The production of ferroalloys necessitates the consumption of substantial quantities of alloy materials. Achieving the national "dual carbon" strategic goals and reducing energy consumption in the steel industry necessitates the implementation of scientific and practical methods and approaches for ferroalloy charging. The objective of alloy reduction technology in the steelmaking alloying process is twofold: first, to minimize the use of alloying elements, and second, to reduce production costs, while ensuring that the final steel retains the required properties and characteristics. The present paper introduces the physicochemical properties of ferroalloys and employs drum tests to quantitatively evaluate their pulverization performance. During handling, alloys should be stored in tiered arrangements based on particle size and density to ensure absorption rates. It is imperative to mitigate the occurrence of collisions during storage, transportation, and utilization to avert pulverization losses prior to furnace entry. An intelligent control system for alloy reduction in steelmaking, developed using neural networks and big data models, has been successfully implemented in over ten domestic steel enterprises. The substitution of customized alloy recycling plans, derived from field operation data and process analysis, has been demonstrated to reduce ferroalloy usage costs for steel producers. In the process of smelting particular steel grades, it is imperative to exercise caution with regard to the presence of deleterious elements within the alloy. Concurrently, precise selection should be made based on changes in the main alloy components to reduce cost increases caused by fluctuations in alloy composition. By analyzing current alloy reduction technologies in steelmaking, this study proposes future improvement directions and trends for ferroalloy reduction methods. Initial efforts must concentrate on the enhancement of ferroalloy quality, with the objective of reducing the usage of superfluous alloy elements and averting the squandering of resources. Secondly, the advancement of digitalization and automation technologies has the potential to enhance the stability and controllability of steelmaking operations by enabling the monitoring and control of the alloying process.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |