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A semi-supervised linear-nonlinear prediction system for tumbler strength of iron ore sintering process with imbalanced data in multiple working modes
Control Engineering Practice ( IF 5.4 ) Pub Date : 2021-02-19 , DOI: 10.1016/j.conengprac.2021.104766
Xiaoxia Chen , Xuhua Shi , Ting Lan

An iron ore sintering is a process that provides qualified sinter for the blast-furnace iron-making process. The tumbler strength is an important physical quality index of the sinter. Precise prediction of the tumbler strength is essential for solving the problem of how to improve it. In this study, a semi-supervised prediction system was devised for the tumbler strength of an iron ore sintering process. First, the process was briefly described with an analysis of the process characteristics. The characteristics of the existence of imbalanced data in multiple working modes, the lack of labeled samples, and the coexistence of linear–nonlinear components were taken into consideration for building the system. Then, the configuration of the prediction system was devised based on the characteristics. The system consists of three parts: working-modes decomposition based on a Gaussian mixture model (GMM) considering the existence multiple working modes, a GMM based just-in-time learning for nearest-samples selection in the relevant working modes considering the existence of imbalanced data, and the development of a semi-supervised linear–nonlinear least-square learning network considering the existence of the linear–nonlinear component and lack of labeled samples. Finally, comparisons of simulation results using actual run process data with scarce and abundant labeled samples verified the effectiveness of the proposed system. In addition, results of industrial application also verified the effectiveness of the prediction system.



中文翻译:

多工作模式下数据不平衡的铁矿烧结过程制粒强度半监督线性-非线性预测系统

铁矿石烧结是为高炉炼铁过程提供合格烧结矿的过程。不倒翁强度是烧结矿的重要物理质量指标。精确预测制动栓强度对于解决如何提高制动栓强度至关重要。在这项研究中,针对铁矿石烧结过程的翻转强度设计了半监督预测系统。首先,通过对过程特征的分析来简要描述该过程。在构建系统时,考虑了多种工作模式下不平衡数据的存在,缺少标记样本以及线性与非线性组件共存的特征。然后,根据特征设计了预测系统的配置。该系统包括三个部分:基于存在多种工作模式的高斯混合模型(GMM)的工作模式分解,考虑不平衡数据存在的相关工作模式中基于GMM的即时学习以进行相关样本的最近样本选择以及一个半监督的线性-非线性最小二乘学习网络,考虑了线性-非线性分量的存在和标记样本的缺乏。最后,使用实际运行过程数据与稀缺和大量标记样品进行的模拟结果比较,验证了所提出系统的有效性。另外,工业应用的结果也验证了该预测系统的有效性。考虑不平衡数据存在的基于GMM的实时学习,在相关工作模式下进行最近样本选择,并考虑线性-非线性的存在开发半监督线性-非线性最小二乘学习网络成分和缺乏标记的样品。最后,使用实际运行过程数据与稀缺和大量标记样品进行的模拟结果比较,验证了所提出系统的有效性。另外,工业应用的结果也验证了该预测系统的有效性。考虑不平衡数据存在的基于GMM的实时学习,在相关工作模式下进行最近样本选择,并考虑线性-非线性的存在开发半监督线性-非线性最小二乘学习网络成分和缺乏标记的样品。最后,使用实际运行过程数据与稀缺和大量标记样品进行的模拟结果比较,验证了所提出系统的有效性。另外,工业应用的结果也验证了该预测系统的有效性。使用实际运行过程数据与稀缺和大量标记样品进行的模拟结果比较,验证了所提出系统的有效性。另外,工业应用的结果也验证了该预测系统的有效性。使用实际运行过程数据与稀缺样本和大量标记样本进行的模拟结果比较,验证了所提出系统的有效性。另外,工业应用的结果也验证了该预测系统的有效性。

更新日期:2021-02-19
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