Overview of Surrogate Modeling in Chemical Process Engineering

McBride, Kevin; Sundmacher, Kai

doi:10.1002/cite.201800091

Cited by 193 publications

(117 citation statements)

References 95 publications

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“…For the sake of readability, some details of each approach are omitted here and we refer to the original publications for comprehensive descriptions. A broader overview on general surrogate modeling is available in the literature [2,3,11,12].…”

Section: Surrogate Modeling Of Llementioning

confidence: 99%

“…The concept of surrogate modeling comprises a number of different approaches [2] that depend on the type of the original model and the desired properties of the surrogate. In chemical process engineering, data-driven surrogate modeling is usually employed [3]. This class of methods generally treats the original model as a black box with input-output data.…”

Section: Introductionmentioning

confidence: 99%

“…The additional cost for sample generation is reduced by employing efficient sampling methods. Adaptive sampling approaches that employ exploration and exploitation strategies are preferred over one-shot methods in recent literature (see [3,4] for references).…”

Section: Introductionmentioning

confidence: 99%

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Surrogate Modeling for Liquid–Liquid Equilibria Using a Parameterization of the Binodal Curve

et al. 2019

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Computational effort and convergence problems can pose serious challenges when employing advanced thermodynamic models in process simulation and optimization. Data-based surrogate modeling helps to overcome these problems at the cost of additional modeling effort. The present work extends the range of methods for efficient data-based surrogate modeling of liquid-liquid equilibria. A new model formulation is presented that enables smaller surrogates with box-constrained input domains and reduced input dimensions. Sample data are generated efficiently by using numerical continuation. The new methods are demonstrated for the surrogate modeling and optimization of a process for the hydroformylation of 1-decene in a thermomorphic multiphase system.

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Section: Surrogate Modeling Of Llementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surrogate Modeling for Liquid–Liquid Equilibria Using a Parameterization of the Binodal Curve

et al. 2019

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“…Process models are used in the design and operation stages of a process plant for a wide array of tasks including design optimization, sensitivity analysis and uncertainty quantification among others (McBride and Sundmacher, 2019). Most common process models use regression, where a variable of interest (e.g., a drag, or heat transfer coefficient) can be mapped as some function of operating conditions, fluid properties, and/or geometric parameters.…”

Section: Introductionmentioning

confidence: 99%

Data-driven surrogate modeling and benchmarking for process equipment

Gonçalves

Batchvarov

Liu

et al. 2020

DCE

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In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization. A suite of computational fluid dynamics (CFD) simulations geared toward chemical process equipment modeling has been developed and validated with experimental results from the literature. Various regression-based active learning strategies are explored with these CFD simulators in-the-loop under the constraints of a limited function evaluation budget. Specifically, five different sampling strategies and five regression techniques are compared, considering a set of four test cases of industrial significance and varying complexity. Gaussian process regression was observed to have a consistently good performance for these applications. The present quantitative study outlines the pros and cons of the different available techniques and highlights the best practices for their adoption. The test cases and tools are available with an open-source license to ensure reproducibility and engage the wider research community in contributing to both the CFD models and developing and benchmarking new improved algorithms tailored to this field.

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“…When a clear relationship between input variables and their responses is not accessible to the designer, it is challenging to perform optimization and study sensitivity and reliability analysis. A surrogate model, on the other hand, can replace these complex, underlying models with simple models which has several advantages including much faster simulation [16]. In recent times we see increasing use of surrogate model within the chemical process engineering particularly for model predictive control (MPC) [17], model-based optimization [18], sensitivity analysis [19], reliability assessment [20] among others.…”

Section: Introductionmentioning

confidence: 99%

Impact of Sampling Technique on the Performance of Surrogate Models Generated with Artificial Neural Network (ANN): A Case Study for a Natural Gas Stabilization Unit

et al. 2019

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Data-driven models are essential tools for the development of surrogate models that can be used for the design, operation, and optimization of industrial processes. One approach of developing surrogate models is through the use of input–output data obtained from a process simulator. To enhance the model robustness, proper sampling techniques are required to cover the entire domain of the process variables uniformly. In the present work, Monte Carlo with pseudo-random samples as well as Latin hypercube samples and quasi-Monte Carlo samples with Hammersley Sequence Sampling (HSS) are generated. The sampled data obtained from the process simulator are fitted to neural networks for generating a surrogate model. An illustrative case study is solved to predict the gas stabilization unit performance. From the developed surrogate models to predict process data, it can be concluded that of the different sampling methods, Latin hypercube sampling and HSS have better performance than the pseudo-random sampling method for designing the surrogate model. This argument is based on the maximum absolute value, standard deviation, and the confidence interval for the relative average error as obtained from different sampling techniques.

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Overview of Surrogate Modeling in Chemical Process Engineering

Cited by 193 publications

References 95 publications

Surrogate Modeling for Liquid–Liquid Equilibria Using a Parameterization of the Binodal Curve

Surrogate Modeling for Liquid–Liquid Equilibria Using a Parameterization of the Binodal Curve

Data-driven surrogate modeling and benchmarking for process equipment

Impact of Sampling Technique on the Performance of Surrogate Models Generated with Artificial Neural Network (ANN): A Case Study for a Natural Gas Stabilization Unit

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