Control-aware batch process scheduling

Santander, Omar; Bâldea, Michael

doi:10.1016/j.compchemeng.2021.107360

Cited by 13 publications

(10 citation statements)

References 44 publications

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“…For instance, both are “real-time” decision-making layers, as they operate in the lower time scales (vs the other layers) and need to quickly adapt to sudden changes; both must deal with uncertainties on their models; and intraphase information, such as operation times, product yields, and material consumption can be accurately used to make predictions from one layer to the other in a feedback structure. Methodologies to deal with the integration of scheduling and control have been developed to deal with the full resolution of the MINLP formulation − or by using iterative decomposition algorithms, ,− general benders decomposition frameworks, , stochastic programming, , model predictive control, − scale-bridging models, data-driven models, , and switched system formulations . The modeling of the scheduling problem may rely on discrete-time formulations, ,,, continuous-time formulations, ,− ,− , or cyclic scheduling. ,,,,, Caspari et al .…”

Section: Introductionmentioning

confidence: 99%

“…compared the performance of top-down and bottom-up approaches. Few studies considered the presence disturbances , or uncertainty. , Chu and You considered parametric uncertainty in the kinetics and a scheduling with varying processing times. Simkoff and Baldea, developed a data-driven methodology that accounts for uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…11,26,27,30,38,43 Caspari et al 44 compared the performance of top-down and bottom-up approaches. Few studies considered the presence disturbances 34,43 or uncertainty. 33,34 Chu and You 33 considered parametric uncertainty in the kinetics and a scheduling with varying processing times.…”

Section: Introductionmentioning

confidence: 99%

“…Simkoff and Baldea, 42 developed a data-driven methodology that accounts for uncertainty. Santander and Baldea 34 considered bounded disturbances and uncertainties when developing a stochastic programming scheduling formulation with processing time relationships from historical data.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Integration of Scheduling and Control for Chemical Batch Plants under Stochastic Uncertainty: A Back-Off Approach

Vera

Ricardez‐Sandoval

2022

Ind. Eng. Chem. Res.

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A new back-off methodology is presented to address mixed integer dynamic optimization (MIDO) formulations that arise from modeling the integration of scheduling and control of flow-shop batch plants under stochastic parametric uncertainty. The core idea of the methodology is to generate optimal scheduling and control decisions combined with optimal unit operation times that offer dynamic feasibility in the presence of stochastic parametric uncertainty using a back-off strategy. Back-off terms are introduced in the model constraints to represent the variability of the system caused by the stochastic uncertainty. The proposed framework decomposes the MIDO problem into a parametric sensitivity analysis, a scheduling problem, a dynamic optimization problem, and a set of dynamic feasibility problems (dynamic feasibility test) that are solved in an iterative fashion until convergence. A key novelty in this work is the addition of a parametric sensitivity analysis that generates correlations included in the scheduling problem to account for process dynamics. A case study featuring a flow-shop batch plant consisting of two dynamic reaction processes and two steady-state separation processes is used to test the performance of the proposed back-off methodology. The results show that unit operation times chosen from optimization are better suited to accommodate stochastic parametric uncertainty, while the control actions enforce process operational and product quality constraints at reasonable economic costs. Hence, the method proposed in this work has the potential of addressing optimal scheduling and control problems under stochastic realizations in flow-shop batch plants.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integration of Scheduling and Control for Chemical Batch Plants under Stochastic Uncertainty: A Back-Off Approach

Vera

Ricardez‐Sandoval

2022

Ind. Eng. Chem. Res.

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“…In a different vein, Valdez-Navarro and Ricardez-Sandoval proposed a back-off decomposition algorithm for scheduling and control under uncertainty for multiproduct, multiunit batch processes. In our own work, we proposed a two-stage stochastic scheduling program including process dynamics that handles endogenous and exogenous uncertainties and integrates scheduling and control for batch processes.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Stochastic Deep Learning–Short-Term Production Scheduling–Optimal Control Framework for General Batch Processes

Santander

Giannikopoulos

Stadtherr

et al. 2022

Ind. Eng. Chem. Res.

Self Cite

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Integrated operational decision-making in chemical plants is important for improving profitability. Integrated scheduling and control frameworks have been developed to enhance coordination between tactical and operational decisions. As such frameworks start to incorporate more features (e.g., uncertainty, detailed nonlinear process models) the computation time and resources that they require may increase significantly and lead to solutions that cannot be implemented online. Leveraging deep learning models that can capture process nonlinearities without a significant computational burden is a potential solution to this problem. Motivated by the above, we propose a two-stage stochastic integrated deep learning−scheduling−optimal control framework that can be used to model any batch process and can be solved efficiently. The benefits are demonstrated using a prototype case study.

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Integrated batch production planning and scheduling optimization considering processing time uncertainty

Gao,

Guo,

Liu

et al. 2024

Optim Eng

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Control-aware batch process scheduling

Cited by 13 publications

References 44 publications

Integration of Scheduling and Control for Chemical Batch Plants under Stochastic Uncertainty: A Back-Off Approach

Integration of Scheduling and Control for Chemical Batch Plants under Stochastic Uncertainty: A Back-Off Approach

An Integrated Stochastic Deep Learning–Short-Term Production Scheduling–Optimal Control Framework for General Batch Processes

Integrated batch production planning and scheduling optimization considering processing time uncertainty

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