Scenario‐integrated modeling and optimization of dynamic systems

Abel, O.; Marquardt, Wolfgang

doi:10.1002/aic.690460414

Cited by 27 publications

(5 citation statements)

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“…11,23 Typically, optimization of these systems is carried out using dynamic optimization via control parametrization, where the control actions exerted on the system are discretized over the operation horizon of interest. 24,25 Although several aspects of campaign manufacturing are covered in the literature, a more comprehensive investigation which includes process design, operational optimization, and techno-economic analysis is lacking.…”

Section: Introductionmentioning

confidence: 99%

“…There has been work published in the literature devoted to the analysis of campaign‐based manufacturing schemes, including the use of nonsmooth formulations for modeling continuous campaigns, 16,21,22 and the comparison of batch and continuous process manufacturing for specific products, such as atropine 11,23 . Typically, optimization of these systems is carried out using dynamic optimization via control parametrization, where the control actions exerted on the system are discretized over the operation horizon of interest 24,25 . Although several aspects of campaign manufacturing are covered in the literature, a more comprehensive investigation which includes process design, operational optimization, and techno‐economic analysis is lacking.…”

Section: Introductionmentioning

confidence: 99%

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Techno‐economic analysis of dynamic, end‐to‐end optimal pharmaceutical campaign manufacturing using PharmaPy

et al. 2023

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Increased interest in the pharmaceutical industry to transition from batch to continuouos manufacturing motivates the use of digital frameworks that allow systematic comparison of candidate process configurations. This article evaluates the technical and economic feasibility of different end‐to‐end optimal process configurations, namely, batch, hybrid and continuous, for small‐scale manufacturing of an active pharmaceutical ingredient. Production campaigns were analyzed for those configurations containing continuous equipment, where significant start‐up effects are expected given the relatively short campaign times considered. Hybrid operating mode was found to be the most attractive process configuration at intermediate and large annual production targets, which stems from combining continuous reactors and semi‐batch vaporization equipment. Continuous operation was found to be more costly, due to long stabilization times of continuous crystallization, and thermodynamic limitations of flash vaporization. Our work reveals the benefits of systematic digital evaluation of process configurations that operate under feasible conditions and compliant product quality attributes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Techno‐economic analysis of dynamic, end‐to‐end optimal pharmaceutical campaign manufacturing using PharmaPy

et al. 2023

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“…18,19 The main advantage of the SIP method is that it guarantees rigorous global optimality for certain categories of nonlinear problems. Another approach is the multiscenario-based method, [20][21][22] which generates scenarios within the uncertainty set and iteratively adds more scenarios as model constraints. However, this method cannot theoretically examine and ensure the feasibility of all scenarios over the whole uncertainty set.…”

Section: Introductionmentioning

confidence: 99%

Robust dynamic optimization for nonlinear chemical processes under measurable and unmeasurable uncertainties

Liang

Yuan

2022

AIChE Journal

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We formulate an integrated framework for the robust dynamic optimization of nonlinear chemical processes under measurable and unmeasurable uncertainties. An affine decision rule is proposed to approximate the causal dependence of the wait‐and‐see decision variables on the gradually revealed measurable uncertainties. To overcome the computational intractability of the proposed model, a linearization technique based on the first‐order Taylor expansion is introduced around the nominal values of uncertainties to derive the robust dynamic counterpart, which can be discretized to a large‐scale nonlinear programming (NLP) formulation. Effects of first discretizing the dynamic models or introducing the affine decision rule are investigated. The proposed framework is also compared with the state‐of‐the‐art re‐optimization and traditional robust optimization approaches. An illustrative example and an industrial semi‐batch 2‐mercaptobenzothiazole production case are involved to demonstrate the advantages and applicability of the proposed framework.

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“…The control design of the serially connected processes developed in the past used dynamic programming based optimization tools to handle the performance of the current run across stages [2,3]. The rigorous treatment of exceptional events by scenario-based differential algebraic equation models was also proposed [4]. Because the fatal problem of the methods lies in the fact that the above optimization is obtainable based on a reliable and healthy process model, with aging of the process, the control design based on the models with fixed parameters could not compensate for such process drifts or ramp disturbances.…”

Section: Introductionmentioning

confidence: 99%

Development of MBPLS based control for serial operation processes

Chen

Tsai

2009

Korean J. Chem. Eng.

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A control scheme based on the multiblock PLS (MBPLS) model for multi-stage processes (or serially connected processes) is developed. MBPLS arranges a large number of variables into meaningful blocks for each stage of the large-scale system. Two control design strategies, course-to-course (CtC) and within-stage (WS) controls, are proposed for the re-optimization design in the whole multistage course. In CtC, MBPLS control and optimization are done by applying feedback from the finished output quality when one course for all stages is done. It utilizes the information from the current course to improve quality of the next one. In WS, the MBPLS-based re-optimization strategy is developed to explore the possible adjustments of the future inputs at the rest of the stages in order to fix up the disturbances just in time and to maintain the product specification when the current course is finished. The proposed technique is successfully applied to two simulated industrial problems, including a photolithography sequences and a reverse osmosis desalination process, and the advantages of the proposed method are demonstrated.

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Scenario‐integrated modeling and optimization of dynamic systems

Cited by 27 publications

References 50 publications

Techno‐economic analysis of dynamic, end‐to‐end optimal pharmaceutical campaign manufacturing using PharmaPy

Techno‐economic analysis of dynamic, end‐to‐end optimal pharmaceutical campaign manufacturing using PharmaPy

Robust dynamic optimization for nonlinear chemical processes under measurable and unmeasurable uncertainties

Development of MBPLS based control for serial operation processes

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