利用神经网络和热力学计算设计铜合金熔炼过程中助熔剂成分，以达到抑制难熔腐蚀和加速MnO溶解到助熔剂中的良好平衡

Journal of the Japan Institute of Metals and Materials Pub Date : 2021-07-01 DOI:10.2320/JINSTMET.J2020063

Itaru Hasegawa, T. Koizumi, K. Kita, Masanori Suzuki, Toshihiro Tanaka

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引用次数: 0

摘要

提出了一种设计铜合金熔炼熔剂的新方法，使熔剂既能抑制难熔材料的腐蚀，又能促进MnO在熔剂中的溶解。在本研究中，采用神经网络计算方法对耐火材料的熔剂腐蚀进行了评估，预测的耐火材料腐蚀量与实验数据吻合较好。为了评价MnO在助熔剂中溶解的相关性质，采用热力学分析方法考察了助熔剂的粘度和助熔剂中MnO的活性。综合以上评价结果，提出了一种高效的液流组成设计方法。作为该方法的应用实例，在使用Al 2o3耐火材料时，发现sio2 - 55mass % na2o助熔剂是最佳助熔剂。[doi:10.2320 / jinstmet.]J2020063]

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Designing of Flux Composition in Copper Alloy Melting Process to Achieve Good Balance between Suppression of Refractory Corrosion and Acceleration of MnO Dissolution into Flux Using Neural Network and Thermodynamic Computation

A new method of designing a ﬂ ux for the copper alloy melting process that can achieve a good balance between the suppression of refractory corrosion by the ﬂ ux and the acceleration of MnO dissolution into the ﬂ ux was proposed. In this study, NN ( neural network ) computation was used to evaluate the refractory corrosion by the ﬂ ux, and the predicted amounts of corrosion of refractories were in good agreement with experimental data. For the evaluation of the properties related to the MnO dissolution into the ﬂ ux, both the viscosity of ﬂ uxes and the activity of MnO in ﬂ uxes were examined using thermodynamic analysis. By integrating the results of the above evaluations, an e ﬃ cient method of designing the ﬂ ux composition was devised. As an example of the application for this method, SiO 2 – 55mass ％ Na 2 O ﬂ ux was found to be the optimal ﬂ ux when Al 2 O 3 refractory was employed. ［ doi:10.2320 / jinstmet.J2020063 ］

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Journal of the Japan Institute of Metals and Materials

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