Micromixing injection combustion is an effective low-NO x combustion technology, but a high wall temperature near the burner nozzle exit is detrimental to the nozzle and will affect burner safety in this technology. In this paper, CO 2 dilution and micromixing injection combustion were combined to try to reduce NO x emissions and lower the wall temperature near the nozzle exit. Several experimental results for flames with a thermal power of 20 kW are reported as follows: the wall temperature near the nozzle can be lowered effectively by CO 2 dilution combustion. The more diluent there is, the lower the wall temperature near the nozzle exit can be. Both low NO x and CO emissions can be achieved at the same time. By adjusting the combustion parameters, NO x emissions can be reduced to 2 ppm (15% O 2 ), and the CO emissions can be reduced to 3 ppm (15% O 2 ). In terms of the relationship between NO x and CO emissions and diluents, more diluents can lead to lower NO x emissions but can increase the CO emission. The hydrocarbon ratio of the diluted fuel can affect the wall temperature and the NO x and CO emissions. When the C/H ratio is high, the wall temperature of the nozzle is high and the NO x and the CO emissions increase.

In order to further solve the problems in promoting the classification of media content in colleges and universities, the effective analysis and understanding of multimedia data content can be better realized based on the characteristics of multimedia data in colleges and universities, combining with the characteristics of rich information, large differences in performance, and large amount of large-scale data. This essay mainly introduces the technology of university media content detection and classification based on information fusion algorithm and focuses on the application of university multimedia content detection, analysis, and understanding, to explore the image discrimination auxiliary attribute feature learning and content association prediction and classification. A benchmark model for media content detection and classification is constructed. Through the model test, it is found that the F 1 value of the model is more than 70%, the check rate is more than 80%, and the recall rate is more than 50%. On this basis, a content detection system based on campus network is constructed.