recognition and by the analysis of the condensable and noncondensable pyrolytic gases obtained from Py-GC/MS. Accordingly, the actual influence of the flash co-pyrolysis of willow and PHB, executed with a semi-continuous pyrolysis reactor, on the pyrolytic oil features is investigated by GC/MS. Based on these experiments an explanation for the observed synergy during flash co-pyrolysis of willow and PHB is proposed.
Source: Chemometrics and Intelligent Laboratory Systems, Available online 25 October 2011
Tom Cornelissen, Geert Molenberghs, Maarten Jans, Jan Yperman, Sonja Schreurs, ...
Practically it is extremely difficult to collect observations following a fully sound statistical design, typically encompassing a high number of repetitions, of an intensive and elaborate experimental procedure such as flash pyrolysis reactor experiments. Pyrolysis - gas chromatography / mass spectrometry (Py-GC/MS) is an extremely useful analytical technique in order to simulate a high number of repetitive pyrolysis experiments in an acceptable time span. Combining Py-GC/MS experiments and statistical data processing, conclusions can be drawn on the pyrolysis behaviour of any input material, supplying crucial information on its respective behaviour during the actual flash pyrolysis experiments.In this research Py-GC/MS experiments combined with a tailored statistical data processing methodology strongly indicate the occurrence of synergetic interactions during the flash co-pyrolysis of willow/polyhydroxybutyrate (PHB) blends. Such interactions are also indicated by pattern recognition and by the analysis of the condensable and noncondensable pyrolytic gases obtained from Py-GC/MS. Accordingly, the actual influence of the flash co-pyrolysis of willow and PHB, executed with a semi-continuous pyrolysis reactor, on the pyrolytic oil features is investigated by GC/MS. Based on these experiments an explanation for the observed synergy during flash co-pyrolysis of willow and PHB is proposed.
Highlights
► A full statistical data processing methodology is applied on Py-GC/MS experiments ► Composition changes during co-pyrolysis in various ratios of willow waste and PHB ► Synergy is proved in flash co-pyrolysis of willow waste and PHB in various ratiosVariable Selection for Multifactorial Genomic Data
26 October 2011, 20:50:53
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, Available online 25 October 2011
Sonia Tarazona, Sonia Prado-López, Joaquín Dopazo, Alberto Ferrer, Ana Conesa
Dimension reduction techniques are used to explore genomic data. Due to the large number of variables (genes) included in this kind of studies, variable selection methods are needed to identify the most responsive genes in order to get a better interpretation of the results or to conduct more specific experiments. These methods should be consistent with the amount of signal in the data. For this purpose, we introduce a novel selection strategy called minAS and also adapt other existing strategies, such us Gamma approximation, resampling techniques, etc. All of them are based on studying the distribution of statistics measuring the importance of the variables in the model. These strategies have been applied to the ASCA-genes analysis framework and more generally to dimension reduction techniques as PCA. The performance of the different strategies was evaluated using simulated data. The best performing methods were then applied on an experimental dataset containing the transcriptomic profiles of human embryonic stem cells cultured under different oxygen concentrations. The ability of the methods to extract relevant biological information from the data is discussed.
Source: Chemometrics and Intelligent Laboratory Systems, Available online 25 October 2011
Sonia Tarazona, Sonia Prado-López, Joaquín Dopazo, Alberto Ferrer, Ana Conesa
Dimension reduction techniques are used to explore genomic data. Due to the large number of variables (genes) included in this kind of studies, variable selection methods are needed to identify the most responsive genes in order to get a better interpretation of the results or to conduct more specific experiments. These methods should be consistent with the amount of signal in the data. For this purpose, we introduce a novel selection strategy called minAS and also adapt other existing strategies, such us Gamma approximation, resampling techniques, etc. All of them are based on studying the distribution of statistics measuring the importance of the variables in the model. These strategies have been applied to the ASCA-genes analysis framework and more generally to dimension reduction techniques as PCA. The performance of the different strategies was evaluated using simulated data. The best performing methods were then applied on an experimental dataset containing the transcriptomic profiles of human embryonic stem cells cultured under different oxygen concentrations. The ability of the methods to extract relevant biological information from the data is discussed.
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