A Holistic Approach for the Model-Based Control of Crystal Size, Shape and Purity in Integrated Batch and Continuous Crystallization-Wet Milling Systems

Executive Summary

crystallization process.

the digital twin developed can be used for in silico investigation and digital design of the

measurements can be incorporated in the parameter estimation. We also demonstrate how

improvement in the 2D PBM model capability and investigate how different 2D CSD

proprietary active pharmaceutical ingredient. This year we also achieved significant

of a digital twin, based on both 1D and 2D PBM for a model crystallization system of a

size-dependent growth expression and parameter estimation formulation in the development

between parameters and confidence intervals. We demonstrated the benefits of the novel

enables faster convergence of the parameter estimation and decrease in inter-correlation

proposes to use a series of “intelligent” constraints in the optimization. The novel formulation

that has been used traditionally only for qualitative monitoring. The formulation also

formulation proposed incorporates semi-quantitave data from FBRM measurement, a tool

real experimental data to achieve a digital twin development. The novel parameter estimation

numerical solution approach, which is the critical enabling step to connect general models to

A major focus this year was to develop robust parameter estimation formulation and

kinetics and demonstrate the improved prediction ability of models using the new expression.

thermocycles. We also introduce novel formulations for modeling size-dependent growth

an important phenomenon when considering the design of crystallization processes with

agglomeration and breakage mechanisms, which can also be used to model de-agglomeration,

In this report we focus on further development of the generalized PBM model to include

combination of process modeling, high performance process simulation and optimization.

development efforts, which also highlights the achievable improvements enabled by the

(MPC) design as well. This report gives a deeper insight into these interdisciplinary

equations to soft-sensor development, which is required for the model predictive control

model (PBM) development of the system concept, through efficient implementation of model

system design requires an interdisciplinary effort, which ranges from population balance

shape control during crystallization processes. The successful crystallization process and

aim of developing process systems engineering approaches for improved crystal size and

This report summarizes the main achievements during the year 2020 of the project with the