This paper reports on the comparison of three modeling approaches that were applied to a fed batch evaporative sugar crystallization process. They are termed white box, black box, and grey box modeling strategies, which reflects the level of physical transparency and understanding of the model. White box models represent the traditional modeling approach, based on modeling by first principles. Black box models rely on recorded process data and knowledge collected during the normal process operation. Among various tools in this group an artificial neural networks (ANN) approach is adopted in this paper. The grey box model is obtained from a combination of first principles modeling, based on mass, energy and population balances, with an ANN to approximate three kinetic parameters ‐‐ crystal growth rate, nucleation rate and the agglomeration kernel. The results have shown that the hybrid modeling approach outperformed the other aforementioned modeling strategies.
This paper describes the use of near infrared spectroscopy as a tool for the determination of moisture and resin content on papers impregnated with melamine-formaldehyde resins for high-pressure laminate production. The papers had different colours and grammages. The near infrared analysis range comprised wavelengths between 12,000 cm À1 and 4000 cm À1. Several multivariate calibration procedures and pre-processing techniques were tested for selection of the best spectral interval, including interval partial least-square, forward interval partial least-square and synergy interval partial leastsquare. The performance of calibration models was evaluated computing the root mean-squared error of cross-validations and the coefficient of determination (R 2). An external validation procedure was done using different decorative papers (red, pearl, yellow, violet and pale green). The performances of the best models were compared using the statistical criterion root mean square error of prediction. It was shown that the developed models can be applied in the determination of resin content independently of the grammage and colour of the papers. However, regarding the volatile content, the models seemed to be affected by external factors, such as the presence of dyes and pigments, and were only applicable to papers having spectra similar to those used in the calibration model.
Quality control of amino resins must be based on reproducible and rapid methods. Fourier-transform near infrared spectroscopy (FT-NIR) has been gaining increasing interest in this context. However, it is not always possible to perform the analysis under the same conditions. Temperature and storage time, in particular, are two factors that often vary. However, their influence on the FT-NIR results is not yet well understood. This work describes how temperature and resin ageing affect the near infrared spectra of amino resins. It is shown that a previously calibrated near infrared model to assess the molar ratio of amino resins has a linear response with temperature. To counter-act this effect and improve the speed of analysis, the spectral pre-processing of extended multiplicative scattering correction was used in conjunction with the loadings of water at different temperatures. This procedure was able to diminish the dependency of the model in relation to temperature for two amino resins (an R2 above 95 % of a linear fit went down to below 1%). With respect to the ageing of amino resins, NIR spectra of two resins were examined for a period of 9 days. It was found that the spectra are influenced by the continuation of the condensation reactions and the formation of aggregates, which causes increase in absorbance with resin ageing. This was proven by checking the differences between NIR spectra of amino resins before and after being subjected to ultrasonic treatment to promote deagglomeration.
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