Optimization of grade transitions in polyethylene solution polymerization process under uncertainty

Shi, Jun; Biegler, Lorenz T.; Hamdan, Intan Munirah; Wassick, John M.

doi:10.1016/j.compchemeng.2016.08.002

Cited by 45 publications

(32 citation statements)

References 15 publications

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“…Such uncertainties can be built into the optimization problem so that feasible designs can be determined with superior performance characteristics. Advances in this area include large‐scale algorithms applied to multi‐scenario optimization as well as formulations for back‐off constraints .…”

Section: Discussionmentioning

confidence: 99%

Integrated Dynamic Optimization and Scheduling of Polymerization Processes with First Principle Models

Nie

Biegler

2017

Chemie Ingenieur Technik

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An integrated dynamic optimization strategy is presented for block copolymerization processes. First the reactor model, which has unstable modes and may lead to unbounded profiles under certain design and operating conditions, is derived. Second, optimal recipes for operating of the copolymerization process are determined. Finally, an optimization strategy for the integration of scheduling and dynamic process operation for general continuous/batch processes is considered. The resulting approach leads to significant improvements in productivity, while maintaining safe operation and satisfaction of product specifications.

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Section: Discussionmentioning

confidence: 99%

Integrated Dynamic Optimization and Scheduling of Polymerization Processes with First Principle Models

Nie

Biegler

2017

Chemie Ingenieur Technik

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“…The back-off method described in this section has been adapted from that presented in Shi et al 37 but is expanded in the current work to be applied to the integration of design, control, and scheduling. This section describes the theory of back-off terms, while the calculation and implementation of the back-off terms are discussed in the following section.…”

Section: Back-off Parametersmentioning

confidence: 99%

“…Consider the set of the inequality constraints g in Problem (1). 37 To simplify the present analysis, the effect of uncertainty is assumed to be insensitive to the decision variables, as shown in (2) and (3). Thus, back-off terms b are introduced to each inequality constraint g to approximate the effect of uncertainty, and force the solution to back off from the optimal nominal solution.…”

Section: Back-off Parametersmentioning

confidence: 99%

“…This method does not guarantee an optimal solution when subject to variability, but its simplicity has made it attractive for use in industry. 36,37 Although each of the aforementioned studies makes use of back-off terms in their optimization problems, the algorithms and approximations are significantly different. 25 In our previous work, a critical-set based decomposition algorithm was presented for integration of design, control and scheduling, which considered critical (worst-case) realizations of process disturbance and uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty

2018

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An algorithm that employs the back‐off method to provide optimal solutions for integration of design, control, and scheduling for multiproduct systems is presented, featuring a flexibility and feasibility analysis. The algorithm employs Monte Carlo (MC) sampling to generate a large number of random realizations, and simulate the system to determine feasibility. Back‐off terms are determined and incorporated into a new flexibility analysis to approximate the effect of stochastic uncertainty and disturbances. Through successive iterations, the algorithm converges, terminating on a solution that is robust to a specified level of process variability due to stochastic realizations in the disturbances and uncertain parameters. The proposed algorithm has been successfully applied to a multiproduct continuous stirred tank reactor for which optimal design, control, and scheduling decisions are identified, subject to stochastic uncertainty and disturbance. The present approach has been compared to a critical‐set (multiscenario) method showing the benefits and limitations of both approaches. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2379–2389, 2018

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“…Even with advances in solving complex grade transition problems, few works have considered the impact of uncertainty on the quality of the optimal solutions in such problems. There are at least two sources of uncertainty to be considered in product transition problems: model and production uncertainty.…”

Section: Introductionmentioning

confidence: 99%

A multi‐scenario nonlinear model predictive control approach for robust product transitions

Flores‐Tlacuahuac

Gutiérrez-Limón

2018

Can J Chem Eng

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Dynamic product transitions are ubiquitous operations in the processing industry. When a first‐principles dynamic model is deployed for real system representation, the calculation of the dynamic optimal trajectory for product transition can be cast as an optimal control problem. A common practice in addressing the solution of optimal product transitions lies in the assumption of free of uncertainty first‐principle models. Ignoring the effect of model uncertainty on product transitions can result in unfeasible dynamic trajectories. In this work, an optimization scenario approach, featuring variable scenario weighting functions, is deployed for assessing the impact of model uncertainty on the control actions such that feasible and optimal transition trajectories are computed featuring minimum deviation from target values. The optimization approach was applied to three nonlinear reaction systems. The results demonstrate that when the variable weighting optimization scenario approach is suitable for approximating model uncertainty, feasible transition trajectories can be calculated at relatively low computational cost (for small or medium scale systems).

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Optimization of grade transitions in polyethylene solution polymerization process under uncertainty

Cited by 45 publications

References 15 publications

Integrated Dynamic Optimization and Scheduling of Polymerization Processes with First Principle Models

Integrated Dynamic Optimization and Scheduling of Polymerization Processes with First Principle Models

Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty

A multi‐scenario nonlinear model predictive control approach for robust product transitions

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