基于强化学习与图神经网络的天然沥青研磨粒径控制方法
本发明涉及一种基于强化学习与图神经网络的天然沥青研磨粒径控制方法。本发明的方法中,通过构建多参量耦合的图神经网络(GNN)模型,实现研磨过程的精准预测与动态特征表达。本发明的方法依据实时粒径和能耗数据自动调整研磨转速与介质比例,使粒径均匀度达到最优、能耗最小化。强化学习模型具备迁移适应能力,可在不同原料批次和工况下持续优化控制效果,显著提升研磨过程的智能化和自适应水平。
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