煅烧工艺参数与生块质量的预测模型

Prediction Model of Calcining Process Parameters and Raw Block Quality

论文提出了结合BP神经网络算法和遗传算法,针对预焙阳极生产过程中煅烧工艺参数与生块质量间关系的预测模型。同时具体介绍了模型的相关参数,阐述了模型的评估标准和算法流程,最后展示了模型的实验数据。通过实验验证,BP神经网络算法在结合遗传算法后,相较于单独使用BP神经网络算法,显著提高了模型的预测准确率,为生块质量的预测提供了有效的参考。

In this paper,a prediction model based on BP neural network and genetic algorithm is proposed to predict the relationship between calcination process parameters and raw block quality. At the same time,the relevant parameters of the model are introduced in detail,the evaluation criteria and the algorithm flow of the model are expounded,and the experimental data of the model are finally presented. Experimental results show that the BP neural network algorithm improves the prediction accuracy of the model significantly when combined with the genetic algorithm compared with using the BP neural network algorithm alone,and provides an effective reference for the prediction of the quality of the generated block.