【摘要】 链篦机-回转窑工艺是生产球团矿的一套工艺，由于链篦机-回转窑生产工艺的复杂性，成品球的质量与各个工序生产参数和工艺参数相互耦合，很难建立精确的机理模型。在实际生产过程中对成品球团矿质量的检测存在较大的滞后，不能及时反馈调整工艺生产参数。针对此问题，基于Spearman相关系数和Pearson相关系数对参数进行特征处理，并结合XGBoost算法、粒子群优化算法建立了球团矿抗压强度预测模型，用实际生产数据完成模型的训练。结果表明，该模型有较高的预测效果，命中率可达94%，满足现场实际生产需求，可实现对球团矿质量的快速精准预测，为现场生产及工艺参数的调整提供参考。更多还原

【Abstract】 The chain grate rotary kiln process is a set of processes for producing pellet ore. Due to the complexity of the chain grate rotary kiln production process, the quality of the finished pellets is coupled with the production parameters and process parameters of each process, making it difficult to establish an accurate mechanism model. There is a significant lag in the detection of the quality of finished pellet ore in the actual production process, and it is not possible to provide timely feedback and adjust the process production parameters. In response to this issue, a prediction model for compressive strength of ball ore was established based on Spearman correlation coefficient and Pearson correlation coefficient for parameter feature processing, combined with XGBoost algorithm and particle swarm optimization algorithm. The model was trained using actual production data. The results show that the model has a high prediction performance, with a hit rate of up to 94%, which meets the actual production needs on site and can achieve fast and accurate prediction of the quality of ball ore, providing reference for on-site production and adjustment of process parameters.更多还原