Development of a surrogate model of an amine scrubbing digital twin using machine learning methods

• Published in: Computers & chemical engineering Vol. 174; p. 108252
• Main Authors: Galeazzi, Andrea, Prifti, Kristiano, Cortellini, Carlo, Di Pretoro, Alessandro, Gallo, Francesco, Manenti, Flavio
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2023; Elsevier
• Subjects: Amine scrubbing; Chemical and Process Engineering; Design of experiments; Digital twin; Engineering Sciences; Latin hypercube; Machine-learning; Surrogate modeling; Amine scrubbing; Latin hypercube; Digital twin; Surrogate modeling; Machine-learning; Design of experiments
• ISSN: 0098-1354; 1873-4375; 1873-4375
• DOI: 10.1016/j.compchemeng.2023.108252

Advancements in the process industry require building more complex simulations and performing computationally intensive operations like optimization. To overcome the numerical limit of conventional process simulations a surrogate model is a viable strategy. In this work, a surrogate model of an industrial amine scrubbing digital twin has been developed. The surrogate model has been built based on the process simulation created in Aspen HYSYS and validated as a digital twin against real process data collected during a steady-state operation. The surrogate relies on an accurate Design of Experiments procedure. In this case, the Latin-Hypercube method has been chosen and several nested domains have been defined in ranges around the nominal steady state operative condition. Several machine learning models have been trained using cross-validation, and the most accurate has been selected to predict each target. The resulting surrogate model showed a satisfactory performance, given the data available. •The method proposed can be applied to automatically surrogate any digital twin.•Data-driven machine learning algorithms can be applied to metamodel a digital twin.•Surrogate performance is greatly affected by the design of experiments training data.