使用机器学习技术预测电子废物热循环中的热重力度数据:以数据为导向的方法
Labeeb Ali1, Kaushik Sivaramakrishnan1, Mohamed Shafi Kuttiyathil1
1Department of Chemical and Petroleum Engineering, United Arab Emirates University, Sheikh Khalifa Bin Zayed Street, Al-Ain 15551, United Arab Emirates.
ACS omega
|November 29, 2023
概括
机器学习准确地复制了四博醇和血二氧化解的热重量测量分析数据,使电子废物回收的在线监控成为可能. 这种方法减少了错误,提高了电子垃圾处理的效率.
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.
- 人工智能的人工智能
背景情况:
- 电子垃圾的热回收面临着含化合物的挑战,需要有效的除策略.
- 铁氧化物 (血) 是一种有效的除剂,用于制阻燃剂,如四博醇 (TBP).
- 热重力测量分析 (TGA) 为TBP与血的分解提供了质量损失数据,但需要仪器时间,并且可能涉及错误.
研究的目的:
- 为了评估机器学习 (ML) 技术在复制TBP和TBP+血合溶解的TGA数据中的有效性.
- 探索ML在线监测电子废物热处理的潜力,减少实验时间和错误.
- 评估ML在提高电子废物回收过程的效率和成本效益方面的应用.
主要方法:
- 利用非线性回归ML模型,特别是随机森林 (RF) 和梯度增强回归 (GBR),分析TGA数据.
- 经过训练和验证的模型使用来自TGA实验的大型数据集在热解 (N2) 和氧化 (O2) 条件下进行.
- 针对TBP和TBP+血酸盐混合物,研究了不同训练样本大小 (10,000至40,000) 的模型性能.
主要成果:
- 射频和GBR模型在复制TGA数据时实现了高预测准确率 (0.999) 和低预测误差.
- ML模型成功地模拟了TGA数据的非线性和多维性质,用于用血分解TBP.
- 该研究表明,用ML替代或补充传统的TGA用于过程监控的可行性.
结论:
- 机器学习为分析和预测电子废物组件的热分解行为提供了一种强大,高效和准确的方法.
- 开发的ML方法具有实时在线监测电子废物处理的巨大潜力,降低成本并提高过程效率.
- 这项研究强调了ML在材料表征和可持续电子废物回收利用技术的进步中的更广泛应用.
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