A finite time thermodynamic (FTT) model of HI decomposition membrane reactor under different sweep modes is established. The sweep flow rate, reaction inlet pressure, permeable membrane thickness and reactor length are taken as decision variables, and the multi-objective optimization is carried out to maximize HI conversion rate, H₂ recovery rate and total entropy generation rate. It is found that the HI conversion rate and H₂ recovery rate are consistent to some extent within a given range of decision variables, but they cannot reach the optimum with the total entropy generation rate at the same time. Compared with the co-current sweep mode, the target values of Pareto front have higher HI conversion and H₂ recovery in the countercurrent sweep mode. Different decision methods are used to select the optimal solution. TOPSIS decision point in cocurrent mode and LINMAP decision point in counter-current mode had smaller deviation factors and could be used as the optimal solution for reactor parameter design.

Based on the quantum law of corresponding states and considering the structural differences between hydrogen isomers, a mathematical model was developed for predicting the transport parameters of low-temperature orthohydrogen and parahydrogen. The results were examined and analyzed, and it was found that the quantum correspondence state principle method could predict the viscosity and thermal conductivity of orthohydrogen and parahydrogen in the temperature range of 20∼100 K and pressure range of 0.01∼10 MPa with good accuracy. The pressure has a significant impact on the prediction accuracy of the model, and the prediction error of the correspondence state principle is basically controlled within 6% when the ambient pressure is less than 1 MPa. Further correction of the physical constants in the model is expected to improve the prediction accuracy of the correspondence state principle.

Based on the adaptive demand of the defrosting control method, the degradation of heating capacity (DHC) method was developed in this work to identify the frosty state, and the defrosting effect was evaluated adopting a fully connected neural network (FNN) classification model. Results indicated that in the monitoring case of the ASHP system, the proposed DHC method can effectively identify the frosty state, and the defrosting effect recognition accuracy achieved 91.3% for the trained FNN classification model in the testing data set. Compared with the original defrosting control method, the defrosting frequency, heating loss and power consumption were respectively reduced by 66.3%, 1775 MJ and 1829 MJ, and the SCOP was increased by 8.6% throughout the heating season. The promising results in this work will provide an innovative approach for the implementation and optimization of the defrosting control strategy of the ASHP system in practical operation.

Based on a quasi-steady-state simulation model, a method to optimize the cooling-down rate of an auto-cascade refrigeration system operating with a rectifying column and multi-component mixed refrigerant is proposed in the paper. Targeted on the maximum cooling capacity, the optimal suction pressure-evaporating temperature curve for a specified mixed refrigerant concentration can be obtained, which can be matched by the sectional suction pressure adjustment in the practical applications to reduce the overall cooling-down time, and the concentration of mixed refrigerant can be optimized to further improve the cooling-down rate based on this way. The fastest overall cooling-down time is achieved using the R50/R1150/R290/R600a as the mixed refrigerant (0.35/0.25/0.15/0.25 by mole) and corresponding shifting parameters (including two shifting temperatures −20°C and −105°C) and three shifting suction pressure 700 kPa, 600 kPa and 550 kPa) for the sectional suction pressure adjustment, when the air temperature in the test chamber is dropped from 20°C to −100°C. Calculation results also show that the optimal concentrations are different for different evaporating temperatures, and the cooling-down rate can be improved further if the concentration of the mixed refrigerant can be adjusted during the cooling-down process. The optimization methods used in this paper can also provide references for researches on cooling-down processes of other refrigeration systems using multi-component mixed refrigerants.

The optimal cogeneration share coefficient can guide the capacity configuration of cogeneration units and maximize the economic benefits of cogeneration. In this paper, a method for determining the techno-economic optimal cogeneration share coefficient considering the energy consumption characteristics of variable load conditions is proposed, the off-design working condition analysis model of cogeneration system is developed, and the variable load energy consumption characteristics of the case unit are calculated. The techno-economic cogeneration share coefficient is optimized. The results show that the optimal techno-economic heating coefficients are 0.750 and 0.702 respectively when considering and ignoring the energy consumption characteristics of variable load conditions, and the corresponding annual cost saving relative error without considering characteristics of variable load conditions reaches 22.0%. More accurate techno-economic benefits of cogeneration will be obtained by considering the energy consumption characteristics of variable load conditions.