利用机器学习来预测和优化来自废物材料的可持续活性碳的纹理特性
Ahmed Farid Ibrahim1,2, Mohamed Abdrabou Hussein3,4
1Department of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia.
Scientific reports
|April 2, 2025
概括
机器学习模型准确地预测活性碳 (AC) 的特性,优化从废物中可持续生产. 这种方法提高了效率,并解决了废物管理的挑战.
科学领域:
- 材料科学 材料科学 材料科学
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 对可持续废物管理的日益增长的需求推动了从废物中生产活性碳 (AC) 的创新.
- 电流的质地特性 (表面积,孔积) 对于气体净化和废水处理至关重要,但传统的评估是昂贵和复杂的.
- 机器学习 (ML) 为预测交流属性和优化生产提供了一个潜在的解决方案.
研究的目的:
- 开发和应用ML模型来预测AC的纹理特性.
- 为了优化AC生产过程,使用与梯度增强回归器模型集成的遗传算法.
- 识别影响交流性能的关键参数,并确定最佳合成条件.
主要方法:
- 应用了各种ML模型,包括随机森林 (RF),决策树 (DT),梯度增强回归器 (GBR),支持矢量机器 (SVM) 和人工神经网络 (ANN).
- 使用的输入参数,如原材料类型,颗粒大小和激活条件.
- 整合了基因算法 (GA) 与GBR模型进行流程优化.
主要成果:
- 射频和GBR模型在预测表面积方面取得了高准确性 (R2 > 0.96),优于线性回归模型 (R2 < 0.6).
- 灵敏度分析揭示了激活温度,激活剂与碳的比率以及颗粒大小作为重要因素.
- 在激活温度为800-900°C和活性剂与碳比为3.8.8的情况下,获得了最佳的交流性能.
结论:
- 机器学习模型提供了一个准确而有效的方法来预测交流电的特性,超越了传统的技术.
- 采用GA-GBR方法,可以优化交流合成,以提高可持续性.
- 这项研究提供了一个可扩展的解决方案,用于提高交流生产效率和解决废物管理问题.
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