## 1 Introduction
### Previous Work
They also optimized parameters such as gas pressure, line speed, material height, separation distance and gas-ejection elevation angle by using the response surface methodology with FLUENT simulation. [PERSON] et al. [15] used magnetic induction spectroscopy to classify Cu, Al and brass. [PERSON] et al. [16] used spectral and spatial features to classify non-ferrous metals. [PERSON] et al. [6; 33; 34] used a 3D (three-dimensional) laser sensor and weight sensor to identify light scrap such as Alc, Alw and Mg in accordance with multivariate analysis which combined apparent density of materials with other 3D parameters. They also used a neural network to improve the identification accuracy of scrap from several shredder factories, and used portable XRF to conduct element analysis experiments on identified materials to verify the application in the automobile industry. Nevertheless, in this method, the shredder equipment, the shredder process, original hollow or tubular materials and other factors may have a greater impact on the classification accuracy. [PERSON] et al. [35] used the thermal conductivity measurement to identify materials, and carried out experimental verification on Cu, brass, SS and Al. The reported error of identification was less than 5%, and even the measurement error of aluminum thermal conductivity was only 0.56%. In fact, the method is still in the laboratory stage, and it is difficult to apply in engineering. In addition, X-ray dual energy CT (computed tomography) can be seen in medicine or industry, but it has some evident defects, including low speed, low efficiency, complex processing, and high cost [36]. There are also some beneficial attempts in XRT multi-energy fusion [37]. The previous research has provided support for this study. However, the XRT application theories above-mentioned are mostly limited to material identification combined with other sensors; and the identification results are still to be improved. For example, in the field of security inspection, when detecting prohibited explosives, the identification only shows an approximate range of atomic number [24]. On the other hand, many theories have not yet been clarified in detail with respect to thickness removal effect, or lack of feasibility in practical engineering application. For example, (i) the integral coefficient term in Equation (5) or Equation (6) is not easy to obtain accurately; (ii) the SQR algorithm used in Reference [20] is complicated; (iii) the energy selection of the final dual-energy monochromatic R value is empirical; (iv) the XRT of MeV energy is mostly used for large-scale container customs' clearance and security inspections, by which the identification of industrial bulk materials will overconsume the occupation and the cost of protection and equipment; (v) the main problem of dual-energy CT is its relatively slow speed for detection. In short, many existing methods cannot be completely applied to industry due to their severe application conditions. Established theories above-mentioned have not yet made comprehensive focus on industrial applications either, so as to fail to improve identification accuracy and sorting efficiency.