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A hybrid model framework for the optimization of preparative chromatographic processes
Deepak Nagrath1, Achille Messac, B Wayne Bequette
1Howard P. Isermann Department of Chemical Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180-3590, USA.
Biotechnology Progress
|February 7, 2004
Summary
This study introduces a hybrid modeling approach for preparative chromatography optimization. This method accelerates simulations and design exploration for improved protein separation processes.
Area of Science:
- Chemical Engineering
- Biotechnology
- Analytical Chemistry
Background:
- Preparative chromatography is crucial for purifying biomolecules.
- Optimization of these processes is computationally intensive.
- Existing models may not efficiently handle complex systems.
Purpose of the Study:
- To develop an efficient optimization framework for preparative chromatography.
- To reduce computational time for process simulation and design.
- To enable rapid exploration of various design scenarios.
Main Methods:
- Experimental determination of physical model parameters (general rate model, steric mass action isotherm).
- Development of neural-network-based empirical models using physical model simulation data.
- Optimization using sequential quadratic programming to maximize production rate times yield.
Main Results:
- Hybrid models significantly reduce computational time for simulation and optimization.
- The framework allows for multivariable optimization.
- Rapid exploration of different design scenarios is enabled.
Conclusions:
- Hybrid empirical models offer an efficient approach for complex preparative chromatography.
- This method enhances process optimization and design flexibility.
- The framework is applicable to binary and tertiary model protein systems.