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

Flores‐Tlacuahuac, Antonio; Grossmann, Ignacio E.

doi:10.1021/ie1008629

Cited by 21 publications

(20 citation statements)

References 28 publications

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“…Of course, the same formulation may also be used for disturbance rejection and/or tracking problems. We should remark that in previous works [9], [10], [11] Mathematically the NLMPC problem can posed as follows:…”

Section: Nonlinear Model Predictive Control Constraintsmentioning

confidence: 99%

MINLP Formulation for Simultaneous Planning, Scheduling, and Control of Short-Period Single-Unit Processing Systems

Gutiérrez-Limón

Flores‐Tlacuahuac

Grossmann

2014

Ind. Eng. Chem. Res.

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A simultaneous strategy for solving the integrated planning, scheduling and control problem considering short-term periods is proposed in this paper. The problem is formulated as a mixed integer dynamic optimization problem. The main practical motivation to use a simultaneous, rather than a sequential solution strategy is to seek improved optimal solutions by considering the interactions among planning, scheduling and control. Integration of the three levels of the problem represents a challenge given the very different horizon times involved in the operations, resulting in growth of the problem in terms of the number of equations to be solved, and hence on computational requirements to carry it out. A full space approach was used rather than trying a decomposition strategy. Moreover, a nonlinear model predictive control strategy was also used to account for on-line product transition trajectories. The integrated planning, scheduling and control is formulated as a mixed integer dynamic optimization model, which is tested using the dynamic models of three continuous stirred tank reactors featuring different nonlinear behavior.

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Section: Nonlinear Model Predictive Control Constraintsmentioning

confidence: 99%

MINLP Formulation for Simultaneous Planning, Scheduling, and Control of Short-Period Single-Unit Processing Systems

Gutiérrez-Limón

Flores‐Tlacuahuac

Grossmann

2014

Ind. Eng. Chem. Res.

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“…The shipment is quantified by S it . Equation (12) shows that it AREA should be the product of inventory level and time duration of one period.…”

Section: Inventorymentioning

confidence: 99%

“…As a consequence, the transition times and the transition costs were assumed to be known parameters. The integration of scheduling and dynamic optimization considers variable transition times and transition costs, which are determined by the time-dependent trajectories [9][10][11][12][13]. The integrated problem is often formulated into a mixed-integer dynamic optimization (MIDO) problem [14], which can be then transformed into a H. Shi, Y. Chu, and F. You mixed-integer nonlinear programming (MINLP) problem by discretizing the differential equations [9,15].…”

Section: Introductionmentioning

confidence: 99%

Integrated planning, scheduling, and dynamic optimization for continuous processes

Shi

Chu

You

2014

53rd IEEE Conference on Decision and Control

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Integration of planning, scheduling, and dynamic optimization significantly improves the overall performance of a production process, compared to the traditional sequential method that solves each sub-problem one by one. The integrated model can be formulated as a mixed-integer dynamic optimization (MIDO) problem which can be then transformed into a mixed-integer nonlinear program (MINLP). However, widely-used simultaneous methods, which solve the integrated problem by a general-purpose MINLP solver, encounter computational complexity. They are difficult to apply to large-scale problems. To address this difficulty, we propose a novel efficient method to solve the integrated problem for a multi-product reactor. The method decomposes the dynamic optimization problems from the planning and scheduling problem by discretizing transition times and transition costs. Then the integrated problem is transformed into a mixed-integer linear program, which is much easier to solve than the large-scale MINLP. In the case studies, the proposed method can reduce the computational time by more than three orders of magnitudes in comparison with the simultaneous method.

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“…[1][2][3][4] As two major decision-making layers in the production hierarchy, integration of scheduling and control has attracted significant research efforts in recent years. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] Compared with the traditional method where the scheduling problem and the control problem are solved sequentially, the integrated method can optimize the overall performance of the production process by making a better coordination between the subsystems.…”

Section: Introductionmentioning

confidence: 99%

Moving horizon approach of integrating scheduling and control for sequential batch processes

Chu

You

2014

AIChE Journal

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Online integration of scheduling and control is crucial to cope with process uncertainties. We propose a new online integrated method for sequential batch processes, where the integrated problem is solved to determine controller references rather than process inputs. Under a two-level feedback loop structure, the integrated problem is solved in a frequency lower than that of the control loops. To achieve the goal of computational efficiency and rescheduling stability, a moving horizon approach is developed. A reduced integrated problem in a resolving horizon is formulated, which can be solved efficiently online. Solving the reduced problem only changes a small part of the initial solution, guaranteeing rescheduling stability. The integrated method is demonstrated in a simulated case study. Under uncertainties of the control system disruption and the processing unit breakdown, the integrated method prevents a large loss in the production profit compared with the simple shifted rescheduling solution.

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

Cited by 21 publications

References 28 publications

MINLP Formulation for Simultaneous Planning, Scheduling, and Control of Short-Period Single-Unit Processing Systems

MINLP Formulation for Simultaneous Planning, Scheduling, and Control of Short-Period Single-Unit Processing Systems

Integrated planning, scheduling, and dynamic optimization for continuous processes

Moving horizon approach of integrating scheduling and control for sequential batch processes

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