Potential of vis-NIR spectroscopy to monitor the silica precipitation reaction.

Controlling production online is an important issue for chemical companies. Visible and near-infrared (NIR) spectroscopy offers a number of important advantages for process monitoring, and has been used since the 1980s. For complex media such as silica precipitation samples, it is interesting to be able to study independently the scattering and absorption effects. From the scattering coefficient it is possible to extract information on the physical structure of the medium. In this work, the physical changes were monitored during a silica precipitation reaction by simple measurement of collimated transmittance NIR spectra. It is shown that it is possible to differentiate samples before and after the gel point, which is a key parameter for monitoring the process. From these NIR spectra the scattering coefficients were simply extracted, allowing a global vision of the physical changes in the medium. Then principal component analysis of the spectra allowed refinement of the understanding of the scattering effects, in combination with particle size monitoring.

Characterisation of heavy oils using near-infrared spectroscopy: optimisation of pre-processing methods and variable selection.

In this study, chemometric predictive models were developed from near infrared (NIR) spectra for the quantitative determination of saturates, aromatics, resins and asphaltens (SARA) in heavy petroleum products. Model optimisation was based on adequate pre-processing and/or variable selection. In addition to classical methods, the potential of a genetic algorithm (GA) optimisation, which allows the co-optimisation of pre-processing methods and variable selection, was evaluated. The prediction results obtained with the different models were compared and decision regarding their statistical significance was taken applying a randomization t-test. Finally, the results obtained for the root mean square errors of prediction (and the corresponding concentration range) expressed in %(w/w), are 1.51 (14.1-99.1) for saturates, 1.59 (0.7-61.1) for aromatics, 0.77 (0-34.5) for resins and 1.26 (0-14.7) for asphaltens. In addition, the usefulness of the proposed optimisation method for global interpretation is shown, in accordance with the known chemical composition of SARA fractions.