This paper presents a numerical simulation and structural optimization study of the combustion process within the grate and boiler furnace of a 750 t/d waste incineration. The study focuses on adjusting the secondary air velocity, secondary air inclination angle, and the arrangement of secondary air nozzles. These adjustments aim to optimize parameters such as the temperature field, pollutant emission, flow field, particle residence time, and filling degree. The findings demonstrate that high-temperature zones, which lead to slagging problems, are likely to form beneath the front arch. The combustibles inside the furnace are thoroughly burnt, reflecting efficient combustion. The concentration of NOx in the flue gas at the furnace outlet generally ranges between 170 and 200 ppm. Optimal operating conditions are identified as a secondary air inclination angle of 20° with an air velocity of 55 m/s, and an angle of 30° with an air velocity of 55 m/s and 65 m/s, in conjunction with a relative arrangement of the nozzles. Under these conditions, the incineration furnace achieves its best operational state.

The Alpha Magnetic Spectrometer (AMS) is a high-precision particle detector deployed on the International Space Station (ISS) to look for the origin of dark matter, antimatter's existence, and the origin and features of cosmic rays. Analyzing and forecasting the thermal status of electric equipment of AMS is of great significance to ensure that they operate within acceptable temperature limits. In this study, the orbital parameters of the AMS and thermal data of the main radiators are analyzed. Artificial neural network models are established for predicting the temperatures of AMS in orbit under the ISS normal and special operating conditions. The mean squared errors (MSE) of the predictions after the model training show that the established neural network models can accurately predict AMS temperatures. Comparison results between the recorded telemetry data and the predicted temperature obtained from the established neural network models show that the proposed models are precise enough for predicting AMS temperatures with the minimum MSE being 0.006. This work offers a reference for the thermal control of AMS and other spacecraft in orbit.