Hydroformylation represents an important homogeneous catalyzed process, which is widely used within chemical industry. Usually applied with simple alkenes like Propene and Butene aldehydes obtained from alkenes >C6 are relevant intermediates in production of plasticizers, surfactants and polymers. Today the active catalyst species is often based on valuable Rhodium complexes in aqueous solution. This implies the problem of limited water solubility of the reactands, which is acceptable for short chain lengths, but states a problem in case of higher alkenes. Along with that efficient separation and recycling of the catalyst becomes more complicated. There are different approaches tackling this problem, e.g., by using of salt formation in the BASF process or downstream distillation within the Shell process
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.
Medium resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and process monitoring. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra were treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprised direct integration, automated line fitting, indirect hard modeling, and partial least squares regression.
Medium-resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and processmonitoring. In contrast to high-resolution onlineNMR (HR-NMR),MR-NMRcan be operated under rough environmental conditions. A continuous re-circulating stream of reaction mixture fromthe reaction vessel to the NMR spectrometer enables a non-invasive, volume integrating online analysis of reactants and products. Here, we investigate the esterification of 2,2,2-trifluoroethanol with acetic acid to 2,2,2-trifluoroethyl acetate both by 1H HR-NMR (500MHz) and 1H and 19F MRNMR (43MHz) as amodel system. The parallel online measurement is realised by splitting the flow,which allows the adjustment of quantitative and independent flow rates, both in the HR-NMR probe as well as in the MR-NMR probe, in addition to a fast bypass line back to the reactor. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra are treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprise (i) direct integration, (ii) automated line fitting, (iii) indirect hard modelling (IHM) and (iv) partial least squares regression (PLS-R). To assess the potential of these evaluation strategies for MR-NMR, prediction results are compared with the line fitting data derived from the quantitative HR-NMR spectroscopy. Although, superior results are obtained from both IHM and PLS-R for 1H MR-NMR, especially the latter demands for elaborate data pretreatment, whereas IHM models needed no previous alignment.