431736 Development and Validation of a Reduced-Index Dynamic Model of an Industrial High-Purity Column

Thursday, November 12, 2015: 8:49 AM
Salon E (Salt Lake Marriott Downtown at City Creek)
Jose O. A. Matias1, Misagh Ebrahimpour1, Fabio D.S Liporace2, Ardson dos Santos Vianna Jr.1 and Galo A.C. Le Roux1, (1)Chemical Engineering, University of Sao Paulo, São Paulo, Brazil, (2)Petrobras S.A, Rio de Janeiro, Brazil

This work is part of the development and implementation of a state-of-art Real Time Optimization (RTO) system to an industrial-scale depropenizer column, a vapor recompression distillation unit located at Refinaria de Paulínia, Paulínia, São Paulo, Brazil and owned by Petrobras S.A. Dynamic modeling and simulation validation using real process data of the unit are carried out in an equation-oriented environment, EMSO (Environment for Modeling, Simulation and Optimization)(Soares and Secchi, 2003).

The depropenizer is a high purity distillation column with high nonlinear behavior because of the strong interactions due to the vapor recompression system (Mendoza et al., 2013). Furthermore, the difference between internal and external material/energy flows causes a complex multi-time-scale dynamics (Jogwar and Daoutidis, 2009). Modeling such process is a challenging problem due to these characteristics.

In addition, the modeling of dynamic equilibrium processes often results in higher index DAE systems (Pantelides et al., 1988). Usually, phenomenological relationships are used to solve the index problem, but this approach gives rise to errors as a result of unknown parameters and project details that are assumed. Considering that the column's response to composition changes, in general, takes place over a timescale one or two order of magnitude lower than those of flow rate changes, an approach similar to a proportional loop with arbitrarily large gain is used as an alternative to solve the index problem (Ponton and Gawthrop, 1991). The dynamic model structure is based on the steady state model presented by Mendoza et al. (2013) and contains more than eight thousand equations. An extensive validation with real process data shows that the proposed approach is able to predict the dynamic behavior of the column properly. The application of the model in Moving Horizon Estimation is under implementation.

Jogwar, S. S., & Daoutidis, P. (2009, June). Vapor Recompression Distillation: Multi-Scale dynamics and Control. In American Control Conference, 2009. ACC'09. (pp. 647-652). IEEE.

Mendoza, D. F., Palacio, L. M., Graciano, J. E., Riascos, C. A., Vianna Jr, A. S., & Le Roux, G. C. (2013). Real-Time Optimization of an Industrial-Scale Vapor Recompression Distillation Process. Model Validation and Analysis. In Industrial & Engineering Chemistry Research, 52(16), 5735-5746.

Pantelides, C. C., Gritsis, D., Morison, K. R., & Sargent, R. W. H. (1988). The mathematical modelling of transient systems using differential-algebraic equations. In Computers & chemical engineering, 12(5), 449-454.

Ponton, J. W., & Gawthrop, P. J. (1991). Systematic construction of dynamic models for phase equilibrium processes. In Computers & chemical engineering, 15(12), 803-808.

Rincon, F.D., Le Roux, G.A.C., & Lima, F.V. (2013). Robust Calorimetric Estimation of Semi-Continuous and Batch Emulsion Polymerization Systems with Covariance Estimation. In Macromolecular Reaction Engineering, 8(6), 456-466.

Soares, R. D. P., & Secchi, A. R. (2003). EMSO: A new environment for modelling, simulation and optimisation. In Computer Aided Chemical Engineering, 14, 947-952.

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