Surrogate Modelling and Optimization for Complex Liquefied Natural Gas Refrigeration Cycles

Savage, Thomas; Almeida‐Trasvina, Fernando; Chanona, Antonio Del-Rio; Smith, Robin; Zhang, Dongda

doi:10.1016/j.ifacol.2020.12.316

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…A survey on such sampling strategies for global surrogate modeling can be found in [5]. In principle, there are two different approaches of surrogates for an entire flowsheet: either one surrogate model is built for the entire process [6][7][8] or individual surrogate unit models are generated for the key units of the process that subsequently are combined to the overall flowsheet [9]. Following the first approach to train one surrogate model for the entire flowsheet can become impractical for large or complex flowsheets.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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An integrated dimensionality reduction and surrogate optimization approach for plant‐wide chemical process operation

Savage

Almeida‐Trasvina

Chanona³

et al. 2021

AIChE Journal

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With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data-driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large-scale industrial chemical systems.

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Optimal operational analysis of metamodel based single mixed refrigerant cryogenic process for floating liquefied natural gas plant technology

Ali

2022

Results in Engineering

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Surrogate Modelling and Optimization for Complex Liquefied Natural Gas Refrigeration Cycles

Cited by 3 publications

References 9 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

An integrated dimensionality reduction and surrogate optimization approach for plant‐wide chemical process operation

Optimal operational analysis of metamodel based single mixed refrigerant cryogenic process for floating liquefied natural gas plant technology

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