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Prediction Model of Converter Oxygen Consumption Based on Recursive Classification and Feature Selection

机译：基于递归分类和特征选择的转炉耗氧量预测模型

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摘要

Oxygen consumption prediction for steelmaking converter is essential for optimal scheduling and energy saving of oxygen systems. To improve the prediction accuracy of oxygen consumption, an integrated prediction method based on feature space recursive division and feature selection is proposed. The feature space containing the whole converter production data is recursively divided into several feature subspaces containing the training subset. And the complexity of the data distribution will be reduced in each subspace. The simple data distribution will be more easily fitted by the prediction model. Based on recursive feature elimination, the appropriate feature variable combination and the corresponding oxygen consumption prediction models of the converter will be selected for each subset. For the test sample, it will be matched to a corresponding feature space by recursive division conditions. Then oxygen consumption is predicted by the corresponding prediction model based on the optimal combination of feature variables. A converter production data of a steel enterprise are used for testing. SVR and MLP will be used, respectively, for comparison in two groups of comparative experiments. The results show that the prediction performance of the integrated model is better than that of a single prediction model in multiple indicators.
机译：炼钢转炉耗氧量预测是氧气系统优化调度和节能的关键。为了提高耗氧量的预测精度，提出了一种基于特征空间递归划分和特征选择的综合预测方法。将包含整个转炉生产数据的特征空间递归划分为多个包含训练子集的特征子空间。在每个子空间中，数据分布的复杂性都会降低。预测模型更容易拟合简单的数据分布。基于递归特征消除，将为每个子集选择合适的特征变量组合和相应的转炉耗氧量预测模型。对于测试样本，它将通过递归分割条件匹配到相应的特征空间。然后根据特征变量的最优组合，通过相应的预测模型对耗氧量进行预测。利用某钢铁企业转炉生产数据进行检测。SVR和MLP将分别用于两组对比实验的比较。结果表明，综合模型在多指标上的预测性能优于单一预测模型。

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