全自动智能烧结机系统在金刚石钻头制造中的应用

为了尽可能消除人为因素对金刚石钻头烧结质量的影响,解决钻头质量不稳定等问题,与湖北长江精工材料技术有限公司合作,设计开发了一种符合我厂特点的人造金刚石地质钻头全自动智能烧结机系统,实现了一键式操作。该智能烧结机系统性能稳定,控温控压精度高,可以完全按照预先编制的钻头烧结工艺曲线进行烧结,解决了多年来钻头质量不稳定的问题。

面向智能烧结的机尾断面烧结矿FeO预测研究

针对烧结工艺FeO预测准度问题,提出一种基于机器学习的预测模型。通过对机尾断面温度数据的采集和处理,建立包含多个特征的数据集。采用基于MIV(平均影响值)的特征选择方法,筛选出对预测模型权重占比较高的特征。使用Bi-LSTM(双向长短时记忆神经网络)算法对生产工艺数据进行训练和测试,得到高精度的预测模型。通过实验验证了该模型的预测效果,并与其他神经网络模型方法进行了比较,比较结果表明该模型具有较高的预测精度和实用性。在企业误差允许的范围内,准确率达90.2%,因此可以为智能烧结技术的应用和烧结质量的控制和优化提供重要的参考。

济钢320 m^2烧结机智能控制系统

简介了济钢第二烧结厂320m2烧结机的工艺流程;详述了此烧结机全过程智能控制系统(简称“SPSS”)以及构成该系统的11个在线控制模型的控制功能及原理。同时,概述了该控制系统在其工艺设备及仪表上的特殊配置;充分肯定了控制系统对实现烧结生产自动化的重要作用。

基于控制模型策略的大型烧结机智能控制

介绍了莱钢400m^2烧结机的工艺控制流程，阐述了此烧结机全过程智能控制系统以及构成该系统的11个在线控制模型的控制功能及原理。同时，对该控制系统在其工艺设备、仪表上的特殊配置及对实现烧结生产自动化的作用作了概述。

Real-time operation guide system for sintering process with artificial intelligence

In order to optimize the sintering process, a real-time operation guide system with artificial intelligence was developed, mainly including the data acquisition online subsystem, the sinter chemical composition controller, the sintering process state controller, and the abnormal conditions diagnosis subsystem. Knowledge base of the sintering process controlling was constructed, and inference engine of the system was established. Sinter chemical compositions were controlled by the strategies of self-adaptive prediction, internal optimization and center on basicity. And the state of sintering was stabilized centering on permeability. In order to meet the needs of process change and make the system clear, the system has learning ability and explanation function. The software of the system was developed in Visual C++ programming language. The application of the system shows that the hitting accuracy of sinter compositions and burning through point prediction are more than 85%; the first-grade rate of sinter chemical composition, stability rate of burning through point and stability rate of sintering process are increased by 3%, 9% and 4%, respectively.