Application of Nonlinear Model Predictive Control to Optimal Batch Distillation

Bosley, James R.; Edgar, Thomas F.

doi:10.1016/s1474-6670(17)51009-5

Cited by 10 publications

(9 citation statements)

References 6 publications

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“…They proposed and compared different methods for estimating on-line the distillate composition, and found that an extended Luenberger observer provided the best overall performance. Bosley and Edgar 14 considered the issue of implementing on-line the optimal operational trajectory determined a priori by optimizing off-line the performance of a batch column; they proved that nonlinear model predictive control is very effective in tracking the optimal performance. Finefrock et al 3 studied the problem of composition control in a binary ethanol/water batch column.…”

Section: Summary Of Previous Findingsmentioning

confidence: 99%

“…The process dynamic equations are reported in Appendix 1. Unlike other models used for similar studies, 12,14 where L m,0 is the reference value of the internal liquid flow rate, H m and H 0 are the actual and reference molar holdups on tray m, and τ is the tray hydraulic time constant. The energy balances are not included in the model; therefore, the vapor rate is constant inside the column.…”

Section: The Processmentioning

confidence: 99%

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Composition Control in Batch Distillation: Binary and Multicomponent Mixtures

Barolo

Berto

1998

Ind. Eng. Chem. Res.

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A strategy is proposed for the control of constant-distillate-composition operations of batch distillation columns. The proposed control law is derived in the framework of Nonlinear Internal Model Control. The distillate composition is estimated from selected tray temperature measurements by an observer. The same observer provides the estimate of the system states that are necessary for the computation of the control algorithm. Results are provided for binary and multicomponent separations of ideal and nonideal systems. Several sources of process/model mismatch are considered in order to provide a realistic test scenario for the proposed procedure. It is shown that the proposed controller ensures fast and smooth transition from the startup phase to the production phase, and tight composition control until the end of the operation. Moreover, the tuning of the controller is performed very easily. Finally, the on-line computation of the control law is straightforward, which is a promising result toward practical implementation of the proposed method.

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Section: Summary Of Previous Findingsmentioning

confidence: 99%

Section: The Processmentioning

confidence: 99%

Composition Control in Batch Distillation: Binary and Multicomponent Mixtures

Barolo

Berto

1998

Ind. Eng. Chem. Res.

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“…Often, if there is a deviation in product quality, the charge has to be discarded. Hence, shrinking-horizon nonlinear model predictive control (sh-NMPC) has been proposed as a successful platform for the optimal operation of batch processes, with the prediction horizon always running to the final batch time [13,14,26,27]. Seki et al [28] suggested an NMPC scheme for industrial polymerization reactors.…”

Section: Introductionmentioning

confidence: 99%

Computationally efficient NMPC for batch and semi-batch processes using parsimonious input parameterization

Aydın

Bonvin

Sundmacher

2018

Journal of Process Control

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The trend towards high-quality, low-volume chemical production has put more emphasis on batch and semi-batch processing due to its increased operational flexibility. The transient behavior of these processes makes their real-time optimization very challenging. In particular, the large prediction horizons required in shrinking-horizon NMPC increase the real-time computational effort due to expensive matrix factorizations. The computational delay associated with advanced control methods is usually underestimated in theoretical studies. However, this delay may contribute to suboptimal or, worse, infeasible operation in real-life applications. This study proposes to combine a tailored parsimonious input parameterization with shrinking-horizon NMPC to reduce the real-time computational effort. Models of the optimal solution are used to suggest parsimonious parameterizations (especially for sensitivity-seeking arcs) that lead to computationally efficient optimization. The proposed approach is illustrated in simulation on two case studies in the presence of uncertainty, namely a batch binary distillation column and a semi-batch reactor for the hydroformylation of 1-dodecene. The results show that the tailored parsimonious shrinking-horizon NMPC (i) performs very similarly to the standard shrinking-horizon NMPC in terms of cost, (ii) is computationally much more efficient than the standard shrinking-horizon NMPC especially at the beginning of the batch, (iii) is robust to plant-model mismatch.

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“…Quintero‐Marmol et al (1991) proposed an inferential control scheme where composition is estimated through an extended Luenberger observer. Fileti and Rocha‐Pereira (1997) (see also Bosley and Edgar, 1993) used gain‐scheduled PI control for binary batch distillation. In their approach, the controller gain is increased during operation in order to keep maintaining the control goal.…”

Section: Introductionmentioning

confidence: 99%

Backstepping‐based cascade control scheme for batch distillation columns

Monroy-Loperena

Alvarez-Ramı́rez

2004

AIChE Journal

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Published online in Wiley InterScience (www.interscience.wiley.com).Nonstationary dynamics, finite-time operation, large thermodynamical uncertainty and delayed composition measurements make the control of batch distillation processes a challenging and interesting problem. In this paper, a cascade control design to regulate the overhead composition of a batch distillation column is presented. The controller is designed within the framework of robust nonlinear control with modeling error compensation techniques in conjunction with a backstepping approach. The result is a cascade controller with a master loop that, driven by the composition regulation error, produces a time-varying set point for the temperature in a certain tray; and with a secondary controller that manipulates the internal reflux ratio to track the time-varying set point determined by the master composition loop. How to extend the controller design to have multiple slave temperature controllers, to improve the regulation of the overhead composition in a batch distillation column, is also presented. The proposed control approach is illustrated by numerical simulations on a full dynamical model.

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Application of Nonlinear Model Predictive Control to Optimal Batch Distillation

Cited by 10 publications

References 6 publications

Composition Control in Batch Distillation: Binary and Multicomponent Mixtures

Composition Control in Batch Distillation: Binary and Multicomponent Mixtures

Computationally efficient NMPC for batch and semi-batch processes using parsimonious input parameterization

Backstepping‐based cascade control scheme for batch distillation columns

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