A novel adsorbent, UTSA-16, was combined with activated carbon and zeolite 5 A to composed a three-layered bed for hydrogen purification. The adsorption isotherms and breakthrough curves of three-layered bed for a typical steam-methane reformer (SMR) off-gas (H₂/CH₄/CO/CO₂ = 76/3/4/17 mol%) were simulated and validated. Pressure swing adsorption (PSA) cycle models were developed and analyzed to investigate the purification performances of three different combinations of adsorbents. The results showed that UTSA-16, position far from the inlet, achieved a significantly higher hydrogen purity of 99.99%, with the hydrogen recovery of 62.08% and a hydrogen productivity of 7.2209 mol/(kg·h). To explore a better optimization solution, this work introduces a heuristic algorithm, specifically the genetic algorithm (GA), to optimize the structure of the back propagation neural network (BPNN). Two machine learning models, BPNN and back propagation neural network-genetic algorithm (BPNN-GA), were used to predict hydrogen production. The results were compared above all, the BPNN model has a test error of 0.0513, which is larger than the BPNN-GA model’s error of 0.0173. This indicates that the BPNN-GA model performs better for PSA cycle optimization and prediction. The correlation coefficient (R) between the targets of Aspen model and the predicted outputs are close to 1, which showed good accuracy and performance of BPNN-GA model. In conclusion, the BPNN-GA model allows for precise the determination of the optimal operational parameters for achieving optimal performance in the PSA cycle.

In the field of high-end equipment, the synergistic effect of friction-reducing agents plays an important role in the performance study of gel grease. Exploring its tribological and rheological properties can not only significantly reduce the coefficient of friction of mechanical components and enhance its viscosity at high temperatures but also effectively reduce energy consumption, thus improving the service life of high-end equipment. In this study, Schaeffler Load 460 gel grease was mixed with polysiloxane viscosity modifier (PV611) and molybdenum dialkyl dithiocarbamate (RFM3000) according to (3:1, 1:1, and 1:3), and its tribological properties and rheological properties were investigated by the MRS-10G friction and wear tester, MCR302 rotational rheometer, and crossover test. Comparative analyses of tribological and rheological properties were carried out. The results showed that the average coefficient of friction of Schaeffler Load 460 grease was reduced by 57.2%, 60%, and 71.9%, respectively, with the addition of two different ratios of friction reducers; the average diameter of abrasive spots was reduced by 44.5%, 55.4%, and 61.3%; and the shear stress and viscosity were increased by 117.94 Pa and 1295.02 mPa∙s, respectively, compared with that of the original grease, which is a good example for the lubrication of gel grease in the high-end equipment industry. This study provides a new direction and idea for the lubrication research of gel grease in the high-end equipment industry.

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.

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.