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An optimal prediction model for flow boiling heat transfer of refrigerant mixture R245fa inside horizontal smooth tubes is proposed based on the GRNN neural network. The main factors strongly affecting flow boiling such as mass flux rate (G), heat flux (q), quality of vapor-liquid mixture (x), evaporation temperature (Tev), and tube inner diameter (D) are used as the inputs of the model and the flow boiling heat transfer coefficient (h) as the output. Neural network model is used to optimize the prediction of flow boiling heat transfer coefficient of R245fa in horizontal light pipe through training and learning. The prediction results are in good agreement with the experimental results. For the network model of heat transfer, the average deviation is 7.59%, the absolute average deviation is 4.89%, and the root mean square deviation is 10.51%. The optimized prediction accuracy of flow boiling heat transfer coefficient is significantly improved compared with four frequently used conventional correlations. The simulation results reveal that the modeling method based on R245fa neural network is feasible to calculate the flow boiling heat transfer coefficient, and it may provide some guidelines for the optimization design of tube evaporators for R245fa.

With the continuous development of China’s economy, the phenomenon of energy scarcity has become more and more prominent, for which China has put forward the strategic goal of carbon peak and carbon neutrality (double carbon target). As densely populated areas, the demand for energy is especially tight in universities. In combination with the work of “conservation-oriented colleges” carried out by the Ministry of Education, the accurate monthly electrical and water energy consumption of Kunming University of Science and Technology from 2018–2021 was counted, and the data were plotted into an energy consumption analysis chart to determine its compliance with the prediction range of the smoothing index prediction model. The corresponding smoothing indices were calculated by writing smoothing formulas through Excel, and, finally, the overall energy consumption indexes for 2022 and 2023 were successfully predicted using the exponential smoothing method. The errors between the real and forecasted values of electricity and water consumption in 2021 are reduced to 2.61% and 2.44%. The smoothing index predicts that the baseline discounted electricity energy consumption in 2022 is 5,423,658.235 kgce and in 2023 is 5,758,865.224 kgce; on the other hand, the baseline discounted water energy consumption in 2022 is predicted to be 632,654.321 kgce, while in 2023 it is predicted to be 652,321.238 kgce. The projected values can be used as an early warning line for the energy consumption index, and long-term management approaches and data support for energy conservation and carbon emission reduction can be effectively provided. The mentioned research provides an important reference for the proposal and implementation of efficient management measures, and provides strong theoretical technical support for the implementation of the carbon peak and neutrality in universities.

There exist complex chemical reactions and phase transition processes—melting and solidification—in the iron ore sintering process. This will result in bed shrinkage and has an important impact on the sintering process and sinter properties. Numerical simulation was carried out in this paper, and it is based on the porous medium and double-energy equation model, coupled with the porosity shrinkage model. The influence of bed shrinkage on the sintering process was explored and revealed by comparing the calculation results, considering and neglecting bed shrinkage. The results indicate that bed shrinkage has less influence on the highest temperature in the area above 0.15 m of the sintered bed but it severely decreases by about 60 K below 0.15 m. Bed shrinkage makes the thickness of the combustion zone reduce by about 1.367, 1.398, and 4.367 cm at 500, 1000, and 1500 s, respectively. Bed shrinkage reduces the vertical sintering velocity, and the highest temperature of flue gas decreased by 77 K approximately; in addition, it also has a delay of 50 s to reach the highest temperature. The volume concentration of SO2 in the flue gas remains unchanged considering bed shrinkage, but it has a delay of about 50 s to reach the maximum value.