Dynamic parameter estimation and optimization for batch distillation

• Many batch columns remain unoptimized despite possible cost and energy savings.
• Batch distillation case study with parameter ranking and statistical analysis.
• Nonlinear statistical analysis with approximate multivariate confidence regions.
• Dynamic estimation and optimization improves experimental production by 14%.
• Comparison of simplified and detailed models for parameter estimation.

This work reviews a well-known methodology for batch distillation modeling, estimation, and optimization but adds a new case study with experimental validation. Use of nonlinear statistics and a sensitivity analysis provides valuable insight for model validation and optimization verification for batch columns. The application is a simple, batch column with a binary methanol–ethanol mixture. Dynamic parameter estimation with an ℓ1-norm error, nonlinear confidence intervals, ranking of observable parameters, and efficient sensitivity analysis are used to refine the model and find the best parameter estimates for dynamic optimization implementation. The statistical and sensitivity analyses indicated there are only a subset of parameters that are observable. For the batch column, the optimized production rate increases by 14% while maintaining product purity requirements.