基于实验和智能方法,对超细尾部的强度调查和预测,对水泥糊的补充进行了实验和智能方法
Yafei Hu1,2, Keqing Li1,2, Bo Zhang1,2
1School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Materials (Basel, Switzerland)
|June 10, 2023
概括
在采矿填埋中利用固体废物可以提高机械性能. 一项研究利用响应表面方法和机器学习优化了超细尾矿水泥糊补充 (SCPB) 的强度,实现了高预测准确度.
科学领域:
- 材料科学 材料科学 材料科学
- 地质技术工程 地质技术工程
- 环境工程 环境工程
背景情况:
- 在采矿填埋场中利用固体废物提供了显著的经济和环境效益.
- 超精细尾矿水泥糊补充 (SCPB) 是一个关键技术,但其机械性能需要改进.
研究的目的:
- 调查影响SCPB强度的因素,包括水泥材料成分和尾矿颗粒大小.
- 探索SCPB中的微结构和水化产品开发机制.
- 使用机器学习模型预测SCPB的强度.
主要方法:
- 进行了响应表面方法实验.
- 微分析技术被用来研究微观结构.
- 机器学习,特别是由Sparrow Search Algorithm (SSA) 优化的长短期记忆 (LSTM) 神经网络,被用于强度预测.
主要成果:
- 渣粉剂量和泥质量分数显著影响SCPB的强度.
- 使用20%渣粉的SCPB表现出最佳的水化产品和结构完整性.
- 优化的LSTM模型实现了高预测准确性 (RMSE=0.1396,R=0.9131,VAF=81.8747),而SSA优化提高了性能.
结论:
- 该研究提供了通过材料组成和工艺参数优化SCPB机械性能的见解.
- 机器学习,特别是SSA优化的LSTM,在多因素条件下有效预测SCPB的强度.
- 这些发现指导了使用固体废物有效填充超细尾矿的方法.
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