In crystallization processes, the control of particle size distribution, shape and purity are
crucial to achieve the targeted critical quality attributes of the final drug product and meet the …

The development of robust and effective control strategies for crystallization processes is
challenging due to the complexity of the underlying phenomena. The main objective is to …

Crystallization is widely used in the pharmaceutical industry to purify reaction intermediates
and final active pharmaceutical ingredients. This work presents a novel implementation of …

In this work, two model-based reinforcement learning (RL) control strategies are investigated
namely a multi-agent RL and RL-based adaptive PID control. An off-policy deep …

In this contribution, a data-driven control approach was developed and applied
experimentally to a pharmaceutical batch cooling crystallization process. In this approach, a …

Solution crystallization operations have complex dynamics that are typically lumped into two
competing processes namely nucleation and growth. Mathematical models can be used to …

Abstract The paper presents a novel batch-to-batch control approach for crystallization
processes, which can be used for designing the shape of the crystal size distribution (CSD) …

This work develops a framework for building machine learning models and machine-
learning-based predictive control schemes for batch crystallization processes. We consider …

This work develops a framework for building machine learning models and machine
learning-based predictive control schemes for batch crystallization processes. We consider …

In this work, a combination of a Convolutional Neural Network (CNN) based measurement
sensor and a reinforcement learning (RL) framework that speeds up the control loop is …