Dynamic parameter estimation and optimization for batch distillation |
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| Affiliation: | 1. Department of Chemical Engineering, 350 CB, Brigham Young University, Provo, UT 84602, USA;2. Department of Chemical Engineering, University of Utah, 50 S. Central Campus Dr., Rm 3290 MEB, Salt Lake City, UT 84112-9203, USA;1. The University of Utah, Department of Chemical Engineering, 50 S Central Campus Drive, Salt Lake City, UT, 84112, United States;2. Idaho National Laboratory, United States;3. National Renewable Energy Laboratory, United States;4. NXP Semiconductors, United States;5. ExxonMobil, United States;6. Brigham Young University, Department of Chemical Engineering, United States;7. The University of Texas at Austin, Department of Chemical Engineering, United States;1. Departamento de Ingenieria Quimica, Universidad de Guanajuato, 36050 Guanajuato, Guanajuato, Mexico;2. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, USA |
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| 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. |
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| Keywords: | Dynamic parameter estimation Nonlinear statistics Experimental validation Batch distillation Dynamic optimization |
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