Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine Learning
We propose efficient multiple machine learning (ML) models using specifically polynomial and logistic regression ML methods to predict the optimal design of proton exchange membrane (PEM) electrolyzer cells. The models predict eleven different parameters of the cell components for four different inp...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/18/6657 |
| _version_ | 1797488845842808832 |
|---|---|
| author | Amira Mohamed Hatem Ibrahem Rui Yang Kibum Kim |
| author_facet | Amira Mohamed Hatem Ibrahem Rui Yang Kibum Kim |
| author_sort | Amira Mohamed |
| collection | DOAJ |
| description | We propose efficient multiple machine learning (ML) models using specifically polynomial and logistic regression ML methods to predict the optimal design of proton exchange membrane (PEM) electrolyzer cells. The models predict eleven different parameters of the cell components for four different input parameters such as hydrogen production rate, cathode area, anode area, and the type of cell design (e.g., single or bipolar). The models fit well as we trained multiple machine learning models on 148 samples and validated the model performance on a test set of 16 samples. The average accuracy of the classification model and the mean absolute error is 83.6% and 6.825, respectively, which indicates that the proposed technique performs very well. We also measured the hydrogen production rate using a custom-made PEM electrolyzer cell fabricated based on the predicted parameters and compared it to the simulation result. Both results are in excellent agreement and within a negligible experimental uncertainty (i.e., a mean absolute error of 0.615). Finally, optimal PEM electrolyzer cells for commercial-scaled hydrogen production rates ranging from 500 to 5000 mL/min were designed using the machine learning models. To the best of our knowledge, we are the first group to model the PEM design problem with such large parameter predictions using machine learning with those specific input parameters. This study opens the route for providing a form of technology that can greatly save the cost and time required to develop water electrolyzer cells for future hydrogen production. |
| first_indexed | 2024-03-10T00:09:08Z |
| format | Article |
| id | doaj.art-68417e08a0644d5bba5366a8d35003ba |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T00:09:08Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-68417e08a0644d5bba5366a8d35003ba2023-11-23T16:03:33ZengMDPI AGEnergies1996-10732022-09-011518665710.3390/en15186657Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine LearningAmira Mohamed0Hatem Ibrahem1Rui Yang2Kibum Kim3School of Mechanical Engineering, Chungbuk National University, Cheongju 28644, KoreaSchool of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, KoreaSchool of Mechanical Engineering, Chungbuk National University, Cheongju 28644, KoreaSchool of Mechanical Engineering, Chungbuk National University, Cheongju 28644, KoreaWe propose efficient multiple machine learning (ML) models using specifically polynomial and logistic regression ML methods to predict the optimal design of proton exchange membrane (PEM) electrolyzer cells. The models predict eleven different parameters of the cell components for four different input parameters such as hydrogen production rate, cathode area, anode area, and the type of cell design (e.g., single or bipolar). The models fit well as we trained multiple machine learning models on 148 samples and validated the model performance on a test set of 16 samples. The average accuracy of the classification model and the mean absolute error is 83.6% and 6.825, respectively, which indicates that the proposed technique performs very well. We also measured the hydrogen production rate using a custom-made PEM electrolyzer cell fabricated based on the predicted parameters and compared it to the simulation result. Both results are in excellent agreement and within a negligible experimental uncertainty (i.e., a mean absolute error of 0.615). Finally, optimal PEM electrolyzer cells for commercial-scaled hydrogen production rates ranging from 500 to 5000 mL/min were designed using the machine learning models. To the best of our knowledge, we are the first group to model the PEM design problem with such large parameter predictions using machine learning with those specific input parameters. This study opens the route for providing a form of technology that can greatly save the cost and time required to develop water electrolyzer cells for future hydrogen production.https://www.mdpi.com/1996-1073/15/18/6657PEM water electrolysismachine learningcell designhydrogen production |
| spellingShingle | Amira Mohamed Hatem Ibrahem Rui Yang Kibum Kim Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine Learning Energies PEM water electrolysis machine learning cell design hydrogen production |
| title | Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine Learning |
| title_full | Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine Learning |
| title_fullStr | Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine Learning |
| title_full_unstemmed | Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine Learning |
| title_short | Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine Learning |
| title_sort | optimization of proton exchange membrane electrolyzer cell design using machine learning |
| topic | PEM water electrolysis machine learning cell design hydrogen production |
| url | https://www.mdpi.com/1996-1073/15/18/6657 |
| work_keys_str_mv | AT amiramohamed optimizationofprotonexchangemembraneelectrolyzercelldesignusingmachinelearning AT hatemibrahem optimizationofprotonexchangemembraneelectrolyzercelldesignusingmachinelearning AT ruiyang optimizationofprotonexchangemembraneelectrolyzercelldesignusingmachinelearning AT kibumkim optimizationofprotonexchangemembraneelectrolyzercelldesignusingmachinelearning |