Version 1
: Received: 23 October 2024 / Approved: 24 October 2024 / Online: 24 October 2024 (10:27:04 CEST)
How to cite:
Zhang, N.; Hu, S.; Xin, Q. A Novel Optimization Method via Box-Behnken Design Integrated with Back Propagation Neural Network – Genetic Algorithm on Hydrogen Purification. Preprints2024, 2024101883. https://doi.org/10.20944/preprints202410.1883.v1
Zhang, N.; Hu, S.; Xin, Q. A Novel Optimization Method via Box-Behnken Design Integrated with Back Propagation Neural Network – Genetic Algorithm on Hydrogen Purification. Preprints 2024, 2024101883. https://doi.org/10.20944/preprints202410.1883.v1
Zhang, N.; Hu, S.; Xin, Q. A Novel Optimization Method via Box-Behnken Design Integrated with Back Propagation Neural Network – Genetic Algorithm on Hydrogen Purification. Preprints2024, 2024101883. https://doi.org/10.20944/preprints202410.1883.v1
APA Style
Zhang, N., Hu, S., & Xin, Q. (2024). A Novel Optimization Method via Box-Behnken Design Integrated with Back Propagation Neural Network – Genetic Algorithm on Hydrogen Purification. Preprints. https://doi.org/10.20944/preprints202410.1883.v1
Chicago/Turabian Style
Zhang, N., Sumeng Hu and Qianqian Xin. 2024 "A Novel Optimization Method via Box-Behnken Design Integrated with Back Propagation Neural Network – Genetic Algorithm on Hydrogen Purification" Preprints. https://doi.org/10.20944/preprints202410.1883.v1
Abstract
High purity hydrogen is a necessary need for fuel cell. Pressure swing adsorption (PSA) technology is one of the effective methods for hydrogen purification and separation. A layered adsorption bed packed with activated carbon and zeolite 5A for five-component gas mixture (H2/CH4/CO/N2/CO2=56.4/26.6/8.4/5.5/3.1 mol%) PSA model was built. The model was validated by breakthrough curves and comparing the results with the experimental data. The purification performance of six-step layered bed PSA cycle was studied using the model. In order to optimize the cycle, the Box-Behnken design (BBD) method was used, as implemented in Design Expert™. In addition to adsorption time, the pressure equalization time and the feed flow rate were considered as independent optimization parameters. Quadratic regression equations were then obtained for three responses of the system, namely purity, recovery, and productivity. To explore a better optimization solution, a novel optimization method of machine learning with back propagation neural network (BPNN) was proposed in this work, a kind of heuristic algorithm of genetic algorithm (GA) is introduced to optimize the structure of BPNN. The predicted outputs of hydrogen production using two kinds of models based on back propagation neural network-genetic algorithm (BPNN-GA) and BBD method integrated with BPNN-GA (BBD-BPNN-GA) models. The results showed that the BBD-BPNN-GA model have a better performance with the mean square error (MSE) of 0.0005, while the MSE of BPNN-GA model was 0.0035. And the correlation coefficient of R-values were much closer to 1 of the BBD-BPNN-GA model, which was illustrated that the BBD-BPNN-GA model can be effectively applied to the prediction and optimization of PSA process.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
