(699e) At-Line Monitoring of Diphenhydramine Synthesis Via Low-Field NMR Spectroscopy As Process Analytical Technology | AIChE

(699e) At-Line Monitoring of Diphenhydramine Synthesis Via Low-Field NMR Spectroscopy As Process Analytical Technology

Authors 

Tsilomelekis, G., Rutgers University
Singh, R., Rutgers, The State University of New Jer
Muzzio, F., Rutgers, The State University of New Jersey
The benefits of continuous manufacturing include greener and safer processes, better control over reaction conditions, and smaller, more agile systems.[1] Of particular focus is the rational implementation of process analytical technologies (PAT) to monitor and control the process. Online PAT enables the simultaneous monitoring of the system while providing a) robust response to changes and, b) capability to extract fast process information.

The production of diphenhydramine, the most used antihistamine, primarily follows one of two batch processes in industry.[2] Therefore, it is an excellent target for process intensification via continuous flow synthesis.[3] It has been chosen as a model reaction in this work for showcasing the application of PAT to determine the kinetics of the synthesis reaction network, which has not been shown in the literature in the scenario of a nonpolar solvent. In this work, we evaluate low-field flow NMR for PAT applications and was employed in-line with a microfluidic system.

Simulated reaction mixtures have yielded calibration NMR spectra. Figure 1a shows the spectra and calibration curve for chlorodiphenylmethane (DPC). The methane bridge hydrogen is chemically distinct allowing for further analysis and modelling. The DPC simple calibration model yielded an R2 of 0.91 and for the DPH model 0.97. Efforts on PLS modelling are placed for the development of a more robust predictive model.

The synthesis of DPH has been evaluated at various temperatures. We also conduct experiments varying initial concentrations of reaction mixtures to unravel the apparent order of the reaction which in turn is used for developing a kinetic model. Using the concentration predictions, we successfully estimate rate constants.

References

  1. Burcham, C.L., Florence, A.J., and Johnson, M.D. Annu. Rev. Chem. Biomol. Eng. 9,253 (2018)
  2. Snead, D.R. and Jamison, T.F. Chem. Sci. 4, 2822 (2013)
  3. Loren, B.P., et. al., R.G. Chem. Sci. 8, 4363 (2017)