Hydrogen purification layered bed optimization based on artificial neural network prediction of breakthrough curves |
| |
| Affiliation: | 1. Hubei Key Laboratory of Advanced Technology for Automotive Components and Hubei Collaborative Innovation Center for Automotive Components Technology, School of Automotive Engineering, Wuhan University of Technology, Hubei 430070, China;2. School of Automotive Engineering, Wuhan Technical College of Communications, Hubei 430065, China;3. Hydrogen Research Institute, Université du Québec à Trois-Rivières, QC G9A 5H7, Canada;4. School of Mathematics and Computer Science, Jianghan University, Hubei 430056, China;1. Young Researcher and Elite Club, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran;2. Department of Gas Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), P.O. Box 63431, Ahwaz, Iran;3. Department of Chemistry, Sharif University of Technology, Azadi Avenue, Tehran, Iran;4. Universal Scientific Education and Research Network (USERN), Los Angeles, CA, USA;5. Department of Chemical Engineering, University of Tehran, Tehran, Iran;6. Automation and Instrumentation Department, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), P.O. Box 63431, Ahwaz, Iran;1. Hubei Key Laboratory of Advanced Technology for Automotive Components and Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Hubei 430070, China;2. Hydrogen Research Institute, Université du Québec à Trois-Rivières, QC G9A 5H7, Canada;1. Escola de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil;2. PEQ/COPPE, Universidade Federal do Rio de Janeiro, Brazil;1. Hubei Key Laboratory of Advanced Technology for Automotive Components and Hubei Collaborative Innovation Center for Automotive Components Technology, School of Automotive Engineering, Wuhan University of Technology, Hubei, 430070, China;2. Hydrogen Research Institute, Université du Québec à Trois-Rivières, QC, G9A 5H7, Canada |
| |
| Abstract: | Artificial neural network has generally been used for a quantity of tasks such as classification, prediction, clustering and association analysis in different application fields. To the best of our knowledge, there are few researches on breakthrough curve used artificial neural network. In this paper, an artificial neural network model is established for breakthrough curves prediction in relation to a ternary components gas with a two-layered adsorbent bed piled up with activated carbon (AC) and zeolite, and an optimization is concluded by the artificial neural network. The performance data which acquired by Aspen model has been utilized for training artificial neural network (ANN) model. The ANN model trained has great competence for making prediction of hydrogen purification performance of PSA cycle with impressive speed and rational accuracy. On the strength of the ANN model, we implemented an optimization for seeking first-rank PSA cycle parameters. The optimization is concentrated on the effect of inlet flow rate, pressure and layer ratio of activated carbon height to zeolite height. Furthermore, this paper shows that the PSA cycle's optimal operation parameters can be obtained by use of ANN model and optimization algorithm, the ANN model has been trained according to the data generated by Aspen adsorption model. |
| |
| Keywords: | Hydrogen purification Breakthrough curve Pressure swing adsorption Layered bed Artificial neural network Machine learning |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
