An integrated framework for plant data-driven process modeling using deep-learning with Monte-Carlo simulations

Soonho Hwangbo*, Resul Al, Gürkan Sin

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


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 languageEnglish
Article number107071
JournalComputers & Chemical Engineering
Number of pages14
Publication statusPublished - 2020


  • Deep-learning
  • Domain-knowledge
  • Full-scale plant data
  • Nitrous-oxide emissions
  • Process modeling
  • Wastewater treatment plant

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