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

Publication Reference: 
ARR-21-10
Author Last Name: 
Nagy
Authors: 
Zoltan K. Nagy
Report Type: 
ARR
Research Area: 
Systems Engineering
Publication Year: 
2021
Publication Month: 
1

Executive Summary
This report summarizes the main achievements during the year 2020 of the project with the
aim of developing process systems engineering approaches for improved crystal size and
shape control during crystallization processes. The successful crystallization process and
system design requires an interdisciplinary effort, which ranges from population balance
model (PBM) development of the system concept, through efficient implementation of model
equations to soft-sensor development, which is required for the model predictive control
(MPC) design as well. This report gives a deeper insight into these interdisciplinary
development efforts, which also highlights the achievable improvements enabled by the
combination of process modeling, high performance process simulation and optimization.
In this report we focus on further development of the generalized PBM model to include
agglomeration and breakage mechanisms, which can also be used to model de-agglomeration,
an important phenomenon when considering the design of crystallization processes with
thermocycles. We also introduce novel formulations for modeling size-dependent growth
kinetics and demonstrate the improved prediction ability of models using the new expression.
A major focus this year was to develop robust parameter estimation formulation and
numerical solution approach, which is the critical enabling step to connect general models to
real experimental data to achieve a digital twin development. The novel parameter estimation
formulation proposed incorporates semi-quantitave data from FBRM measurement, a tool
that has been used traditionally only for qualitative monitoring. The formulation also
proposes to use a series of “intelligent” constraints in the optimization. The novel formulation
enables faster convergence of the parameter estimation and decrease in inter-correlation
between parameters and confidence intervals. We demonstrated the benefits of the novel
size-dependent growth expression and parameter estimation formulation in the development
of a digital twin, based on both 1D and 2D PBM for a model crystallization system of a
proprietary active pharmaceutical ingredient. This year we also achieved significant
improvement in the 2D PBM model capability and investigate how different 2D CSD
measurements can be incorporated in the parameter estimation. We also demonstrate how
the digital twin developed can be used for in silico investigation and digital design of the
crystallization process.