一种UV-Vis光谱检测方法用于硫酸盐溶液中的离子,该方法基于离散波形变换和极端梯度增强
Hongqiu Zhu1, Jianqiang Yuan1, Qilong Wan1
1School of Automation, Central South University, Changsha 410083, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|February 6, 2024
概括
一个新的DWT-XGBoost模型使用UV-Vis光谱仪准确检测溶液中的离子. 这种光学方法为高效的生产提供了比传统分析更快,更具成本效益的替代方案.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 是一种重要的战略金属,离子度严重影响电解效率.
- 传统的离子检测方法效率低下,耗时,劳动密集.
- 光学检测为分析离子提供了高效,低成本的替代方案.
研究的目的:
- 开发一种先进的光学检测模型,用于炼溶液中的离子.
- 使用DWT-XGBoost算法建立一个强大的度-吸收关系模型.
- 提高工业生产中离子监测的效率和准确性.
主要方法:
- 使用紫外可见光 (UV-Vis) 光谱测量来获得离子的光谱数据.
- 萨维茨基-戈莱 (S-G) 平滑被用来消除光谱数据.
- 离散波纹转换 (DWT) 和主要组件分析 (PCA) 用于特征提取,其次是极端梯度增强 (XGBoost) 模型. 贝叶斯优化调整了超参数以提高性能.
主要成果:
- 在DWT-XGBoost模型中,对于低度和高度的离子预测都表现出了卓越的准确性.
- 对于低度的,该模型实现RMSE为0.034 mg/L,MAE为0.025 mg/L,MAPE为6.983%.
- 对于高度的,该模型实现了0.231 mg/L的RMSE,0.067 mg/L的MAE和0.472%的MAPE.
结论:
- 拟议的DWT-XGBoost模型在预测离子度方面明显优于现有的算法.
- 这种光学检测方法为炼实时监控提供了高效和精确的方法.
- 该研究验证了先进的机器学习技术与光谱光度计相结合的潜力,以优化工业过程.
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