An integrated framework for plant data-driven process modeling using deep-learning with Monte-Carlo simulations
- Hwangbo, Soonho; Al, Resul; Sin, Gurkan
- Issue Date
- PERGAMON-ELSEVIER SCIENCE LTD
- Deep-learning; Full-scale plant data; Domain-knowledge; Process modeling; Wastewater treatment plant; Nitrous-oxide emissions
- COMPUTERS & CHEMICAL ENGINEERING, v.143
- Journal Title
- COMPUTERS & CHEMICAL ENGINEERING
- 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 (R-2). 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. (C) 2020 Elsevier Ltd. All rights reserved.
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- 공학계열 > Dept.of Materials Engineering and Convergence Technology > Journal Articles
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