【獲獎(jiǎng)】
2024年下半年寶鋼股份AI應(yīng)用場(chǎng)景成果獎(jiǎng)(寶鋼工作期間)
【代表性學(xué)術(shù)著作、論文】
[1] Gu M, Xu A, Wang H, et al. Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM[J]. Processes, 2021, 9(11): 1987.
[2] Gu M, Xu A, Yuan F, et al. An Improved CBR Model Using Time-series Data for Predicting the End-point of a Converter[J]. ISIJ International, 2021: ISIJINT-2020-687.
[3] Gu M, Xu A, He D, et al. Prediction Model of End-Point Molten Steel Temperature in RH Refining Based on PCA-CBR[C]//11th International Symposium on High-Temperature Metallurgical Processing. Springer, Cham, 2020: 741-755.
[4] 谷茂強(qiáng),徐安軍,劉旋,王慧賢.基于數(shù)據(jù)驅(qū)動(dòng)的轉(zhuǎn)爐二吹階段鋼水溫度動(dòng)態(tài)預(yù)測(cè)模型[J].工程科學(xué)學(xué)報(bào),2022,44(09):1595-1606.
[5] 谷茂強(qiáng),徐安軍,賀東風(fēng),汪紅兵,馮凱.基于案例推理的鋼水溫度在線(xiàn)管控模型[J].鋼鐵研究學(xué)報(bào),2017,29(06):468-473.
[6] Zhang S, Gu M, Zong Y, et al. Improved back propagation neural network method for predicting sulfur content in hot metal[J]. Metallurgical Research & Technology, 2024, 121(1): 103.
[7] Yuan F, Xu A, Gu M. Development of an improved CBR model for predicting steel temperature in ladle furnace refining[J]. International Journal of Minerals, Metallurgy and Materials, 2021, 28(8): 1321-1331.
[8] Liu X, Xu A, Gu M, et al. A multi-task learning prediction model for the endpoint of converter steelmaking based on genetic algorithm optimised[J]. Ironmaking & Steelmaking, 2025, 52(2): 153-164.
[9] 王慧賢,徐安軍,谷茂強(qiáng),等.基于分段式氧氣脫碳效率的轉(zhuǎn)爐供氧量預(yù)測(cè)模型[J].中國(guó)冶金,2023,33(07):91-99.
【專(zhuān)利】
[1]袁飛,谷茂強(qiáng),徐安軍,汪紅兵. 一種轉(zhuǎn)爐后吹碳含量動(dòng)態(tài)預(yù)測(cè)方法及裝置[P]. 北京市:CN113239482B,2022-02-08.
[2]袁飛,谷茂強(qiáng),徐安軍. 一種轉(zhuǎn)爐終點(diǎn)鋼水碳含量與溫度預(yù)測(cè)方法及裝置[P]. 北京市:CN113255102B,2022-02-08.
【主要科研項(xiàng)目】