Model-based scale-up of vacuum contact drying of pharmaceutical compounds

Heat and mass transfer based on the low-temperature thermal treatment of hydrocarbons-impacted soil: A numerical simulation and sandbox validation

Thermal treatment can be an effective method for soil remediation, and numerical models play a crucial role in elucidating the underlying processes that affect efficacy. In this study, experiments were conducted to examine the low-temperature thermal treatment for removing n-hexane and n-octane from soil. The results showed that the removal of two alkanes followed the pseudo-first-order kinetics. Additionally, a quantitative relationship between kinetics constant and temperature was established. Based on experimental results, a simple mathematical model was presented via COMSOL Multiphysics 6.0. The processes considered in the model incorporated conductive and convective heat transfer, the vaporization latent heat, and the removal of organic contaminants which was quantified using an advection-dispersion equation combined with a pseudo-first-order kinetic. The developed model was first validated by a thermal treatment in a soil column, demonstrating conformity with the measured temperature and concentration values. Subsequently, the temporal and spatial changes in soil temperature and contaminant levels were evaluated for different heating temperatures. It was found that thermal conduction dominated heat transfer, whereas thermal convection caused by the migration of liquid water intensified when the temperature was higher than the boiling point. The completion time exhibited a correlation with the heating temperature. It was predicted that the time required to achieve a 90% removal efficiency could be shortened from 14 h to 9.5 h by elevating the heating temperature from 80 ℃ to 120 ℃. The study also investigated the impact of the initial water content on heat transfer. It was observed that the saturated soil showed the slowest heating rate and the longest boiling stage.

A Novel Computational Approach Coupled with Machine Learning to Predict the Extent of Agglomeration in Particulate Processes

Solid particle agglomeration is a prevalent phenomenon in various processes across the chemical, food, and pharmaceutical industries. In pharmaceutical manufacturing, agglomeration is both desired in unit operations like wet granulation and undesired in unit operations such as agitated filter drying of highly potent active pharmaceutical ingredients (API). Agglomeration needs to be controlled for optimal physical properties of the API powder. Even after decades of work in the field, there is still very limited understanding of how to quantify, predict, and control the extent of agglomeration, owing to the complex interaction between the solvent and the solid particles and stochasticity imparted by mixing. Furthermore, a large size of industrial scale particulate process systems makes it computationally intractable. To overcome these challenges, we present a novel theory and computational methodology to predict the agglomeration extent by coupling the experimental measurements of agglomeration risk zone or "sticky zone" with discrete element method. The proposed model shows good agreement with experiments. Further, a machine learning model was built to predict agglomeration extent as a function of input variables, such as material properties and processing conditions, in order to build a digital twin of the unit operation. While the focus of the present study is the agglomeration of particles during industrial drying processes, the proposed methodology can be readily applied to numerous other particulate processes where agglomeration is either desired or undesired.

Ultrasound-assisted solution crystallization of fotagliptin benzoate: Process intensification and crystal product optimization

The ultrasound-assisted crystallization process has promising potentials for improving process efficiency and modifying crystalline product properties. In this work, the crystallization process of fotagliptin benzoate methanol solvate (FBMS) was investigated to improve powder properties and downstream desolvation/drying performance. The direct cooling/antisolvent crystallization process was conducted and then optimized with the assistance of ultrasonic irradiation and seeding strategy. Direct cooling/antisolvent crystallization and seeding crystallization processes resulted in needle-like crystals which are undesirable for downstream processing. In contrast, the ultrasound-assisted crystallization process produced rod-like crystals and reduced the crystal size to facilitate the desolvation of FBMS. The metastable zone width (MSZW), induction time, crystal size, morphology, and process yield were studied comprehensively. The results showed that both the seeding and ultrasound-assisted crystallization process (without seeds) can improve the process yield and the ultrasound could effectively reduce the crystal size, narrow the MSZW, and shorten the induction time. Through comparing the drying dynamics of the FBMS, the small rod-shaped crystals with a mean size of 9.6 μm produced by ultrasonic irradiation can be completely desolvated within 20 h, while the desolvation time of long needle crystals with an average size of about 157 μm obtained by direct cooling/antisolvent crystallization and seeding crystallization processes is more than 80 h. Thus the crystal size and morphology were found to be the key factors affecting the desolvation kinetics and the smaller size produced by using ultrasound can benefit the intensification of the drying process. Overall, the ultrasound-assisted crystallization showed a full improvement including crystal properties and process efficiency during the preparation of fotagliptin benzoate desolvated crystals.