Data-driven surrogate modeling and benchmarking for process equipment

• Published in: Data-Centric Engineering (Online) Vol. 1
• Main Authors: Gonçalves, Gabriel F. N., Batchvarov, Assen, Liu, Yuyi, Liu, Yuxin, Mason, Lachlan R., Pan, Indranil, Matar, Omar K.
• Format: Journal Article
• Language: English
• Published: Cambridge Cambridge University Press 01.01.2020
• Subjects: Active learning; Algorithms; Benchmarks; Best practice; Complex systems; Computational fluid dynamics; data-driven; Default; Design optimization; Design parameters; Fluid dynamics; Gaussian process; Machine learning; Methods; Parameter sensitivity; Regression analysis; Sensitivity analysis; Simulation; Simulators; Software; Source code; surrogate models; Task complexity
• ISSN: 2632-6736; 2632-6736
• DOI: 10.1017/dce.2020.8

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. The recommendations provided here can be used for engineers interested in building computationally inexpensive surrogate models for fluid systems for design or optimization purposes. The test cases can be used by researchers to test and benchmark new algorithms for active learning for this class of problems. An open-source library with tools and scripts has been provided in order to support derived work.