An overview of simultaneous strategies for dynamic optimization

Biegler, Lorenz T.

doi:10.1016/j.cep.2006.06.021

Cited by 452 publications

(286 citation statements)

References 62 publications

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“…Even when a solution could be found, an increase around 2-3 orders of magnitude in computation time was required. This is so because very good initial values of the decision variables are normally required for the efficient solution of dynamic optimization problems using the simultaneous approach as described in [13]. In all cases the optimal solutions using either approach were the same with the computational load being the main difference between both solution methods.…”

Section: Mido Solution Strategymentioning

confidence: 81%

“…However, we do not expect additional computational problems because of the unstable nature of the steady-states. This desired feature of our MIDO approach has to do with using the simultaneous approach for solving dynamic optimization problems [13]. Table 6: Simultaneous scheduling and control results for the non-isothermal adiabatic tubular reactor case study.…”

Section: Non-isothermal Adiabatic Tubular Reactor With Recycle and MImentioning

confidence: 99%

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Simultaneous Cyclic Scheduling and Control of Tubular Reactors: Parallel Production Lines

Flores‐Tlacuahuac

Grossmann

2011

Ind. Eng. Chem. Res.

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In this work we propose a simultaneous scheduling and control optimization formulation to address both optimal steady-state production and dynamic product transitions in multiproduct tubular reactors working in several parallel production lines. Because the problem involves integer and continuous variables and the dynamic behavior of the underlying system the resulting optimization problem is cast as a Mixed-Integer Dynamic Optimization (MIDO) problem. Moreover, because spatial and temporal variations are considered when modeling the addressed systems, the dynamic systems give rise to a system of one-dimensional partial differential equations. For solving the MIDO problems we transform them into a mixed-integer nonlinear programs (MINLP). We use the method of lines for spatial discretization, whereas orthogonal collocation on finite elements was used for temporal discretization. The proposed simultaneous scheduling and control formulation is tested using three multiproduct continuous tubular reactors featuring complex nonlinear behavior.2

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Section: Mido Solution Strategymentioning

confidence: 81%

Section: Non-isothermal Adiabatic Tubular Reactor With Recycle and MImentioning

confidence: 99%

Simultaneous Cyclic Scheduling and Control of Tubular Reactors: Parallel Production Lines

Flores‐Tlacuahuac

Grossmann

2011

Ind. Eng. Chem. Res.

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“…From this discretization and the (algorithm specific) parameterization of the differential states results a highly structured NLP that is usually solved by either an interior point or an active set based algorithm. For details on an efficient implementation and further references see, e.g., [24,4].…”

Section: Direct Approach To Optimal Controlmentioning

confidence: 99%

Reformulations and algorithms for the optimization of switching decisions in nonlinear optimal control

Säger

2009

Journal of Process Control

121

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“…To model the pure scheduling optimization problem we use the parallel lines scheduling formulation proposed in [2], whereas for addressing the optimal control calculations we use the simultaneous approach described in [3]. We again propose to solve both problems simultaneously, rather than sequentially, because of the aforementioned reasons.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Scheduling and Control of Multiproduct Continuous Parallel Lines

Flores‐Tlacuahuac

Grossmann

2010

Ind. Eng. Chem. Res.

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In this work we propose a simultaneous scheduling and control optimization formulation to address both optimal steady-state production and dynamic product transitions in multiproduct parallel continuous stirred tank reactors. The simultaneous scheduling and control problem for multiproduct parallel continuous reactors is cast as a Mixed-Integer Dynamic Optimization (MIDO) problem.The reactor dynamic behavior is described by a set of nonlinear ordinary differential equations that are combined with the set of mixed-integer algebraic equations representing the optimal scheduling production model. We claim that the proposed simultaneous scheduling and control approach avoids suboptimal solutions, that are obtained when both problems are solved in a decoupled way. Hence, the proposed optimization strategy can yield improved optimal solutions. The simultaneous approach for addressing the solution of dynamic optimization problems, based on orthogonal collocation on finite elements, is used to transform the set of ordinary differential equations into a Mixed-Integer Nonlinear Programming (MINLP) problem. The proposed simultaneous scheduling and control formulation is tested using three multiproduct continuous stirred tank reactors featuring different nonlinear behavior characteristics.2

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An overview of simultaneous strategies for dynamic optimization

Cited by 452 publications

References 62 publications

Simultaneous Cyclic Scheduling and Control of Tubular Reactors: Parallel Production Lines

Simultaneous Cyclic Scheduling and Control of Tubular Reactors: Parallel Production Lines

Reformulations and algorithms for the optimization of switching decisions in nonlinear optimal control

Simultaneous Scheduling and Control of Multiproduct Continuous Parallel Lines

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