Frank Westad - Academia.edu (original) (raw)

Papers by Frank Westad

Elsevier eBooks, 2009

ABSTRACT This chapter presents the concept and theory of independent component analysis (ICA). Th... more ABSTRACT This chapter presents the concept and theory of independent component analysis (ICA). The method originated from signal processing research, where unknown signal sources are mixed to a new set of signals. This general objective of separating signals into pure sources is called blind source separation (BSS). ICA has been shown to be useful in solving the BSS problem, and if the pure sources are found, then also the mixing system may be estimated. Similar situations occur in chemometrics where the pure spectra of chemical compounds and their concentrations are observed with different type of instrumentation such as spectroscopy. The necessity of proper validation in ICA is emphasized and put into a chemometric framework. Examples on simulated as well as spectroscopic data are shown to illustrate the potential of ICA in chemometric applications.

Journal of biomedical research & environmental sciences, Jun 1, 2023

Frontiers in analytical science, Jun 9, 2022

John Wiley & Sons, Inc. eBooks, Jul 1, 2016

Analytical and Bioanalytical Chemistry, Dec 4, 2019

Grass and Forage Science, Feb 14, 2012

The Collaborative Research Center InPROMPT aims to establish a novel process concept for the hydr... more The Collaborative Research Center InPROMPT aims to establish a novel process concept for the hydroformylation of long-chained olefins, using a rhodium complex as catalyst in the presence of syngas. Recently, the hydroformylation in micro-emulsions, which allows for the efficient recycling of the expensive rhodium catalyst, was found to be feasible. However, the temperature and concentration sensitive multi-phase system demands a continuous observation of the reaction to achieve an operational and economically feasible plant operation. For that purpose, we tested the potential of both NMR and Raman spectroscopy for process control assistance. The lab-scale experiments were supported by sampling for off-line GC-analysis as reference analytics. The results of the NMR experiments will be part of another contribution.

Within the Collaborative Research Center InPROMPT a novel process concept for the hydroformylatio... more Within the Collaborative Research Center InPROMPT a novel process concept for the hydroformylation of long-chained olefins is studied in a mini-plant, using a rhodium complex as catalyst in the presence of syngas. Recently, the hydroformylation in micro¬emulsions, which allows for the efficient recycling of the expensive rhodium catalyst, was found to be feasible. However, the high sensitivity of this multi-phase system with regard to changes in temperature and composition demands a continuous observation of the reaction to achieve a reliable and economic plant operation. For that purpose, we tested the potential of both online NMR and Raman spectroscopy for process control. The lab-scale experiments were supported by off-line GC-analysis as a reference method. A fiber optic coupled probe of a process Raman spectrometer was directly integrated into the reactor. 25 mixtures with varying concentrations of olefin (1-dodecene), product (n-tridecanal), water, n-dodecane, and technical surfactant (Marlipal 24/70) were prepared according to a D-optimal design. Online NMR spectroscopy was implemented by using a flow probe equipped with 1/16” PFA tubing serving as a flow cell. This was hyphenated to the reactor within a thermostated bypass to maintain process conditions in the transfer lines. Partial least squares regression (PLSR) models were established based on the initial spectra after activation of the reaction with syngas for the prediction of unknown concentrations of 1-dodecene and n-tridecanal over the course of the reaction in the lab-scale system. The obtained Raman spectra do not only contain information on the chemical composition but are further affected by the emulsion properties of the mixtures, which depend on the phase state and the type of micelles. Based on the spectral signature of both Raman and NMR spectra, it could be deduced that especially in reaction mixtures with high 1-dodecene content the formation of isomers as a competitive reaction was dominating. Similar trends were also observed during some of the process runs in the mini-plant. The multivariate calibration allowed for the estimation of reactants and products of the hydroformylation reaction in both laboratory setup and mini-plant.

arXiv (Cornell University), Jul 1, 2023

Sensors

Biodegradable magnesium-based implants offer mechanical properties similar to natural bone, makin... more Biodegradable magnesium-based implants offer mechanical properties similar to natural bone, making them advantageous over nonbiodegradable metallic implants. However, monitoring the interaction between magnesium and tissue over time without interference is difficult. A noninvasive method, optical near-infrared spectroscopy, can be used to monitor tissue’s functional and structural properties. In this paper, we collected optical data from an in vitro cell culture medium and in vivo studies using a specialized optical probe. Spectroscopic data were acquired over two weeks to study the combined effect of biodegradable Mg-based implant disks on the cell culture medium in vivo. Principal component analysis (PCA) was used for data analysis. In the in vivo study, we evaluated the feasibility of using the near-infrared (NIR) spectra to understand physiological events in response to magnesium alloy implantation at specific time points (Day 0, 3, 7, and 14) after surgery. Our results show tha...

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description, 2022

During drilling and logging, depth alignment of well logs acquired in the same borehole section a... more During drilling and logging, depth alignment of well logs acquired in the same borehole section at different times is a vital preprocessing step before any petrophysical analysis. Depth alignment requires high precision as depth misalignment between different log curve measurements can substantially suppress possible correlations between formation properties, leading to imprecise interpretation or even misinterpretation. Standard depth alignment involves cross correlation, which typically requires user intervention for reliability. To improve the depth alignment process, we apply deep-learning techniques and propose a simple and practical implementation of a one-dimensional (1D) supervised convolutional neural network (1D CNN). We train seven CNN models using different log measurements, such as gamma ray, resistivity, P- and S-wave sonic, density, neutron, and photoelectric factor (PEF), to estimate depth mismatches between the corresponding raw logging-while-drilling (LWD) and elec...

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description, 2022

The oil and gas industry of today is undergoing rapid digitalization. This implies a massive effo... more The oil and gas industry of today is undergoing rapid digitalization. This implies a massive effort to transform standard work procedures and workflows into more efficient practices and implementations using machine learning (ML) and automation. This will enable geoscientists to explore and exploit vast amounts of data quickly and efficiently. To address these current industry challenges, we propose a pilot well-log database in HDF5 (Hierarchical Data Format version 5) format that can be continuously extended if new data become available. It also provides versatility for data preparation for further analysis. We show an alternative way to store and use log files in a hierarchical structure that is easy to understand and handle by research institutes, companies, and academia. We also touch upon well-log depth matching, a long-standing industry challenge, to synchronize data from different logging passes to a single depth reference. Having a robust automated solution for depth matchin...

SEG Technical Program Expanded Abstracts 2020, 2020

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Jul 1, 2021

In this paper, it is presented how Cross Model Validation (CMV), also known as double cross valid... more In this paper, it is presented how Cross Model Validation (CMV), also known as double cross validation, efficiently can be applied for variable selection in spectroscopic applications. The chosen applications are FT-IR spectroscopic measurements of mixtures of marzipan and NIR spectra of diesel fuels. Standard Normal Variate (SNV) is applied as a spectral pre-treatment to reduce baseline effects in the spectra for the FT-IR data whereas 2nd derivative was applied for the diesel fuels. Variable selection based on jack-knifing and frequency of significance from Cross Model Validation is employed for identifying non-relevant spectral regions as well as providing a relevant subset for model optimization. The results show a high degree of correspondence between the objectively found wavelength bands and the reported chemical interpretation found in the literature. In addition, the stability of the models due to conservative validation with respect to predictive performance is exemplified. Finally, an example of how the use of downweighing variables ensures optimal prediction ability and detailed model interpretation is shown.

Frontiers in Analytical Science

Variable selection is a topic of interest in many scientific communities. Within chemometrics, wh... more Variable selection is a topic of interest in many scientific communities. Within chemometrics, where the number of variables for multi-channel instruments like NIR spectroscopy and metabolomics in many situations is larger than the number of samples, the strategy has been to use latent variable regression methods to overcome the challenges with multiple linear regression. Thereby, there is no need to remove variables as such, as the low-rank models handle collinearity and redundancy. In most studies on variable selection, the main objective was to compare the prediction performance (RMSE or accuracy in classification) between various methods. Nevertheless, different methods with the same objective will, in most cases, give results that are not significantly different. In this study, we present three other main objectives: i) to eliminate variables that are not relevant; ii) to return a small subset of variables that has the same or better prediction performance as a model with all o...

Elsevier eBooks, 2009

ABSTRACT This chapter presents the concept and theory of independent component analysis (ICA). Th... more ABSTRACT This chapter presents the concept and theory of independent component analysis (ICA). The method originated from signal processing research, where unknown signal sources are mixed to a new set of signals. This general objective of separating signals into pure sources is called blind source separation (BSS). ICA has been shown to be useful in solving the BSS problem, and if the pure sources are found, then also the mixing system may be estimated. Similar situations occur in chemometrics where the pure spectra of chemical compounds and their concentrations are observed with different type of instrumentation such as spectroscopy. The necessity of proper validation in ICA is emphasized and put into a chemometric framework. Examples on simulated as well as spectroscopic data are shown to illustrate the potential of ICA in chemometric applications.

Journal of biomedical research & environmental sciences, Jun 1, 2023

Frontiers in analytical science, Jun 9, 2022

John Wiley & Sons, Inc. eBooks, Jul 1, 2016

Analytical and Bioanalytical Chemistry, Dec 4, 2019

Grass and Forage Science, Feb 14, 2012

The Collaborative Research Center InPROMPT aims to establish a novel process concept for the hydr... more The Collaborative Research Center InPROMPT aims to establish a novel process concept for the hydroformylation of long-chained olefins, using a rhodium complex as catalyst in the presence of syngas. Recently, the hydroformylation in micro-emulsions, which allows for the efficient recycling of the expensive rhodium catalyst, was found to be feasible. However, the temperature and concentration sensitive multi-phase system demands a continuous observation of the reaction to achieve an operational and economically feasible plant operation. For that purpose, we tested the potential of both NMR and Raman spectroscopy for process control assistance. The lab-scale experiments were supported by sampling for off-line GC-analysis as reference analytics. The results of the NMR experiments will be part of another contribution.

Within the Collaborative Research Center InPROMPT a novel process concept for the hydroformylatio... more Within the Collaborative Research Center InPROMPT a novel process concept for the hydroformylation of long-chained olefins is studied in a mini-plant, using a rhodium complex as catalyst in the presence of syngas. Recently, the hydroformylation in micro¬emulsions, which allows for the efficient recycling of the expensive rhodium catalyst, was found to be feasible. However, the high sensitivity of this multi-phase system with regard to changes in temperature and composition demands a continuous observation of the reaction to achieve a reliable and economic plant operation. For that purpose, we tested the potential of both online NMR and Raman spectroscopy for process control. The lab-scale experiments were supported by off-line GC-analysis as a reference method. A fiber optic coupled probe of a process Raman spectrometer was directly integrated into the reactor. 25 mixtures with varying concentrations of olefin (1-dodecene), product (n-tridecanal), water, n-dodecane, and technical surfactant (Marlipal 24/70) were prepared according to a D-optimal design. Online NMR spectroscopy was implemented by using a flow probe equipped with 1/16” PFA tubing serving as a flow cell. This was hyphenated to the reactor within a thermostated bypass to maintain process conditions in the transfer lines. Partial least squares regression (PLSR) models were established based on the initial spectra after activation of the reaction with syngas for the prediction of unknown concentrations of 1-dodecene and n-tridecanal over the course of the reaction in the lab-scale system. The obtained Raman spectra do not only contain information on the chemical composition but are further affected by the emulsion properties of the mixtures, which depend on the phase state and the type of micelles. Based on the spectral signature of both Raman and NMR spectra, it could be deduced that especially in reaction mixtures with high 1-dodecene content the formation of isomers as a competitive reaction was dominating. Similar trends were also observed during some of the process runs in the mini-plant. The multivariate calibration allowed for the estimation of reactants and products of the hydroformylation reaction in both laboratory setup and mini-plant.

arXiv (Cornell University), Jul 1, 2023

Sensors

Biodegradable magnesium-based implants offer mechanical properties similar to natural bone, makin... more Biodegradable magnesium-based implants offer mechanical properties similar to natural bone, making them advantageous over nonbiodegradable metallic implants. However, monitoring the interaction between magnesium and tissue over time without interference is difficult. A noninvasive method, optical near-infrared spectroscopy, can be used to monitor tissue’s functional and structural properties. In this paper, we collected optical data from an in vitro cell culture medium and in vivo studies using a specialized optical probe. Spectroscopic data were acquired over two weeks to study the combined effect of biodegradable Mg-based implant disks on the cell culture medium in vivo. Principal component analysis (PCA) was used for data analysis. In the in vivo study, we evaluated the feasibility of using the near-infrared (NIR) spectra to understand physiological events in response to magnesium alloy implantation at specific time points (Day 0, 3, 7, and 14) after surgery. Our results show tha...

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description, 2022

During drilling and logging, depth alignment of well logs acquired in the same borehole section a... more During drilling and logging, depth alignment of well logs acquired in the same borehole section at different times is a vital preprocessing step before any petrophysical analysis. Depth alignment requires high precision as depth misalignment between different log curve measurements can substantially suppress possible correlations between formation properties, leading to imprecise interpretation or even misinterpretation. Standard depth alignment involves cross correlation, which typically requires user intervention for reliability. To improve the depth alignment process, we apply deep-learning techniques and propose a simple and practical implementation of a one-dimensional (1D) supervised convolutional neural network (1D CNN). We train seven CNN models using different log measurements, such as gamma ray, resistivity, P- and S-wave sonic, density, neutron, and photoelectric factor (PEF), to estimate depth mismatches between the corresponding raw logging-while-drilling (LWD) and elec...

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description, 2022

The oil and gas industry of today is undergoing rapid digitalization. This implies a massive effo... more The oil and gas industry of today is undergoing rapid digitalization. This implies a massive effort to transform standard work procedures and workflows into more efficient practices and implementations using machine learning (ML) and automation. This will enable geoscientists to explore and exploit vast amounts of data quickly and efficiently. To address these current industry challenges, we propose a pilot well-log database in HDF5 (Hierarchical Data Format version 5) format that can be continuously extended if new data become available. It also provides versatility for data preparation for further analysis. We show an alternative way to store and use log files in a hierarchical structure that is easy to understand and handle by research institutes, companies, and academia. We also touch upon well-log depth matching, a long-standing industry challenge, to synchronize data from different logging passes to a single depth reference. Having a robust automated solution for depth matchin...

SEG Technical Program Expanded Abstracts 2020, 2020

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Jul 1, 2021

In this paper, it is presented how Cross Model Validation (CMV), also known as double cross valid... more In this paper, it is presented how Cross Model Validation (CMV), also known as double cross validation, efficiently can be applied for variable selection in spectroscopic applications. The chosen applications are FT-IR spectroscopic measurements of mixtures of marzipan and NIR spectra of diesel fuels. Standard Normal Variate (SNV) is applied as a spectral pre-treatment to reduce baseline effects in the spectra for the FT-IR data whereas 2nd derivative was applied for the diesel fuels. Variable selection based on jack-knifing and frequency of significance from Cross Model Validation is employed for identifying non-relevant spectral regions as well as providing a relevant subset for model optimization. The results show a high degree of correspondence between the objectively found wavelength bands and the reported chemical interpretation found in the literature. In addition, the stability of the models due to conservative validation with respect to predictive performance is exemplified. Finally, an example of how the use of downweighing variables ensures optimal prediction ability and detailed model interpretation is shown.

Frontiers in Analytical Science

Variable selection is a topic of interest in many scientific communities. Within chemometrics, wh... more Variable selection is a topic of interest in many scientific communities. Within chemometrics, where the number of variables for multi-channel instruments like NIR spectroscopy and metabolomics in many situations is larger than the number of samples, the strategy has been to use latent variable regression methods to overcome the challenges with multiple linear regression. Thereby, there is no need to remove variables as such, as the low-rank models handle collinearity and redundancy. In most studies on variable selection, the main objective was to compare the prediction performance (RMSE or accuracy in classification) between various methods. Nevertheless, different methods with the same objective will, in most cases, give results that are not significantly different. In this study, we present three other main objectives: i) to eliminate variables that are not relevant; ii) to return a small subset of variables that has the same or better prediction performance as a model with all o...