(238d) Comparison of Distillation Strategies, Optimization and Scale-up for an Industrial Process Based on Mechanistic Modelling

API’s production/chemistry processes commonly include distillation operations for several purposes and in different stages and steps of the process. Despite not being considered as a critical or problematic operation, their study and mechanistic modelling can bring real gains to the process, including optimization of process conditions for different scales to save time and solvent volumes and reducing the number of lab experiments required.

Unlike the empirical or statistical models, the mechanistic model of a distillation considers and relates the mathematical descriptions of the chemical and physical processes. Thus, the model provides accurate answers to important questions raised when developing and scaling-up a distillation and allows a deeper knowledge of the process.

To increase the confidence level of the results provided by the mechanistic approach, we demonstrate our methodology in terms of equipment characterization, fluid properties calculation along the distillation and simulation of different distillation strategies (e.g. continuous feed distillation vs put-and-take) while developing our models using DynoChem. Distillation data gathered from previous experimental runs was used to validate the mechanistic model by direct comparison of key process variables.

We demonstrate the application of this methodology by showing case studies with different objectives for industrial projects. In one of the examples, we present the comparison between different distillation strategies for specific solvent swap in the process where we pretend to achieve a target final composition while reducing the volumes of fresh solvent needed without impact the process outcome. A second application is focused on the scale-up from Laboratorial to Pilot Scale and optimizing the distillation conditions to reduce operation time through simulations.