Hannu Alatalo

Purification by crystallization from solutions of various viscosities

The crystallization of potassium sulfate has been studied from aqueous solutions of different viscosities. The model for purification by crystallization was based on boundary layer theory. Boundary layer theory can be used to determine the impurity concentration at the crystalline surface. The model explained well the impurity results that were obtained experimentally. The linear crystal growth rates were calculated from the measured crystal size distributions. The obtained results showed that the impurity level was higher with the high viscosity solution than that with a solution of low viscosity. On the other hand, the crystal growth rate was higher in the low viscosity solution than that in the solution of high viscosity.

Evaluation of variation in dynamic processes via online spectrometers

Theory of sampling offers powerful tools for process optimization. An adequate sampling interval can be determined for spectral measurements when utilizing a multivariate extension of variography by applying score vectors as independent sources of uncertainty. The traditional way is to apply variographic analyses into single process variables independently. In the multivariate extension, those process variables are replaced with score vectors of principal component analysis. The combined uncertainty found this way depends not only on the variance in the spectra, but also, for example, on the number of utilized score vectors and the preprocessing method. This approach is illustrated with a crystallization process continuously followed with an attenuated total reflectance Fourier transform infrared instrument. The results show that the approach is highly applicable but should only be utilized as an indicative tool. Copyright

On the effectiveness of cross-fitting in multi-block PLS (CF-MBPLS)

Multi‐block PLS is an extension of partial least squares or projection to latent structures (PLS), where the descriptor matrix is divided into meaningful blocks based on either process units or type of data. A typical application is using process variables as one block and spectral data on another block. It has been utilized in obtaining more information of processes and the effect of different types of variables. In comparison with priority or hierarchical PLS, in multi‐block PLS, there is no need to prioritize blocks in advance because they are iteratively calculated at the same time. With multi‐block PLS, however, it is easy to overfit data resulting in a poor predictive ability. A recent development called cross‐fitting has been reported to alleviate the problem of overfitting in PLS. This approach was adjusted to multi‐block PLS and is tested on two different data sets, where overfitting and sensitivity to outliers are issues. Copyright