使用深度神经网络和引力搜索算法,JAYA,以及多个宇宙优化算法,预测和最小化炸弹飞岩距离
Eslam Ghojoghi1, Mohamad Ali Ebrahimi Farsangi1, Hamid Mansouri1
1Mining Engineering, Department of Mining Engineering, Shahid Bahonar University of Kerman, Iran.
这项研究使用深度神经网络 (DNN) 模型准确预测炸弹飞岩距离,并结合了炸弹设计和岩石特性. 优化算法进一步减少了潜在的飞岩,提高了矿山的安全性.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 地质技术工程 地质技术工程
- 人工智能的人工智能
背景情况:
- 飞岩是地表采矿中的一个主要危险,对人员和环境构成风险.
- 有效的控制需要准确的预测和理解影响飞岩距离的因素.
- 机器学习为预测和模拟飞岩等复杂现象提供了有希望的解决方案.
研究的目的:
- 开发和评估一个深度神经网络 (DNN) 模型,用于预测炸弹飞岩距离.
- 整合优化算法 (JAYA,Multi-Verse,引力搜索) 以尽量减少预测的飞岩.
- 为了确定影响浮岩距离的关键输入参数,在地表地雷爆炸中.
主要方法:
- 利用了来自伊朗桑贡铜矿的245个爆炸记录的数据集.
- 采用了一个深度神经网络 (DNN) 模型,具有七个输入参数 (喷射设计和地力学特性).
- 集成的JAYA,多节优化和引力搜索算法用于模型优化.
主要成果:
- 该DNN模型实现了0.96的高R2值和34.11的MSE,表明了出色的预测准确性.
- 优化算法汇聚到类似的参数值,有效地减少预测的飞岩距离.
- 该模型在根据输入参数预测飞岩距离方面表现出强大的能力.
结论:
- 由优化算法增强的拟议的DNN模型提供了一个非常准确的方法,用于预测和最小化地表采矿中的飞岩距离.
- 这种方法有助于提高喷气作业的安全性和环境管理.
- 该研究强调了人工智能在解决采矿工程中的关键挑战方面的潜力.
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