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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.
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.).
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.
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.
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