Optimal design of heat pipes for city gate station heaters by applying genetic and Bayesian optimization algorithms to an artificial neural network model

High purity hydrogen is a necessary need for fuel cell. Pressure swing adsorption (PSA) technology is one of the effective methods for hydrogen purification. The layered bed PSA model is built and validated. To simplify the calculation of PSA purification performance, the quadratic regression equations are obtained by Box-Behnken design (BBD) method.With adsorption time, pressure equalization time and feed flow rate from PSA process as independent optimization parameters of the BBD method, the hydrogen purity and productivity as two responses. The genetic algorithm (GA) is introduced to the back propagation neural network (BPNN) to solve the optimization problem of the PSA process. In order to explore the performance of optimization algorithms, a novel optimization method is proposed in this work. With the BBD method is integrated with BPNN-GA model to optimize the structure (BBD-BPNN-GA). The results showed that the BBD-BPNN-GA model have a better performance with the MSE of 0.0005, while the mean square error (MSE) of BPNN-GA model is 0.0035. And the correlation coefficient of R-values are much closer to 1of the BBD-BPNN-GA model, which is illustrated that the BBD-BPNN-GA model can be effectively applied to the prediction and optimization of PSA process.

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A Novel Optimization Method Using the Box–Behnken Design Integrated with a Back Propagation Neural Network–Genetic Algorithm for Hydrogen Purification

Zhang,

Hu,

Xin

2024

Applied Sciences

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Pressure swing adsorption (PSA) technology is among the most efficient techniques for purifying and separating hydrogen. A layered adsorption bed composed of activated carbon and zeolite 5A for a gas mixture (H2: 56.4 mol%, CH4: 26.6 mol%, CO: 8.4 mol%, N2: 5.5 mol%, CO2: 3.1 mol%) PSA model was built. The simulation model was validated using breakthrough curves. Then, a six-step PSA cycle model was built, and the purification performance was studied. The Box–Behnken design (BBD) method was utilized in Design Expert software (version 10) to optimize the PSA purification performance. The independent optimization parameters included the adsorption time, the pressure equalization time, and the feed flow rate. Quadratic regression models can be derived to represent the responses of purity and productivity. To explore a better optimization solution, a novel optimization method using machine learning with a back propagation neural network (BPNN) was then proposed, and a kind of heuristic algorithm–genetic algorithm (GA) was introduced to enhance the architecture of the BPNN. The predicted outputs of hydrogen production using two kinds of models based on the BPNN–GA and the BBD method integrated with the BPNN–GA (BBD–BPNN–GA). The findings revealed that the BBD–BPNN–GA model exhibited a mean square error (MSE) of 0.0005, with its R–value correlation coefficient being much closer to 1, while the BPNN–GA model exhibited an MSE of 0.0035. This suggests that the BBD–BPNN–GA model has a better performance, as evidenced by the lower MSE and higher correlation coefficient compared to the BPNN–GA model.

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Optimal design of heat pipes for city gate station heaters by applying genetic and Bayesian optimization algorithms to an artificial neural network model

Cited by 4 publications

References 23 publications

Thermal performance analysis and optimization of a heat pipe-based electronic thermal management system using experimental data-driven neuro-genetic technique

Thermal performance analysis and optimization of a heat pipe-based electronic thermal management system using experimental data-driven neuro-genetic technique

A novel optimization method via Box-Behnken Design integrated with back propagation neural network - genetic algorithm on hydrogen purification

A Novel Optimization Method Using the Box–Behnken Design Integrated with a Back Propagation Neural Network–Genetic Algorithm for Hydrogen Purification

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