Global optimization of distillation columns using explicit and implicit surrogate models

Keßler, Tobias; Kunde, Christian; McBride, Kevin; Mertens, Nick; Michaels, Dennis; Sundmacher, Kai; Kienle, Achim

doi:10.1016/j.ces.2018.12.002

“…A binary input is set to 1 if the respective integer is selected and is 0 otherwise (so-called SOS-1 set). It should be noted that the consideration of discrete decision variables in surrogatebased optimization was applied in the literature (e.g., [43][44][45]), but theoretical foundations of these methods are an active field of research [46,47].…”

Section: Mechanistic Process Model To Describe the Process Plantmentioning

confidence: 99%

Simultaneous rational design of ion separation membranes and processes

Rall

¹

,

Schweidtmann

²

,

Aumeier

³

et al. 2020

Journal of Membrane Science

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Economically viable water treatment process plants for drinking water purification are a prerequisite for sustainable supply of safe drinking water in the future. However, modern membrane process development experiences a disconnect in this domain: the synthesis of the membrane and the design of the process are decoupled. We propose an optimization strategy to simultaneously design the performance of layer-by-layer nanofiltration membrane modules and the separation process. This approach achieves overall optimal performance by extending the search space and thus exploiting synergies. Better separation performances at a lower cost as compared to conventional optimization strategies can be achieved. The key feature of this optimization framework is the integration of artificial neural networks. This machine-learning technique describes membrane performance as a function of its synthesis protocol. We optimize the design problem rigorously by a deterministic global nonlinear optimization method. Thus, this framework yields membrane synthesis protocols and membrane processes that are optimally tailored to the desired separation task. In a showcase, the simultaneous membrane synthesis and process optimization design achieve immediately favorable results with lower impurities at comparable costs. The process investment and operation costs are compared to a state of the art commercially available membrane for nanofiltration.

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“… and Keßler et al. gave recently examples of such applications. If the model structure of the surrogate models is known, more easily tight convex or concave relaxations can be provided, which would also enable improved global optimization.…”

Section: Combining Data and Modelsmentioning

confidence: 99%

Modeling, Simulation, and Optimization 4.0 for a Distillation Column

Asprion

¹

2020

Chemie Ingenieur Technik

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Simulation is besides experimentation a key technology in design, analysis and operation of distillation columns. The reliability of simulations to answer questions concerning the real process strongly depends on model quality, which needs to be reliable and predictive. Usually available plant data can be used to validate and if necessary, to improve the model. In this contribution challenges and opportunities of a continuous model improvement in a flowsheet simulator with emphasis on new possibilities triggered by digitalization will be discussed.

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“…A binary input is set to 1 if the respective integer is selected and is 0 otherwise (so-called SOS-1 set). It should be noted that the consideration of discrete decision variables in surrogatebased optimization was applied in the literature (e.g., [43][44][45]), but theoretical foundations of these methods are an active field of research [46,47].…”

Section: Mechanistic Process Model To Describe the Process Plantmentioning

confidence: 99%

Simultaneous rational design of ion separation membranes and processes

Weßling¹

2020

Preprint

0

View full text Add to dashboard Cite

Economically viable water treatment process plants for drinking water purification are a prerequisite for sustainable supply of safe drinking water in the future. However, modern membrane process development experiences a disconnect in this domain: the synthesis of the membrane and the design of the process are decoupled. We propose an optimization strategy to simultaneously design the performance of layer-by-layer nanofiltration membrane modules and the separation process. This approach achieves overall optimal performance by extending the search space and thus exploiting synergies. Better separation performances at a lower cost as compared to conventional optimization strategies can be achieved. The key feature of this optimization framework is the integration of artificial neural networks. This machine-learning technique describes membrane performance as a function of its synthesis protocol. We optimize the design problem rigorously by a deterministic global nonlinear optimization method. Thus, this framework yields membrane synthesis protocols and membrane processes that are optimally tailored to the desired separation task. In a showcase, the simultaneous membrane synthesis and process optimization design achieve immediately favorable results with lower impurities at comparable costs. The process investment and operation costs are compared to a state of the art commercially available membrane for nanofiltration.

show abstract

Global optimization of distillation columns using explicit and implicit surrogate models

Cited by 46 publications

References 29 publications

Simultaneous rational design of ion separation membranes and processes

Simultaneous rational design of ion separation membranes and processes

Modeling, Simulation, and Optimization 4.0 for a Distillation Column

Simultaneous rational design of ion separation membranes and processes

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