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Prof. Richard D Braatz

Edwin R Gilliland Professor of Chemical Engineering

Associate Faculty Director, Center for Biomedical Innovation (CBI)

Primary DLC

Department of Chemical Engineering

MIT Room: E19-551

(617) 253-3112

braatz@mit.edu

https://cheme.mit.edu/profile/richard-d-braatz/

Assistant

Angelique Scarpa

(617) 258-0431

ascarpa@mit.edu

Areas of Interest and Expertise

Process Systems and Control
Pharmaceutical Crystallization
Multiscale Systems Engineering
Applied Mathematics
Biopharmaceutical Manufacturing
Biopharmaceutical Manufacturing Systems Controls

Research Summary

Research is in applied mathematics and control theory and its application to manufacturing systems where the control of events at the molecular scale is key to product quality. Some specific applications include:

(*) Optimal design of pharmaceutical crystallizers that utilize process intensification to manufacture crystals of precisely controlled size and molecular structure;

(*) Multiscale modeling and design of nano- and microstructured polymeric materials that respond to internal or external stimuli to spatially control the release of macromolecules;

(*) Control of molecular systems and nanodevices, from molecular clustering on surfaces to carbon nanotube-based nanobiosensors.

(summary updated 11/2011)

Projects

September 12, 2023

Department of Chemical Engineering

Center for Battery Sustainability (CBS)

Principal Investigators Martin Bazant , Richard Braatz

September 7, 2011

Department of Chemical Engineering

Braatz Group

Principal Investigator Richard Braatz

Video

07.16.24-TokyoLifeScienceSymposium_ADigitalTwinforContinuousmRNAManufacturingy_RichardBraatz

July 16, 2024Conference Video Duration: 41:44

This presentation describes a digital twin that is being developed for end-to-end continuous manufacturing of mRNA biotherapeutics. Mechanistic models are being constructed for all unit operations. These dynamic models are integrated with models for constraints, uncertainties, and disturbances to form a digital twin for automated, integrated continuous manufacturing. The digital twin is suitable for (1) evaluation and validation of mechanistic hypotheses to gain mechanistic understanding, (2) comparison of multiple process flowsheet options, (3) optimization of individual unit operations and their control systems, (4) the design of end-to-end operations, and (5) the real-time operation alongside plant operations. Experimentally validated results are presented for multiple unit operations.

07.16.24-TokyoLifeScienceSymposium_ConnectingwiththeMITInnovationEcosystemPanel Discussion

July 16, 2024Conference Video Duration: 58:0

11.15-16.23-RD-Braatz

November 16, 2023Conference Video Duration: 36:48

4.12.22-Health-Science-Braatz-Nguyen

April 12, 2022Conference Video Duration: 26:23

3.4.21-Future-Manufacturing-Roundtable

March 4, 2021Conference Video Duration: 75:8

Brian Anthony
Associate Director, MIT.nano
Faculty Lead, Industry Immersion Program in Mechanical Engineering
David E. Hardt
Ralph and Eloise Cross Professor, Mechanical Engineering
Professor, Engineering Systems
Richard Braatz
Gilliland Professor, Chemical Engineering
Faculty Research Officer
Katrin Ellen Daehn
Postdoctoral Associate, Department of Materials Science and Engineering
Craig R Karasack
Technical Director of Ergonomics and Manufacturing Technology
Risk Control Services, Liberty Mutual Insurance
Kurt Bettenhausen
Member of the Board for New Technologies and Development, HARTING Technology Group

2021-Future-Manufacturing-Richard-Braatz

March 2, 2021Conference Video Duration: 10:25

AI in LIfe Science 2018 - Richard Braatz

December 4, 2018Conference Video Duration: 28:22

Robust Data Analytics in Biopharmaceutical Manufacturing

Although process data analytics is a valuable tool for improving the manufacturing of biologic drugs, selection of the best method requires a substantial level of expertise. This talk describes a robust and automated approach for process data analytics tool selection that allows the user to focus on goals rather than methods. The approach first applies tools to automatically interrogate the data to ascertain its characteristics, e.g., nonlinearity, correlation, dynamics. This information is then used to select a best-in-class process data analytics tool. The approach is demonstrated for industrial data for the manufacturing of a monoclonal antibody.