Multi-criteria optimization in chemical process design and decision support by navigation on Pareto sets

Bortz, Michael; Burger, J.; Asprion, Norbert; Blagov, S.; Böttcher, Roger; Nowak, U.; Scheithauer, Andreas; Welke, Richard J.; Küfer, Karl‐Heinz; Hasse, Hans

doi:10.1016/j.compchemeng.2013.09.015

Cited by 115 publications

(128 citation statements)

References 14 publications

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“…First, the required optimization solvers and the sandwiching algorithm for adaptive scalarization [1,17] are linked to the Excel COM client by referencing the respective type libraries (tlb), which themselves access corresponding dynamically linked libraries (dll). The employed optimization solvers are NLPQLP (nonlinear programming with non-monotone and distributed line search) for ordinary nonlinear problems and MISQP (mixed-integer sequential quadratic programming) for mixed-integer problems [19].…”

Section: Computational Frameworkmentioning

confidence: 99%

“…A detailed explanation of this algorithm can be found in [1,16,17]. It enables the calculation of Pareto sets with a well-defined approximation quality and with the corresponding lowest possible number of Pareto points, and hence, with a minimum of computing time.…”

Section: Introductionmentioning

confidence: 99%

“…In a recent report, Sundberg et al compared the performance of three different optimization solvers during the optimization of a flow sheet for methanol synthesis implemented in UniSim [10]. For additional applications of this method to process optimization, see [1,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Flow sheet simulations are widely used for development in process engineering, but, unfortunately, the commercially available flow sheet simulators are unable to perform optimization runs with more than a single criterion. This can be overcome either by integrating an MCO framework into a flow sheet simulator [1] or by exploiting an external programming interface. The latter approach is used in this work to realize multi-criteria optimization with CHEMCAD.…”

Section: Introductionmentioning

confidence: 99%

“…However, that lies beyond the scope of the examples treated in this work. For details on combining sandwiching and hyperboxing, the reader is referred to [1].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multi‐criteria Optimization of an Industrial World‐Scale Process

Kurnatowski

Bortz

Scherrer

et al. 2017

Chemie Ingenieur Technik

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Commercially available flow sheet simulators cannot perform optimization runs with more than one criterion. To overcome this problem, the flow sheet simulator CHEMCAD is combined with an external optimization solver, and the Excel VBA client is used to arrange inter-process communications. The resulting tool for multi-criteria optimization in chemical process engineering is applied to two separation problems. First, a dividing-wall column is used as a theoretical example to test different starting points and different scalarization techniques. Second, a significant decrease in energy demand is achieved for a real continuous world-scale facility.

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Section: Computational Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…However, that lies beyond the scope of the examples treated in this work. For details on combining sandwiching and hyperboxing, the reader is referred to [1].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multi‐criteria Optimization of an Industrial World‐Scale Process

Kurnatowski

Bortz

Scherrer

et al. 2017

Chemie Ingenieur Technik

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Energy Management in Chemical Industry

Asprion,

Giesa,

Mairhofer

et al. 2024

Ullmann's Encyclopedia of Industrial Chemistry

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The article contains sections titled: 1 Introduction 2 Sustainability and Energy Management 2.1 Feedstock and Energy Supply for the Chemical Industry 2.1.1 Current Feedstock Supply 2.1.2 Current Energy Supply 2.1.3 Alternative Feedstock Supply 2.1.4 Alternative Energy Supply 2.2 Regulatory Aspects 2.3 Process Design Objectives 3 Energy Management in Chemical Process Operations 3.1 Process Development 3.1.1 Process Synthesis 3.1.2 Development of New Processes 3.1.3 Reevaluation of Existing Processes 3.2 Plant Design 3.2.1 Exergy Analysis and Multicriteria Optimization 3.2.2 Process Operation Parameters 3.2.3 Pinch Technology 3.3 Equipment Design 3.3.1 Design of Distillation Columns 3.3.2 Heat Exchanger Design 3.4 Plant Operation 3.4.1 Control Design and Strategy 3.4.2 Plant Information Systems 3.4.3 Electrical Energy 3.5 Production Site Management 4 Conclusion List of Abbreviations References

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A hierarchical method to integrated solvent and process design of physical CO₂ absorption using the SAFT‐γ Mie approach

et al. 2015

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in Wiley Online Library (wileyonlinelibrary.com) Molecular-level decisions are increasingly recognized as an integral part of process design. Finding the optimal process performance requires the integrated optimization of process and solvent chemical structure, leading to a challenging mixed-integer nonlinear programming (MINLP) problem. The formulation of such problems when using a group contribution version of the statistical associating fluid theory, SAFT-c Mie, to predict the physical properties of the relevant mixtures reliably over process conditions is presented. To solve the challenging MINLP, a novel hierarchical methodology for integrated process and solvent design (hierarchical optimization) is presented. Reduced models of the process units are developed and used to generate a set of initial guesses for the MINLP solution. The methodology is applied to the design of a physical absorption process to separate carbon dioxide from methane, using a broad selection of ethers as the molecular design space. The solvents with best process performance are found to be poly(oxymethylene)dimethylethers.

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Multi-criteria optimization in chemical process design and decision support by navigation on Pareto sets

Cited by 115 publications

References 14 publications

Multi‐criteria Optimization of an Industrial World‐Scale Process

Multi‐criteria Optimization of an Industrial World‐Scale Process

Energy Management in Chemical Industry

A hierarchical method to integrated solvent and process design of physical CO₂ absorption using the SAFT‐γ Mie approach

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Multi-criteria optimization in chemical process design and decision support by navigation on Pareto sets

Cited by 115 publications

References 14 publications

Multi‐criteria Optimization of an Industrial World‐Scale Process

Multi‐criteria Optimization of an Industrial World‐Scale Process

Energy Management in Chemical Industry

A hierarchical method to integrated solvent and process design of physical CO2 absorption using the SAFT‐γ Mie approach

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Product

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A hierarchical method to integrated solvent and process design of physical CO₂ absorption using the SAFT‐γ Mie approach