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A continuous esterification is performed in micro-structured reactors, and monitored with miniaturized MIR spectroscopy. Measured spectra are immediately analyzed with a PEAXACT predictor in Bruker's OPUS PROCESS. Predictions allow for real-time observation or control of the process.
The influence of water traces on the properties of Ionic Liquids is assessed by MIR spectroscopy. Spectra are analyzed with a spectral Hard Modeling approach. Resulting peak parameters, e.g., peak shifts, are used for quantitative determination of mixture physics.
Benchtop NMR equipment in continuous flow mode is utilized as online process analyzer for an esterification. Spectral Hard Modeling optimizes the synchronous evaluation of 1H and 19F spectra.
Hydrocarbon streams in petrochemical processes require composition analysis, both P-I-O-N-A contents (n-/iso-/cyclo-paraffins, olefins, aromatics) and detail single component analysis. Real-time access to this information is achieved through process Raman technology. Intelligent calibration approaches efficiently reduce the effort for multi-parameter calibrations.
Monitoring the production of urea from CO2 and ammonia bears multiple challenges in an industrial environment: high pressure, strong corrosivity, very sensitive reaction equilibria, and the inaccessibility of representative process samples. Combining rugged process Raman technology and intelligent quantitative modeling achieves a robust PAT solution.
Raman spectroscopy is the enabling PAT in hydrocarbons processing, as it can differentiate easily between the components involved. Presented examples are linear and branched alkanes, aromatic isomers, or different chain lengths of hydrocarbons as they appear in distillation, cracking, and reforming units of petrochemical refineries.
In continuous flow chemistry, studying complex reaction mixtures in real time is challenging, but provides valuable insights to enhance reaction understanding and control. We highlight the integration of NMR spectroscopy in a miniaturized flow process for nitration.
Real-time monitoring of complex molecules in fine chemical synthesis is achieved with NMR spectroscopy in flow mode. Where high-field spectra allow straightforward Peak Integration, benchtop low-field instruments benefit from Spectral Hard Modeling to quantify the components.
Acrylic acid and related monomers are continuously polymerized in aqueous solution. For MIR spectroscopic monitoring, probes are installed along the entire reactor length. Spectral Hard Modeling allows to separate monomer and polymer contributions to the spectra.
CO2 is converted with epoxides into innovative polyethercarbonates. The formation of carbonate and polyether moieties is observed inline with ATR-MIR spectroscopy under harsh conditions. Spectral Hard Modeling allows bottom-up modelling for quantification.
Synthetic rubber consists of dienes like butadiene, isoprene or chloroprene, and olefins like acrylonitrile, styrene or others. The formation of the polymer can be tracked with Raman spectroscopy, not only in terms of monomer conversion, but also in terms of polymer microstructure (cis-/trans-functionalization, 1.2- vs. 1.4-insertion etc.).
MIR spectroscopy is applied for process monitoring in a pilot-scale fermentation of sugars. Thanks to a Spectral Hard Modeling approach, the water spectrum is accounted for in the analysis step. Online substrate content prediction can act as a shut-off criterion for the process.
Substrate and product content in an industrial bio-process are monitored by Raman spectroscopy. Time resolution is improved dramatically compared to offline analysis, and the immediate access to the process performance enables the real-time process control.
The generation of bio-methane in a converter of industrial sewage sludge is monitored by gas-phase Raman spectroscopy. Measurements are performed directly in the product gas stream. The influence of process parameter changes is well reflected in the predicted methane profiles.
Platform chemicals based on renewable feedstocks typically require the hydrogenation of plant-based carbohydrates (sugars, cellulose, …) in pressurized reactors before they undergo consecutive reactions. These conversions are well observable with inline Raman spectroscopy.
Does spectral analysis always need to rely on the same method/approach? Taking bioprocesses with their abundancy of process parameters, which are typically modeled by multivariate statistics only, we show how to use the entire chemometrics toolbox more efficiently with PEAXACT.
Cell cultivations are essential in the production of biopharmaceuticals and therapeutics. Raman spectroscopy offers the highest benefits for the monitoring of these processes, but requires careful and sound analysis of the spectral data.
The Monipa® analyzer by IRUBIS, based on mid-infrared technology, has successfully demonstrated its capabilities in monitoring the ultrafiltration-diafiltration workup of a monoclonal antibody used as a biosimilar.
Key process steps for the valorization of Biomass in a biorefinery can be elucidated, monitored, and optimized with spectroscopic PAT tools: infrared for the chemical pretreatment, NMR and Raman for the generation of platform intermediates, and Raman for the downstream work-up. In all cases, Spectral Hard Modeling allows precise quantification of the process mixtures.
Complex multiphase solvent system are effectively monitored by inline MIR and Raman spectroscopy, combined with Spectral Hard Modeling, to avoid equilibrium disturbance by conventional sampling and offline analysis. The distribution of dissolved gases such as CO2 or of biorefinery key chemicals like 5-HMF are easily accessible and enable targeted process tuning.
A glass-made tray column is equipped with a fiber-optic 4-sensor MIR spectroscopic system. Concentrations are not only measured in boiler and condenser, but also on the trays. A Spectral Hard Modeling analysis restricts the required reference measurements to ambient temperature.
The development of an API crystallization process is monitored with ATR-MIR spectroscopy throughout lab and pilot plant scale. Model transfer is significantly facilitated through the use of Spectral Hard Modeling.
A Raman analyzer with multiple probes is used to monitor a distillation column, operated with isomer mixtures for performance characterization. Composition changes can be tracked down to the sub-percent range and thus serve as a valuable input for column design and operation.
In a high-pressure autoclave the phase composition of a biphasic reaction is monitored in both phases simultaneously. Reaction progress and mass transfer can easily be separated for a model-based process optimization.
Carotenoid contents of various food samples are determined with UV spectroscopy. Though similar in structure, a Spectral Hard Modeling analysis is capable of separating the overlapping spectral contributions of the different components.
The robustness of a Raman and mid-IR based substance identification is achieved with a discriminant analysis approach. Spectra of typical Pharma ingredients are recorded as a ring study under systematically varied conditions. Classification models are trained and validated with independent material samples.
Silanes are highly versatile materials for a multitude of applications. Product properties are adjusted by blending additives or stabilizers, according to the field of application. Low-field nuclear magnetic resonance spectroscopy (LF-NMR) combined with spectral analysis using Spectral Hard Modeling is the key to precise release control.
Quantitative nuclear magnetic resonance (qNMR) is used within the quality control procedure of silanes. PEAXACT methods are used to analyze complex overlapping signals within the TopSpin/IconNMR environment, and were therefore embedded into the automated qNMR workflow via the PEAXACT AppServer.
The degree of hydrogenation of liquid organic hydrogen carriers (LOHC) is analyzed with a novel Raman technology from Optoquant. Real process samples containing benzyl toluene, dibenzyl toluene, and other LOHCs are analyzed by the use of a ratiometric PEAXACT model.
Water vapor in MIR spectra is compensated through an Indirect Hard Modeling approach. Treating water vapor as a regular mixture component allows to account for variations in the overall content. The main component profiles emerge free of interference.
Gas chromatograms are classified before quantitative analysis. A model-based unsupervised approval procedure identifies typical irregularities like missing or additional peaks, retention time shifts, etc. Batch approval times are significantly reduced.
The multi-step synthesis sequence of a fine chemical is elucidated by applying Hard Modeling Factor Analysis (HMFA) to MIR spectra measured inline. The estimated pure component spectra are validated through mixtures samples prepared in the lab and thus allow to set up the reaction network.
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