Effective abatement of the greenhouse gas sulphur hexafluoride (SF 6 ) waste is of great importance for the environment protection. This work investigates the size effect and the surface properties of γ-Al 2 O 3 pellets on SF 6 degradation in a packed bed dielectric barrier discharge (PB-DBD) system. Experimental results show that decreasing the packing size improves the filamentary discharges and promotes the ignition and the maintenance of plasma, enhancing the degradation performance at low input powers. However, too small packing pellets decrease the gas residence time and reduce the degradation efficiency, especially for the input power beyond 80 W. Besides, lowering the packing size promotes the generation of SO 2 , while reduces the yields of S-O-F products, corresponding to a better degradation. After the discharge, the pellet surface becomes smoother with the appearance of S and F elements. Density functional theory calculations show that SF 6 is likely to be adsorbed at the Al III site over the γ-Al 2 O 3 (110) surface, and it is much more easily to decompose than in the gas phase. The fluorine gaseous products can decompose and stably adsorb on the pellet surface to change the surface element composition. This work provides a better understanding of SF 6 degradation in a PB-DBD system.
In the realm of high-dimensional data analysis, numerous fields stand to benefit from its applications, including the biological and medical sectors that are crucial for computer-aided disease diagnosis and prediction systems. However, the presence of a significant number of redundant or irrelevant features can adversely affect system accuracy and real-time diagnosis efficiency. To mitigate this issue, this paper proposes two innovative wrapper feature selection (FS) methods that integrate the ant colony optimization (ACO) algorithm and hybrid rice optimization (HRO). HRO is a recently developed metaheuristic that mimics the breeding process of the three-line hybrid rice, which is yet to be thoroughly explored in the context of solving high-dimensional FS problems. In the first hybridization, ACO is embedded as an evolutionary operator within HRO and updated alternately with it. In the second form of hybridization, two subpopulations evolve independently, sharing the local search results to assist individual updating. In the initial stage preceding hybridization, a problem-oriented heuristic factor assignment strategy based on the importance of the knee point feature is introduced to enhance the global search capability of ACO in identifying the smallest and most representative features. The performance of the proposed algorithms is evaluated on fourteen high-dimensional biomedical datasets and compared with other recently advanced FS methods. Experimental results suggest that the proposed methods are efficient and computationally robust, exhibiting superior performance compared to the other algorithms involved in this study.
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