All Categories (1-3 of 3)

1. Robust nonlinear model predictive control of batch processes

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   Contributor(s):: Nagy, Zoltan K., Braatz, Richard D.

   NMPC explicitly addresses constraints and nonlinearities during the feedback control of batch processes. This NMPC algorithm also explicitly takes parameter uncertainty into account in the state estimation and state feedback controller designs. An extended Kalman filter estimates the process...

2. Regions of attainable particle sizes in continuous and batch crystallization processes

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   Contributor(s):: Vetter, Thomas, Burcham, Christopher L., Doherty, Michael F.

   Process alternatives for continuous crystallization, i.e., cascades of mixed suspension, mixed product removal crystallizers (MSMPRCs) and plug flow crystallizers (PFCs), as well as batch crystallizers are discussed and modeled using population balance equations. The attainable region approach...

3. Control of Batch and Continuous Crystallization Processes using Reinforcement Learning

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   Contributor(s):: Benyahia, Brahim, Anandan, Paul Danny Rielly, Chris Türkay, Metin, Gani, Rafiqul

   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 pharmaceutical regulatory requirements. This work presents novel optimal trajectory tracking control...