Detection of Nucleation during Cooling Crystallization through Moving Window PCA Applied to in Situ Infrared Data

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

Solubility and Crystallization of Piroxicam from Different Solvents in Evaporative and Cooling Crystallization

In this work, the solubility of a non-steroidal anti-inflammatory drug (NSAID), piroxicam, is investigated. The polymorphic form II, which is the most stable form at room temperature, was investigated in seven different solvents with various polarities. It has been found that the solubility of piroxicam in the solvents is in the following order: chloroform > dichloromethane > acetone > ethyl acetate > acetonitrile > acetic acid > methanol > hexane. Crystallization of piroxicam from different solvents has been performed with evaporative crystallization and cooling crystallization; the effects of solvent evaporation rate and solute concentration have also been studied. Both form I and form II could be produced in cooling and evaporative crystallization, and no simple link can be identified between the operating parameters and the polymorphic outcome. Results obtained in the present work showed the stochastic nature of the nucleation of different polymorphs as well as the complexity of the crystallization of a polymorphic system.

Fault detection with moving window PCA using NIRS spectra for monitoring the anaerobic digestion process

Principal component analysis (PCA) is a popular method for process monitoring. However, most processes are time-varying, thus older samples are not representative of the current process status. This led to the introduction of adaptive-PCA based monitoring, such as moving window PCA (MWPCA). In this study, near-infrared spectroscopy (NIRS) responses to digester failures were evaluated to develop a spectral data processing tool. Tests were performed with a spectroscopic probe (350-2,500 nm), using a 35 L mesophilic continuously stirred tank reactor. Co-digestion experiments were performed with pig slurry mixed with several co-substrates. Different stresses were induced by abruptly increasing the organic load rate, changing the feedstock or stopping the stirring. Physicochemical parameters as well as NIRS spectra were acquired for lipid, organic and protein overloads experiments. MWPCA was then applied to the collected spectra for a multivariate statistical process control. MWPCA outputs, Hotelling T2 and residuals Q statistics showed that most of the induced dysfunctions can be detected with variations in these statistics according to a defined criterion based on spectroscopic principles and the process. MWPCA appears to be a multivariate statistical method that could help in decision support in industrial biogas plants.