本溪钢铁（集团）有限责任公司简称本钢，始建于1905年，位于辽宁省本溪市，距省会沈阳63公里处。拥有总资产" 468.6亿元，净资产222亿元。在岗职工61897人。改革开放30年，本钢发展了巨大变化，特别从党的十五大到十七大的十年间，实现了跨越式发展，成为集采矿、选矿、烧结、焦化、炼铁、炼钢、轧钢、动力、运输、科研、机械加工制造、房地产开发等为一体的配套齐全的国有特大型钢铁联合企业。
In the modern steelmaking, the impact behaviour of supersonic oxygen jet on the molten bath is vital importance for the refining process. The impact characteristics and flow field distribution of molten bath for swirl-type oxygen lance in 260 Mg converter were analyzed by physical experiments and numerical simulations, and industrial test research was conducted. These results demonstrate that compared to the traditional oxygen lance, the cavity depth, cavity width and mixing time of swirl-type oxygen lance decrease, increase and decrease, respectively. The mixing efficiency of molten bath for swirl-type oxygen lance is improved by 5.5% to 16.8%. The optimal operation parameters of swirl-type oxygen lance for 260 Mg converter are H=40de and Q=80 Nm3·h-1. When the swirl angle is 10 °, the molten bath obtains the maximum volume-average velocity, the minimum dead zone volume and the shortest mixing time. Industrial test shown that compared with traditional oxygen lance, the swirl-type oxygen lance increased the dephosphorization rate by 2.3%, decreased the final carbon content by 0.005%, and decreased the iron content of final slag by 0.11%. The findings of this work can provide a basic reference for the industrial application of swirl-type oxygen lance in large-scale converter.
In cold rolling production, rolling force is the decisive parameter that causes the strip to undergo plastic deformation, and its setting accuracy directly affects the thickness accuracy, plate shape quality and edge drop control effect of products. However, traditional rolling force models rely on a large number of simplified assumptions, resulting in limited calculation accuracy. To overcome the above problems, this study proposes a rolling force prediction and interpretable method based on industrial big data for cold rolling. This method first develops a cross-process industrial IoT platform to integrate key process data of hot-cold rolling lines and provide a complete industrial dataset for model training. Then, an adaptive inertia weight strategy is introduced to improve the whale optimization algorithm (IWOA), and the hyperparameters of the stochastic configuration network (SCN) are optimized to construct a rolling force prediction model. Finally, the SHAP method is used to analyse the characteristic contribution of each input feature of the hot-cold rolling line to the rolling force prediction results, thereby enhancing the interpretability of the rolling force prediction process. The experimental results demonstrate that compared with baseline models or other advanced models in the rolling field, the proposed IWOA-SCN exhibits the best prediction accuracy and generalization ability in rolling force prediction. In industrial experiments, the proposed model significantly reduces the rolling force prediction deviation, further verifying the accuracy and adaptability of the method under complex working conditions, and providing strong support for achieving precise process control and improving product quality in cold rolling production.
