Heat exchangers (HX) are often utilized in industry, and the optimization of the performance of HX is a key area of research. In this study, EVAP-COND software 4.0 and genetic algorithm (GA) based optimization methods were proposed to optimize the circuitry and fin pitch of a finned tube heat exchanger for an air conditioner. A simulation model for a multi-circuit finned-tube evaporator used in an air conditioning unit was developed using the EVAP-COND software, and further validated based on the experimental data. Considering the refrigerant flow maldistribution of the original HX, four different circuit arrangements, i.e., types A, B, C, and D, were designed and optimized circuitry obtained. Based on both simulation and experimental results, D-type HX with 1.8 mm fin pitch was selected as 10% tubes could be saved with no significant loss of heat transfer capacity. Then the fin pitch was further optimized using the multi-objective GA method, with both Colburn factor j and friction factor f being considered. Optimization results showed that, in Pareto front, points 1 to 4 showed the increase in the Colburn factor j was negative, while the decrease in the friction factor f was positive. The friction factor decreased by 3.5% as one moved from Point 1 to Point 4, but the Colburn factor rose by 1.02%. Points 5 to 10 demonstrated that, while the decrease in the friction factor was negative, the increase in the Colburn factor was positive. The friction factor decreased by 5.31%, but the Colburn factor increased by 1.51% when going from Point 5 to Point 10. The results of optimization demonstrated that the objective function performed at its optimum when the fin pitch was around 1.77 mm.

Centrifugal chillers have been widely used in medium- and large-scale air conditioning projects. However, equipment running with faults will result in additional energy consumption. Meanwhile, it is difficult to diagnose the minor faults of the equipment. Therefore, the Extreme Gradient Boost (XGBoost) algorithm was used to solve the above problem in this article. The ASHRAE RP-1043 dataset was employed for research, utilizing the feature splitting principle of XGBoost to reduce the data dimension to 23 dimensions. Subsequently, the five important parameters of the XGBoost algorithm were optimized using Multi-swarm Cooperative Particle Swarm Optimization (MSPSO). The minor fault diagnosis model, MSPSO-XGBoost, was established. The results show that the ability of the proposed MSPSO-XGBoost model to diagnose eight different states is uniform, and the diagnostic accuracy of the model reaches 99.67%. The accuracy rate is significantly improved compared to that of the support vector machine (SVM) and back propagation neural network (BPNN) diagnostic models.

Sintering behaviour of ash specimens from blends of Jincheng (JC) coal and pine sawdust (PS) was investigated using a pressure-drop sintering device combined with scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), X-ray diffraction (XRD) analysis, and FactSage modeling. The sintering temperatures consistently displayed a decreased trend when the PS was added into JC. In addition, the ashing temperature had an important influence on the sintering temperature of ash. The SEM analysis revealed a fine, irregular, and fibrous texture in the ash specimens from the lower ashing temperature. Regular block-shape particles were present in the ash from the higher ashing temperature. The EDS results showed that more Ca and Fe appeared with the increase of ashing temperature and more K and Na appeared, while Ca and Fe decreased with the addition of PS. The XRD analysis revealed that low-temperature minerals melted or co-fused with 10% PS addition into coal, while the high-temperature minerals formed with increased ashing temperature. The FactSage calculation indicated that the alkali and alkali earth metal species mainly existed in the forms of aluminosilicates and silicates. Calcium, magnesium, and potassium played a vital role during the sintering of the blends with PS.