The Monte Carlo driven and Machine Learning enhanced Process Simulator
| Autor: | Gürkan Sin, Jerome Frutiger, Nevin Gerek Ince, Mark Nicholas Jones |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
020209 energy General Chemical Engineering Monte Carlo method Polynomial chaos 02 engineering and technology Machine learning computer.software_genre Process simulation Sensitivity 020401 chemical engineering 0202 electrical engineering electronic engineering information engineering Sensitivity (control systems) 0204 chemical engineering Propagation of uncertainty business.industry Uncertainty Sobol sequence Empirical distribution function Computer Science Applications Acentric factor Artificial intelligence business computer |
| Zdroj: | Jones, M N, Frutiger, J, Ince, N G & Sin, G 2019, ' The Monte Carlo driven and Machine Learning enhanced Process Simulator ', Computers & Chemical Engineering, vol. 125, pp. 324-338 . https://doi.org/10.1016/j.compchemeng.2019.03.016 Computers & Chemical Engineering |
| DOI: | 10.1016/j.compchemeng.2019.03.016 |
| Popis: | This study presents a methodology with tools integration to apply advanced uncertainty propagation and sensitivity analysis in connection with commercial process simulation software. The methodology was applied to two processes: a heat pump system and a molecular distillation process. The input parameters of the selected thermodynamic model, namely critical temperature, critical pressure and acentric factor, were considered as a source of uncertainty and analysed using Monte Carlo sampling techniques. This enabled the process model output uncertainty to be described as an empirical distribution function with a 95% confidence interval. Variance-based decomposition such as the Sobol method or standard regression were used to analyse the sensitivity of the respective properties. We also show that machine learning methods such as polynomial chaos expansion (PCE) can be applied to reduce the number of necessary process simulations and obtained equivalent results in comparison with the more costly full Monte Carlo based procedure. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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