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Monitoring specific chemical properties is the key to chemical process control. Today, mainly optical online methods are applied, which require time- and cost-intensive calibration effort. NMR spectroscopy, with its advantage being a direct comparison method without need for calibration, has a high potential for closed-loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and rough environments for process monitoring and advanced process control strategies.
We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (Indirect Hard Modelling – IHM) and applied it to a given pharmaceutical lithiation reaction in the framework of the European Union’s Horizon 2020 project CONSENS. Online low-field NMR (LF NMR) data was analysed by IHM with low calibration effort, compared to a multivariate PLS-R (Partial Least Squares Regression) approach, and both validated using online high-field NMR (HF NMR) spectroscopy.
The Cultivation of “Saccharomyces cerevisiae” for enzyme production was monitored using Near-infrared spectroscopy. An inline NIR optrode was therefore immersed in a 15 L vessel. The calibration was done using a Partial Least Squares (PLS) model with reference measurements of glucose, ammonium, phosphate, ethanol, and optical density. A nonlinear biological process model based on an extended Kalman Filter (EKF) was used to describe the fermentation behavior. It was found that EKF corrects inaccurate PLS predictions.