Tom Van Gerven and Pedro Santos Bartolomé from Process Engineering for Sustainable Systems (ProcESS), KU Leuven have published a new study on dynamic active learning for meta-modelling of process simulations. The article is published in the July issie of the journal Engineering Applications of Artificial Intelligence.
Accurate simulations are of fundamental importance for process engineers, but most modern simulation software is often inflexible, and cannot be easily integrated into new applications. Starting from this motivation, we propose the use of Dynamic Active Learning, the extension of active learning algorithms to dynamic processes simulations in order to generate meta-models which can be flexibly implemented into any application . The algorithm is described, implemented and tested on three process simulations (generated with Aspen Hysys), conducting sensitivity experiments on the main implementation parameters and the complexity of the simulations. The results are compared with a benchmark of (optimally chosen) uniform random inputs, showing an increase in the explored volume on the domain of the process function, which goes up to 120% for the more complex simulation, with an increase in the time demand of only 30% . The results also highlight the importance of the choice of sampling method, since in all cases the space exploration gains quickly diminish as the number of samples increase.
Reference
https://doi.org/10.1016/j.engappai.2024.108539
Acknowledgements
All research was conducted within the EU CHARMING project, which has received funding from the European Union’s EU Framework Programme for Research and Innovation Horizon 2020 under Grant Agreement No 812716. The authors would like to especially thank the European Union citizens, who ultimately finance this research.
