Multi-agent reinforcement learning and RL-based adaptive PID control of crystallization processes
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 …
namely a multi-agent RL and RL-based adaptive PID control. An off-policy deep …
Control of batch and continuous crystallization processes using reinforcement learning
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 …
crucial to achieve the targeted critical quality attributes of the final drug product and meet the …
Robust model-based reinforcement learning control of a batch crystallization process
B Benyahia, PD Anandan… - 2021 9th International …, 2021 - ieeexplore.ieee.org
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 …
challenging due to the complexity of the underlying phenomena. The main objective is to …
Optimal control policies of a crystallization process using inverse reinforcement learning
Crystallization is widely used in the pharmaceutical industry to purify reaction intermediates
and final active pharmaceutical ingredients. This work presents a novel implementation of …
and final active pharmaceutical ingredients. This work presents a novel implementation of …
[PDF][PDF] Optimal strategies to control particle size and variance in antisolvent crystallization operations using deep rl
Solution crystallization operations have complex dynamics that are typically lumped into two
competing processes namely nucleation and growth. Mathematical models can be used to …
competing processes namely nucleation and growth. Mathematical models can be used to …
[HTML][HTML] Physics-informed machine learning for MPC: Application to a batch crystallization process
This work presents a framework for developing physics-informed recurrent neural network
(PIRNN) models and PIRNN-based predictive control schemes for batch crystallization …
(PIRNN) models and PIRNN-based predictive control schemes for batch crystallization …
Learning to navigate a crystallization model with deep reinforcement learning
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 …
sensor and a reinforcement learning (RL) framework that speeds up the control loop is …
Machine learning modeling and predictive control of the batch crystallization process
This work develops a framework for building machine learning models and machine-
learning-based predictive control schemes for batch crystallization processes. We consider …
learning-based predictive control schemes for batch crystallization processes. We consider …
JITL-based concentration control for semi-batch pH-shift reactive crystallization of L-glutamic acid
Although concentration control (C-control) strategy has been shown to give effective and
robust control performance for batch cooling and semi-batch antisolvent crystallizations in …
robust control performance for batch cooling and semi-batch antisolvent crystallizations in …
Machine Learning-Based MPC of Batch Crystallization Process Using Physics-Informed RNNs
G Wu, Z Wu - IFAC-PapersOnLine, 2023 - Elsevier
This work presents a framework for developing physics-informed recurrent neural network
(PIRNN) models and PIRNN-based predictive control schemes for batch crystallization …
(PIRNN) models and PIRNN-based predictive control schemes for batch crystallization …
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