Efficient sensitivity analysis and interpretation of parameter correlations in chemical engineering

Xie, Xue‐Jun; Schenkendorf, René; Krewer, Ulrike

doi:10.1016/j.ress.2018.06.010

Cited by 17 publications

(16 citation statements)

References 47 publications

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“…(1) cannot be solved analytically for practical problems. Thus, in a previous work [8], a useful framework for calculating the sensitivity indicators d i numerically via samplebased concepts was proposed. In detail, two different sample-based methods can be implemented to compute the coupled integrals in Eq.…”

Section: Pce-based Moment-independent Sensitivity Analysismentioning

confidence: 99%

“…Alternatively, the single-loop Monte Carlo simulation method is more efficient in computing the sensitivity indicators and is implemented in this work. The procedure for reformulating the double-loop problem into a single-loop problem can be found in [6,8,9] and the references therein. To decrease the computational load further, PCE is used to substitute the original computational expensive process model with low-cost surrogates which are evaluated in Eq.…”

Section: Pce-based Moment-independent Sensitivity Analysismentioning

confidence: 99%

See 1 more Smart Citation

The Effect of Correlated Kinetic Parameters on (Bio)Chemical Reaction Networks

Xie

Schenkendorf

Krewer

2019

Chemie Ingenieur Technik

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Exploiting the information provided by (bio)chemical reaction networks has proved beneficial for process analysis and design. To this end, parameter uncertainties have to be included in the analysis and design of (bio)chemical processes to ensure reliable model‐based results. The goal is to investigate the impact of parameter correlations on (bio)chemical reaction networks and parameter sensitivities. An efficient sensitivity analysis concept is demonstrated with two simulation studies, and the results indicate a significant impact of the parameter correlations on the derived parameter sensitivities and the model‐based results in general.

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Section: Pce-based Moment-independent Sensitivity Analysismentioning

confidence: 99%

Section: Pce-based Moment-independent Sensitivity Analysismentioning

confidence: 99%

The Effect of Correlated Kinetic Parameters on (Bio)Chemical Reaction Networks

Xie

Schenkendorf

Krewer

2019

Chemie Ingenieur Technik

Self Cite

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show abstract

“…Moreover, the GSA concepts can be categorized into two types: variance-based methods [22,52,53] and moment-independent methods [54]. For details about the definitions and a critical comparison of these two concepts, the interested reader is referred to [55].…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

2018

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Model-based design principles have received considerable attention in biotechnology and the chemical industry over the last two decades. However, parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies have been introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computational expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints for a robust process design. We also apply a global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies: (1) designing a heating/cooling profile for the essential part of a continuous production process; and (2) optimizing the feeding profile for a fed-batch reactor of the penicillin fermentation process. According to the derived results, the proposed framework of robust process design addresses uncertainties adequately and scales well with the number of uncertain parameters. Thus, the described robustification concept should be an ideal candidate for more complex (bio)chemical problems in model-based design.

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“…Moreover, the GSA concepts can be categorized into two types: variance-based methods [32,51,52] and moment-independent methods [53]. For details about the definitions and a critical comparison of these two concepts, the interested reader is referred to [54]. Although the moment-independent method has a more rigorous definition than the variance-based method, the variance-based approach is the standard in GSA and therefore, is implemented in this work.…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

Xie

Schenkendorf

Krewer

2018

Preprint

Self Cite

View full text Add to dashboard Cite

Model-based design has received considerable attention in biological and chemical industries over the last two decades. However, the parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies were introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computation expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints. We also suggest employing the information from global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies where one is to design a heating/cooling profile for the essential part of a continuous production process and the other is to optimize the feeding profile for a fed-batch reactor of the penicillin fermentation process. The results reveal that the proposed approach can be used successfully for complex (bio)chemical problems in model-based design.

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Efficient sensitivity analysis and interpretation of parameter correlations in chemical engineering

Cited by 17 publications

References 47 publications

The Effect of Correlated Kinetic Parameters on (Bio)Chemical Reaction Networks

The Effect of Correlated Kinetic Parameters on (Bio)Chemical Reaction Networks

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

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