Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks

Schweidtmann, Artur M.; Huster, Wolfgang R.; Lüthje, Jannik T.; Mitsos, Alexander

doi:10.1016/j.compchemeng.2018.10.007

Cited by 47 publications

(22 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We impose the annual operation cost C operation as a constraint and minimize the permeate concentration. [55] which is favorable for flowsheet problems [38,56] and optimization problems with ANNs embedded [20,21,35,36,57]. We also considered a full-space formulation with the solver BARON (version 18.5.8 using default options) [51] in GAMS [58] (version 25.1.1).…”

Section: Numerical Optimization Approachmentioning

confidence: 99%

“…For instance, ANNs enable empirical predictions of fouling behavior [24][25][26][27], process parameters [28][29][30], and salt retention for nanofiltration systems [31][32][33]. ANNs have also been used in various disciplines for process modeling with subsequent optimization with ANNs embedded [34][35][36]. To the best of our knowledge, there are no publications integrating membrane performance optimization and simultaneous membrane synthesis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous rational design of ion separation membranes and processes

Rall

Schweidtmann

Aumeier

et al. 2020

Journal of Membrane Science

Self Cite

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

Section: Numerical Optimization Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous rational design of ion separation membranes and processes

Rall

Schweidtmann

Aumeier

et al. 2020

Journal of Membrane Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…For instance, Nentwich et al [42] apply both ANNs and GPs as explicit surrogate models for calculating fugacity coefficients in a hydroformylation process replacing an iterative procedure from the PC-SAFT property model. Likewise, we replace implicit thermodynamic functions from the Helmholtz equation of state for working fluid properties in an organic Rankine cycle by ANNs that allow for calculating any property in an explicit way, thereby speeding up deterministic global optimization of the process [43,44]. Moreover, there are further approaches that directly replace tedious iterative calculations of phase equilibria by surrogate models, e.g., both McBride et al [45] and Nentwich et al [46] use GPs for explicitly calculating liquid-liquid equilibria compositions in a decanter, whereas we replace vapor-liquid equilibrium calculations in a flash by ANNs [47].…”

Section: Hybrid Mechanistic/data-driven Modeling In Chemical Engineeringmentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…We propose to use a continuous surrogate model for the solution of the algebraic Equation to further improve the computational efficiency of the model and make it readily applicable in state‐of‐the‐art frameworks for dynamic optimization. We apply ANNs, as they have been shown to be powerful surrogate models for replacing thermodynamic relationships . Furthermore, ANNs can be evaluated efficiently in a forward manner .…”

Section: Proposed Model Based On Compartmentalization and Annsmentioning

confidence: 99%

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks

Cited by 47 publications

References 37 publications

Simultaneous rational design of ion separation membranes and processes

Simultaneous rational design of ion separation membranes and processes

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

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

Contact Info

Product

Resources

About