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    Item type:Publication, Access status: with files ,
    Reactor network synthesis of enzymatic cascades using superstructure optimization
    (PSE Press, 2026-06-19)
    Chandra, Swastik
    ;
    Kinau, Siv
    ;
    Paschalidis, Leandros
    ;
    Skiborowski, Mirko
    While classical heuristics can be applied to decide on the preferred reactor concept for simple reaction schemes, more complex reaction networks require more sophisticated methods, such as the multilevel reactor design approach or superstructure optimization. Based on an analysis of the existing methods a nonlinear programming framework for a superstructure-based reactor network synthesis is presented, emphasizing numerical robustness and flexible network representation without relying on integer decisions. The approach, which is implemented in GAMS, allows for the combination of continuous stirred-tank and cross-flow reactor models. An exemplary application for the classical Van de Vusse reaction is first shown for validation, prior to the application to an enzymatic cascade based on the Weimberg pathway. Assuming fast co-factor regeneration, the performance of the resulting PFR cascade, which can also be interpreted as a sequence of batch reactions, is compared with a commonly applied single batch reactor. The results show that the two-reactor configuration consistently achieves higher product formation by mitigating inhibition effects, demonstrating the potential of reactor network synthesis for complex reaction systems like enzymatic cascades.
    Publicationtype: Conference Paper
      9  18
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    Item type:Publication, Access status: without files ,
    Hybrid optimization methodologies for the design of chemical processes
    (De Gruyter, 2025-04)
    Kruber, Kai
    ;
    Kinau, Siv Magdalena
    ;
    Skiborowski, Mirko
    ;
    Fernando Israel Gómez-Castro
    ;
    Vicente Rico-Ramírez
    Process synthesis and design problems in chemical engineering usually require a solution to complex nonlinear optimization problems with continuous and discrete decision variables. The resulting mixed-integer nonlinear programming problems are particularly hard to solve, and different strategies are frequently applied for their solution. Gradient-based optimization enables fast computations, exploiting local sensitivity information, but is usually limited to local optima for nonconvex problems, whereas derivative-free optimization methods can be linked to available simulation models, with little effort, but also without any guarantee of optimality. Metaheuristics, especially swarm intelligence and population-based algorithms, are frequently applied for simulation-based process optimization, overcoming the lack of gradient information, at the cost of a considerable number of simulations. Another strategy that is receiving increasing interest builds on surrogate models that are first generated based on an initial sampling of process simulations for systematically varied design variables. Tractable surrogate models do provide the necessary sensitivity information that enables efficient gradient-based optimization, while being only an approximation of the original problem. Each strategy has its advantages and limitations, and no single best option is generally favorable for all kinds of problems. Thoughtful combinations of different strategies have the potential to overcome or at least reduce the individual limitations, while simultaneously combining the strengths of the individual methods. The current chapter provides an introduction and overview of such hybrid optimization methodologies, together with some illustrations of their use for applications in chemical engineering. Several case studies, including utility and entrainer selection, illustrate the performance of hybrid optimization methods and indicate the ability to solve even more complex design problems.
    Publicationtype: Book Part
      68
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    Item type:Publication, Access status: without files ,
    Hybrid optimization methodologies for the design of chemical processes
    (De Gruyter, 2025)
    Kruber, Kai
    ;
    Kinau, Siv Magdalena
    ;
    Skiborowski, Mirko
    Process synthesis and design problems in chemical engineering usually require a solution to complex nonlinear optimization problems with continuous and discrete decision variables. The resulting mixed-integer nonlinear programming problems are particularly hard to solve, and different strategies are frequently applied for their solution. Gradient-based optimization enables fast computations, exploiting local sensitivity information, but is usually limited to local optima for nonconvex problems, whereas derivative- free optimization methods can be linked to available simulation models, with little effort, but also without any guarantee of optimality. Metaheuristics, especially swarm intelligence and population-based algorithms, are frequently applied for simulation-based process optimization, overcoming the lack of gradient information, at the cost of a considerable number of simulations. Another strategy that is receiving increasing interest builds on surrogate models that are first generated based on an initial sampling of process simulations for systematically varied design variables. Tractable surrogate models do provide the necessary sensitivity information that enables efficient gradient-based optimization, while being only an approximation of the original problem. Each strategy has its advantages and limitations, and no single best option is generally favorable for all kinds of problems. Thoughtful combinations of different strategies have the potential to overcome or at least reduce the individual limitations, while simultaneously combining the strengths of the individual methods. The current chapter provides an introduction and overview of such hybrid optimization methodologies, together with some illustrations of their use for applications in chemical engineering. Several case studies, including utility and entrainer selection, illustrate the performance of hybrid optimization methods and indicate the ability to solve even more complex design problems.
    Publicationtype: Book Part
      22