scholarly journals A Process Analytical Concept for In-Line FTIR Monitoring of Polysiloxane Formation

Polymers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 2473
Author(s):  
Julia C. Steinbach ◽  
Markus Schneider ◽  
Otto Hauler ◽  
Günter Lorenz ◽  
Karsten Rebner ◽  
...  
Keyword(s):  
Process Control ◽  
Real Time ◽  
Time Course ◽  
Morphological Changes ◽  
Univariate Analysis ◽  
Principal Component ◽  
Synthesis Process ◽  
Real Time Monitoring ◽  
Wide Range ◽  
Batch Modelling

The chemical synthesis of polysiloxanes from monomeric starting materials involves a series of hydrolysis, condensation and modification reactions with complex monomeric and oligomeric reaction mixtures. Real-time monitoring and precise process control of the synthesis process is of great importance to ensure reproducible intermediates and products and can readily be performed by optical spectroscopy. In chemical reactions involving rapid and simultaneous functional group transformations and complex reaction mixtures, however, the spectroscopic signals are often ambiguous due to overlapping bands, shifting peaks and changing baselines. The univariate analysis of individual absorbance signals is hence often only of limited use. In contrast, batch modelling based on the multivariate analysis of the time course of principal components (PCs) derived from the reaction spectra provides a more efficient tool for real-time monitoring. In batch modelling, not only single absorbance bands are used but information over a broad range of wavelengths is extracted from the evolving spectral fingerprints and used for analysis. Thereby, process control can be based on numerous chemical and morphological changes taking place during synthesis. “Bad” (or abnormal) batches can quickly be distinguished from “normal” ones by comparing the respective reaction trajectories in real time. In this work, FTIR spectroscopy was combined with multivariate data analysis for the in-line process characterization and batch modelling of polysiloxane formation. The synthesis was conducted under different starting conditions using various reactant concentrations. The complex spectral information was evaluated using chemometrics (principal component analysis, PCA). Specific spectral features at different stages of the reaction were assigned to the corresponding reaction steps. Reaction trajectories were derived based on batch modelling using a wide range of wavelengths. Subsequently, complexity was reduced again to the most relevant absorbance signals in order to derive a concept for a low-cost process spectroscopic set-up which could be used for real-time process monitoring and reaction control.

scholarly journals Separating damage from environmental effects affecting civil structures for near real-time damage detection

2017 ◽  
Vol 17 (4) ◽  
pp. 850-868 ◽  
Author(s):  
William Soo Lon Wah ◽  
Yung-Tsang Chen ◽  
Gethin Wyn Roberts ◽  
Ahmed Elamin
Keyword(s):  
Damage Detection ◽  
Real Time ◽  
Environmental Effects ◽  
Environmental Conditions ◽  
Principal Component ◽  
Detection Methods ◽  
Real Time Monitoring ◽  
Civil Structures ◽  
Data Set ◽  
Wide Range

Analyzing changes in vibration properties (e.g. natural frequencies) of structures as a result of damage has been heavily used by researchers for damage detection of civil structures. These changes, however, are not only caused by damage of the structural components, but they are also affected by the varying environmental conditions the structures are faced with, such as the temperature change, which limits the use of most damage detection methods presented in the literature that did not account for these effects. In this article, a damage detection method capable of distinguishing between the effects of damage and of the changing environmental conditions affecting damage sensitivity features is proposed. This method eliminates the need to form the baseline of the undamaged structure using damage sensitivity features obtained from a wide range of environmental conditions, as conventionally has been done, and utilizes features from two extreme and opposite environmental conditions as baselines. To allow near real-time monitoring, subsequent measurements are added one at a time to the baseline to create new data sets. Principal component analysis is then introduced for processing each data set so that patterns can be extracted and damage can be distinguished from environmental effects. The proposed method is tested using a two-dimensional truss structure and validated using measurements from the Z24 Bridge which was monitored for nearly a year, with damage scenarios applied to it near the end of the monitoring period. The results demonstrate the robustness of the proposed method for damage detection under changing environmental conditions. The method also works despite the nonlinear effects produced by environmental conditions on damage sensitivity features. Moreover, since each measurement is allowed to be analyzed one at a time, near real-time monitoring is possible. Damage progression can also be given from the method which makes it advantageous for damage evolution monitoring.

scholarly journals Direct Analysis in Real Time by Mass Spectrometric Technique for Determining the Variation in Metabolite Profiles ofCinnamomum tamalaNees and Eberm Genotypes

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Vineeta Singh ◽  
Atul Kumar Gupta ◽  
S. P. Singh ◽  
Anil Kumar
Keyword(s):  
Real Time ◽  
Principal Component ◽  
Direct Analysis ◽  
Ion Source ◽  
Diabetic Patients ◽  
Phytochemical Analysis ◽  
Active Components ◽  
Cinnamomum Tamala ◽  
Wide Range ◽  
Metabolite Profiles

Cinnamomum tamalaNees & Eberm. is an important traditional medicinal plant, mentioned in various ancient literatures such as Ayurveda. Several of its medicinal properties have recently been proved. To characterize diversity in terms of metabolite profiles ofCinnamomum tamalaNees and Eberm genotypes, a newly emerging mass spectral ionization technique direct time in real time (DART) is very helpful. The DART ion source has been used to analyze an extremely wide range of phytochemicals present in leaves ofCinnamomum tamala. Ten genotypes were assessed for the presence of different phytochemicals. Phytochemical analysis showed the presence of mainly terpenes and phenols. These constituents vary in the different genotypes ofCinnamomum tamala. Principal component analysis has also been employed to analyze the DART data of theseCinnamomumgenotypes. The result shows that the genotype ofCinnamomum tamalacould be differentiated using DART MS data. The active components present inCinnamomum tamalamay be contributing significantly to high amount of antioxidant property of leaves and, in turn, conditional effects for diabetic patients.

scholarly journals Real-time monitoring of fungal inhibition and morphological changes

2015 ◽  
Vol 119 ◽  
pp. 196-202 ◽  
Author(s):  
S.D. Aunsbjerg ◽  
K.R. Andersen ◽  
S. Knøchel
Keyword(s):  
Real Time ◽  
Morphological Changes ◽  
Real Time Monitoring ◽  
Fungal Inhibition

scholarly journals A Mathematical Model toward Real-Time Monitoring of Automotive PEM Fuel Cells

2020 ◽  
Author(s):  
Alireza Goshtasbi ◽  
Benjamin L. Pence ◽  
Jixin Chen ◽  
Michael A. DeBolt ◽  
Chunmei Wang ◽  
...  
Keyword(s):  
Fuel Cells ◽  
Real Time ◽  
Computational Cost ◽  
Membrane Electrode Assembly ◽  
Pem Fuel Cells ◽  
Membrane Electrode ◽  
Performance Measurements ◽  
Real Time Monitoring ◽  
Model Calculations ◽  
Wide Range

A computationally efficient model toward real-time monitoring of automotive polymer electrolyte membrane (PEM) fuel cell stacks is developed. Computational efficiency is achieved by spatio-temporal decoupling of the problem, developing a new reduced-order model for water balance across the membrane electrode assembly (MEA), and defining a new variable for cathode catalyst utilization that captures the trade-off between proton and mass transport limitations without additional computational cost. Together, these considerations result in the model calculations to be carried out more than an order of magnitude faster than real time. Moreover, a new iterative scheme allows for simulation of counter-flow operation and makes the model flexible for different flow configurations. The proposed model is validated with a wide range of experimental performance measurements from two different fuel cells. Finally, simulation case studies are presented to demonstrate the prediction capabilities of the model.

scholarly journals Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor

2021 ◽  
Vol 12 (2) ◽  
pp. 1205
Author(s):  
Jyothi R ◽  
Tejas Holla ◽  
Uma Rao K ◽  
Jayapal R
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Induction Motor ◽  
Diagnostic Tool ◽  
Voltage Source ◽  
Power Electronic ◽  
Real Time Monitoring ◽  
Process Industries ◽  
Ac Drives ◽  
Wide Range

AC drives are employed in process industries for varying applications resulting in a wide range of ratings. The entire process industry has seen a paradigm shift from manual to automated systems. The major factor contributing to this is the advanced power electronics technology enabling power electronic drives for smooth control of electric motors. Induction motors are most commonly used in industries. Faults in the power electronic circuits may occur periodically. These faults often go unnoticed as they rarely cause a complete shutdown and the fault levels may not be large enough to lead to a breakdown of the drive. An early detection of these faults is required to prevent their escalation into major faults. The diagnostic tool for detection of faults requires real time monitoring of the entire drive. In this work, detailed investigation of different faults that can occur in the power electronic circuit of an industrial drive is carried out. Analysis and impact of faults on the performance of the induction motor is presented. A real time monitoring platform is proposed to detect and classify the fault accurately using machine learning. A diagnostic tool also is developed to display the severity and location of the fault to the operator to take corrective measures.

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