Simultaneous modeling and optimization of nonlinear simulated moving bed chromatography by the prediction-correction...

Area of Science:

• Chemical Engineering
• Chromatography Science
• Process Optimization

Background:

• Simulated Moving Bed (SMB) chromatography is crucial for separations.
• Optimizing nonlinear SMB processes is complex and time-consuming.
• Accurate modeling is essential for efficient SMB operation.

Purpose of the Study:

• To develop a systematic method for simultaneous modeling and optimization of nonlinear SMB chromatography.
• To reduce the time and manual effort required for SMB process optimization.
• To achieve high purity and productivity in SMB separations.

Main Methods:

• A prediction-correction (PC) method was employed.
• The method integrates model-based optimization, SMB startup data, isotherm model selection, and parameter estimation.
• Iterative refinement of model parameters and operating conditions was performed.

Main Results:

• The PC method reliably determined nonlinear isotherm models and parameter values using SMB startup data.
• One nonlinear SMB system was optimized within two operating condition adjustments.
• The refined models were validated using frontal and perturbation analysis.

Conclusions:

• The developed PC method offers an efficient and automated approach for nonlinear SMB chromatography.
• This systematic strategy ensures high purity constraints and optimal productivity.
• The method significantly reduces the need for manual tuning in SMB process optimization.