Dynamic Parameter Estimation and Optimization for Batch Distillation

Keywords

Dynamic parameter estimation, Nonlinear statistics, Experimental validation, Batch distillation, Dynamic optimization

Abstract

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.

Original Publication Citation

Safdarnejad, Seyed Mostafa, Jonathan R. Gallacher, and John D. Hedengren. "Dynamic parameter estimation and optimization for batch distillation." Computers & Chemical Engineering 86 (2016): 18-32.

Document Type

Peer-Reviewed Article

Publication Date

2016-03-04

Permanent URL

http://hdl.lib.byu.edu/1877/3605

Publisher

Elsevier

Language

English

College

Ira A. Fulton College of Engineering and Technology

Department

Chemical Engineering

University Standing at Time of Publication

Assistant Professor

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