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The actual combustion rate of pulverized coal in the blast furnace tuyere is hard to be measured. In this research, the combustion rate of pulverized coal injected into oxygen blast furnace was obtained by a new equipment. This equipment can simulate the actual blast furnace well, and the relationship between pulverized coal injection (PCI) ratio and AO/C was established by mathematical deduction. The experimental results show that the best combustibility of the four pulverized coals is C, and when the coal injection ratio is 350 kg/tHM, the combustion rate can be reached 79%, while the combustion rate of B in the same case is only 45.6%. With the increase of AO/C , the relative amount of oxygen to coal increases, the combustion conditions become better, and combustion rate of the pulverized coal increases. In addition, under the condition of high temperature and rapid combustion, with the increase of coal’s volatile, the combustion rate increases and the corresponding PCI ratio is also increased. By using the new equipment, the unburned coal under the oxygen blast furnace conditions can be collected for further study.

In recent years, distributed fiber optic sensing (DFOS) has been widely used for structural deformation monitoring. Conventional deformation monitoring methods using DFOS have a limitation in that they are based on the conventional assumptions in continuum mechanics such as uniformity and continuity. These methods are unsuitable for cracked concrete structures that exhibit discontinuity (discrete body). This paper proposes an advanced neural network method that can accurately measure the deformation of cracked concrete structures. The basic principle of the method is introduced, and its performance is validated by conducting a concrete beam loading test. Subsequently, the deformation calculation results obtained using the proposed method are compared with those obtained using the conventional conjugate beam method. The two methods could accurately estimate the deformation of the concrete beam before crack formation. The proposed neural network method could accurately determine the deformation with a maximum error of only 4.3% at all loading levels even after crack formation, whereas the conventional method exhibited a maximum error of 33.6% in this stage. The test results confirm the advantages of the proposed method in making accurate estimations of the deformation in concrete structures with cracks.