Journal article
An integrated framework for plant data-driven process modeling using deep-learning with Monte-Carlo simulations
KT Consortium, Department of Chemical and Biochemical Engineering, Technical University of Denmark1
PROSYS - Process and Systems Engineering Centre, Department of Chemical and Biochemical Engineering, Technical University of Denmark2
Department of Chemical and Biochemical Engineering, Technical University of Denmark3
This study aims to develop a deep-learning-based and plant data-driven framework for process modeling to help understanding plant-wide processes. The systematic framework consists of the following steps: data processing based on domain-knowledge, deep-learning model development, model selection using information criteria, and global sensitivity analysis with Monte-Carlo simulations.
The assessment of the quality of the optimal deep-learning model to support plant-wide process understanding is the key emphasis of this framework. The proposed framework was applied for analyzing long-term data from wastewater treatment plants to predict nitrous oxide emission characteristics. The results showed a promising potential of the framework to systematically and efficiently develop fit-for-purpose deep-learning models with highly favorable cross-validation statistics (R2).
The framework is expected to facilitate the development of versatile deep-learning models based on plant data encompassing nonlinear and complex process phenomena, where especially mechanistic models are not available.
Language: | English |
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Year: | 2020 |
Pages: | 107071 |
ISSN: | 00981354 and 18734375 |
Types: | Journal article |
DOI: | 10.1016/j.compchemeng.2020.107071 |
ORCIDs: | Hwangbo, Soonho , Al, Resul and Sin, Gürkan |