Data-driven Process Design Exemplified on the Steam Methane Reforming Process

Lueg, Laurens; Schack, Dominik; Örs, Evrim; Schmidt, R.J.; Bickert, Patricia; Kurnatowski, Martin von; Ludl, Patrick Otto; Bortz, Michael

doi:10.1016/b978-0-323-88506-5.50156-x

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…4 shows the results divided into a subsection emphasizing the accuracy of the underlying unit models and a subsection comparing the accuracy of the combined flowsheet with the results obtained by the benchmark simulation. This paper continues recent activities of Air Liquide Forschung und Entwicklung GmbH and the Fraunhofer ITWM in the field of surrogate modeling [13][14][15].…”

Section: Introductionsupporting

confidence: 54%

“…The workflow to enable the process simulation using connected surrogate unit models is based on a previous publication [15] and is schematically illustrated in Fig. 1.…”

Section: Workflowmentioning

confidence: 99%

“…The number of samples and the sampling strategy has to be specified, e.g., random sampling or Latin hypercube sampling. If mole fractions of stream compositions are among the input variables, special sampling strategies have to be employed, since the fractions are not independent (they must add up to 1) [15].…”

Section: Samplingmentioning

confidence: 99%

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Data‐Driven Process Simulation Using Connected Surrogate Unit Models Exemplified on a Steam Methane Reforming Process

Schack

Lueg

Schmidt

et al. 2021

Chemie Ingenieur Technik

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Process simulation based on physical models often faces computational problems with respect to convergence, especially if the underlying flowsheets are complex. The use of data-driven surrogate models connected to flowsheets promises to overcome these challenges. Using the steam methane reforming process, this paper presents the development of surrogate models -artificial neural networks -for the key units of the process that are subsequently connected to form the entire flowsheet. The accuracy of the individual surrogate models is analyzed based on the test error; the accuracy of the flowsheet is evaluated by a benchmark process simulation performed in Aspen Plus . Therefore, the predicted key variables, here outlet temperatures and compositions, are compared to the benchmark. It is shown that their maximum error is below the typical measurement error. The comparison of the accuracy of the surrogate-based flowsheet simulation with the Aspen Plus simulation proves to match very well, as long as the training ranges of the underlying surrogate models are not violated. The promising results of this paper pave the way for future work, such as the optimization of process parameters or superstructure optimization.

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Section: Introductionsupporting

confidence: 54%

“…The workflow to enable the process simulation using connected surrogate unit models is based on a previous publication [15] and is schematically illustrated in Fig. 1.…”

Section: Workflowmentioning

confidence: 99%

See 1 more Smart Citation

Data‐Driven Process Simulation Using Connected Surrogate Unit Models Exemplified on a Steam Methane Reforming Process

Schack

Lueg

Schmidt

et al. 2021

Chemie Ingenieur Technik

Self Cite

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“…A more sophisticated sampling method is required if maintaining these bounds is crucial. Lueg et al [21] present a solution for this problem. They developed and compared different sampling strategies for problems with variables that are partially constrained.…”

Section: Data Generationmentioning

confidence: 99%

Explaining and Integrating Machine Learning Models with Rigorous Simulation

Schöneberger¹,

Aker²,

Fricke³

2021

Chemie Ingenieur Technik

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First‐principle flowsheet simulation is a reliable data resource for training machine learning (ML) models for process industry, especially when plant data is not available. Process simulators play an even more important role for evaluation and validation of ML models. In this work, we present a workflow for building and evaluating ML models based on data generated by a commercial flowsheet simulator. The resulting hybrid models, combining data‐driven predictions with mass and energy balances, have much lower calculation times than the rigorous models. The implementation of such models shows great potential for solving more complex process engineering problems on the high‐dimensional space in the future, while saving the process engineer's time in the present.

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Data-driven Process Design Exemplified on the Steam Methane Reforming Process

Cited by 2 publications

References 6 publications

Data‐Driven Process Simulation Using Connected Surrogate Unit Models Exemplified on a Steam Methane Reforming Process

Data‐Driven Process Simulation Using Connected Surrogate Unit Models Exemplified on a Steam Methane Reforming Process

Explaining and Integrating Machine Learning Models with Rigorous Simulation

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