Modelling of a pressure swing adsorption unit by deep learning and artificial Intelligence tools
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||Chemical Engineering Science|
|Publication status||Published - 2020|
|Publication type||A1 Journal article-refereed|
Syngas is one of the main sources available for the production of pure H2 and synthetic fuels, among others. Pressure swing adsorption (PSA) is considered to be an efficient alternative for pre-treatment of syngas. However, it displays very complex dynamical behaviour. This work proposes the development of different Artificial Intelligence based models for the prediction of the dynamic behaviour of several process output variables. A classical model of ANNs, a machine learning model and a deep learning model was here developed. It was found that Deep Learning networks were the only ones capable of fully representing the dynamic behaviour of the PSA unit, whereas the other models were only partially capable of predicting it. Thus, it is proposed a reliable real-time soft sensor for a PSA unit based on Deep Leaning strategy. This strategy provides bases to overtake several problems associated to this processes control, operation and optimization.
ASJC Scopus subject areas
- Artificial intelligence, Artificial neural networks, Deep learning, Machine learning, Pressure swing adsorption, Syngas