College of Metallurgy and Energy, North China University of Science and Technology, Tangshan, Hebei, China.
Hebei Intelligent Engineering Research Center of Iron Ore Optimization and Ironmaking Raw Materials Preparation Process, North China University of Science and Technology, Tangshan, Hebei, China.
Comput Intell Neurosci. 2022 Jul 7;2022:4790736. doi: 10.1155/2022/4790736. eCollection 2022.
The prediction model with the sinter drum strength as the evaluation index was established based on the index data and historical sintering data generated during the sintering process. The regression prediction model in the algorithm of machine learning was applied to the prediction of the strength of the sinter drum. After verifying the feasibility of drum strength prediction, different data preprocessing methods were used to preprocess the data. Ten regression prediction algorithms such as linear regression, ridge regression, regression tree, support vector regression, and nearest neighbor regression were used for predicting the sinter drum strength to obtain preliminary prediction results. By comparing the prediction results, the most suitable combinations of data preprocessing algorithms and prediction algorithms for sinter drum strength prediction is obtained. The prediction results show that, for the drum strength of the sinter, using the function data standardization algorithm for data preprocessing has the best effect. Then, using gradient boosting regression, random forest regression, and extra tree regression prediction algorithms resulted in higher prediction accuracy. On this basis, the regression prediction model algorithm parameters are optimized and improved. The parameters of the regression prediction algorithm that are most suitable for the prediction of sinter drum strength are obtained.
基于指数数据和烧结过程中生成的历史烧结数据，建立了以烧结鼓强度为评价指标的预测模型。将机器学习算法中的回归预测模型应用于烧结鼓强度的预测。在验证了鼓强度预测的可行性之后，使用不同的数据预处理方法对数据进行预处理。使用线性回归、岭回归、回归树、支持向量回归和最近邻回归等十种回归预测算法对烧结鼓强度进行预测，得到初步预测结果。通过比较预测结果，得出了最适合烧结鼓强度预测的数据预处理算法和预测算法的组合。预测结果表明，对于烧结鼓强度，使用函数数据标准化算法进行数据预处理的效果最佳。然后，使用梯度提升回归、随机森林回归和极端树回归预测算法可以得到更高的预测精度。在此基础上，对回归预测模型算法参数进行优化和改进，得到了最适合烧结鼓强度预测的回归预测算法参数。
