集成机器学习预测废物衍生的硫酸水泥糊中的压力强度
1Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; State Key Laboratory of Advanced Environmental Technology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China.
机器学习模型使用工业废物预测硫酸水泥 (SAC) 的强度. 这种以数据为导向的方法通过确定原料成分,改善可持续水泥合成等关键因素来优化生产.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 工业固体废物为硫酸 (SAC) 合成提供了作为替代原料的潜力.
- 这些废物的复杂成分导致SAC生产中的性能变化.
- 优化SAC生产需要准确预测材料性能,如压力强度.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测SAC糊的压力强度.
- 确定影响SAC压力强度的关键因素,特别是原料成分.
- 提供一个数据驱动的工具,用于有效的原料选和过程优化,以实现可持续的SAC生产.
主要方法:
- 用707个文献数据点的数据集来训练和测试ML模型.
- 采用了集体ML方法,包括随机森林 (RF) 和神经网络 (NN).
- 使用模型解释技术来确定特征的重要性和与压力强度的相关性.
主要成果:
- 整体RF+NN模型实现了高预测准确性,测试R2为0.87.
- 原料成分被确定为最重要的因素,占预测重要性的34.9%.
- 在使用危险废物的独立实验中,经过验证的ML模型显示预测误差低于10.82%.
结论:
- 机器学习提供了一个精确的,数据驱动的工具来预测SAC压力强度.
- 开发的模型允许快速选原料和优化过程,以实现可持续的SAC生产.
- 这种方法提供了一种具有成本效益和节约劳动力的途径,以加快在水泥制造中使用工业废物.
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