Economic Nonlinear Model Predictive Control for Flexible Operation of Air Separation Units

Caspari, Adrian; Faust, Johannes M.M.; Schäfer, Pascal; Mhamdi, Adel; Mitsos, Alexander

doi:10.1016/j.ifacol.2018.11.028

Cited by 25 publications

(19 citation statements)

References 14 publications

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“…This work focuses on a process for producing highly pure gaseous nitrogen for an on‐site customer, such as a chemical plant (Figure ). Similar process designs are well investigated in the literature …”

Section: Air Separation Unitsmentioning

confidence: 99%

“…The only difference between the two lies in the way that the rectification column is modeled. For all other unit operations, we use the same models as in Caspari et al for both solving the OCP and simulating the dynamic response of the plant replacement. Details can be found in the Supporting Information.…”

Section: Case Study: Optimal Control Of a Nitrogen Plantmentioning

confidence: 99%

“…One opportunity to address the real‐time challenge lies in the use of fast update schemes, which avoid the solution of a nonlinear problem. Corresponding control schemes have already been applied to ASUs . A second possibility is the use of reduced‐dimensional dynamic models to lower the computational demand for solving the dynamic optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks

et al. 2019

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The availability of reduced‐dimensional, accurate dynamic models is crucial for the optimal operation of chemical processes in fast‐changing environments. Herein, we present a reduced modeling approach for rectification columns. The model combines compartmentalization to reduce the number of differential equations with artificial neural networks to express the nonlinear input–output relations within compartments. We apply the model to the optimal control of an air separation unit. We reduce the size of the differential equation system by 90% while limiting the additional error in product purities to below 1 ppm compared to a full‐order stage‐by‐stage model. We demonstrate that the proposed model enables savings in computational times for optimal control problems by ~95% compared to a full order and ~99% to a standard compartment model. The presented model enables a trade‐off between accuracy and computational efficiency, which is superior to what has recently been reported for similar applications using collocation‐based reduction approaches.

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Section: Air Separation Unitsmentioning

confidence: 99%

Section: Case Study: Optimal Control Of a Nitrogen Plantmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks

et al. 2019

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“…The latter responses consider electricity spot prices to reduce electrical energy costs. In addition to the economic benefits, environmental impacts, especially carbon footprint, can be mitigated by exploiting electricity with a high share of RES [34,35] electricity-intensive industrial processes such as air separation units (ASU) [36][37][38][39], chlor-alkali (CA) electrolysis [32,40], seawater reverse osmosis [41], and aluminum production processes [42], DSM is particularly worthwhile.…”

Section: Demand Side Managementmentioning

confidence: 99%

Power‐to‐X: Between Electricity Storage, e‐Production, and Demand Side Management

Burre

Bongartz

Brée

et al. 2019

Chemie Ingenieur Technik

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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.The common understanding of Power-to-X is exclusively the use of renewable electricity to manufacture products currently based on fossil sources. In this paper, it is argued that beyond such e-Production many of these technologies also include aspects related to demand side management and temporal storage of electricity. Therefore, a definition of Powerto-X is suggested that encompasses all three aspects. It is discussed, which of these are relevant under which conditions and illustrative examples are highlighted, which show how process systems engineering can help address common challenges for Power-to-X technologies.

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“…Herein, we provide a critical discussion of opportunities and limitations of such hybrid mechanistic/data-driven approaches for reduced dynamic modeling with a particular focus on the operation of distillation columns. Note that efforts addressing the development of efficient NMPC algorithms [24][25][26][27][28] are complementary, i.e., real-time capable control solutions will require both reduced dynamic models and efficient numerical algorithms. This discussion is, however, out of the scope of this manuscript.…”

Section: Introduction -Revisiting An Old Topicmentioning

confidence: 99%

The Potential of Hybrid Mechanistic/Data‐Driven Approaches for Reduced Dynamic Modeling: Application to Distillation Columns

Schäfer

Caspari

Schweidtmann

et al. 2020

Chemie Ingenieur Technik

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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Extensive literature has considered reduced, but still highly accurate, nonlinear dynamic process models, particularly for distillation columns. Nevertheless, there is a need for continuing research in this field. Herein, opportunities from the integration of machine learning into existing reduction approaches are discussed. First, key concepts for dynamic model reduction and their limitations are briefly reviewed. Afterwards, promising model structures for reduced hybrid mechanistic/data-driven models are outlined. Finally, crucial future challenges as well as promising research perspectives are presented.

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Economic Nonlinear Model Predictive Control for Flexible Operation of Air Separation Units

Cited by 25 publications

References 14 publications

Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks

Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks

Power‐to‐X: Between Electricity Storage, e‐Production, and Demand Side Management

The Potential of Hybrid Mechanistic/Data‐Driven Approaches for Reduced Dynamic Modeling: Application to Distillation Columns

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