本文利用BP神经网络建立烧结矿化学成分的预报模型。
Prediction models of in sintering process based on BP neural network is proposed to judge the trend of the chemical composition.
烧结过程控制在总体上可分解为烧结矿化学成分的控制、烧结过程状态的控制和烧结能耗的控制三个方面。
Generally, sintering process control consists of sinter chemical composition control, sintering process state control and sintering energy consumption control.
此系统方案的建立，克服了烧结原料品种变换频繁带来的质量波动，稳定了混匀料和烧结矿的化学成分。
This system project can overcome the quality fluctuation caused by the changes in the sinter material and stabilize the chemical component of the blending material and sintering material.
通过烧结杯试验，找出了宣钢原料条件下烧结矿合理化学成分、烧结矿低硅条件下各种化学成分的控制范围。
With sintering pot test it is found out the reasonable chemical component in sinter under the condition of Xuan Steel as well as the control range of various components for low-silicon sinter.
文章通过对包钢生产烧结矿质量检测数据的统计分析，研究了包钢烧结矿的化学成分和矿物组成对其冶金性能的影响，提出了包钢烧结矿质量指标的适宜范围。
Based on the testing data of the quality of sinter, the influences of chemical composition and mineral composition on metallurgical properties of the sinter are investigated.
文章通过对包钢生产烧结矿质量检测数据的统计分析，研究了包钢烧结矿的化学成分和矿物组成对其冶金性能的影响，提出了包钢烧结矿质量指标的适宜范围。
Based on the testing data of the quality of sinter, the influences of chemical composition and mineral composition on metallurgical properties of the sinter are investigated.
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