Abstract
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.
Original language | English |
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Article number | 107071 |
Journal | Computers & Chemical Engineering |
Volume | 143 |
Number of pages | 14 |
ISSN | 0098-1354 |
DOIs | |
Publication status | Published - 2020 |
Keywords
- Deep-learning
- Domain-knowledge
- Full-scale plant data
- Nitrous-oxide emissions
- Process modeling
- Wastewater treatment plant