快速光学光谱传感器自动校准 开发用于在线监控的传感器
Hunter B Andrews1, Luke R Sadergaski1
1Radioisotope Science and Technology Division, Oak Ridge National Laboratory, 1 Bethel Valley Rd., Oak Ridge, Tennessee 37830, United States.
ACS sensors
|September 19, 2024
概括
研究人员开发了一个自动化平台,ATLAS (应用传感器自动化暂时学习),显著减少光学传感器校准时间和样本量. 这项创新加速了光谱传感器的开发,用于实时监测复杂的化学系统.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 自动化和机器人技术
背景情况:
- 光学光谱技术使复杂系统的实时现场监测成为可能.
- 开发这些传感器的校准模型是耗时的,阻碍了在危险环境中的应用.
- 自动化为加速模型开发和传感器部署提供了解决方案.
研究的目的:
- 开发一个用于快速光谱传感器校准和验证的自动化平台.
- 为了减少构建强大的多变量模型所需的时间和样本量.
- 为了证明平台在复杂和干扰化学系统中的有效性.
主要方法:
- 应用传感器自动化暂时学习 (ATLAS) 平台使用气动控制来实现解决方案的自动循环.
- 通过使用不同度的流量配置构建了多变量模型.
- 度之间的直线过渡作为验证测量.
主要成果:
- 与手工方法相比,模型开发时间减少了76%,样本量减少了60%.
- 在具有重叠的光谱特征和干扰宽带信号的系统中表现出强大的预测性能 (RMSEP <9%).
- 成功模拟了过程监控场景,突出了模型优化和适应的潜力.
结论:
- 亚特拉斯平台显著加速了光学光谱传感器的发展.
- 自动化对于克服传感器校准时间和样本使用的限制至关重要.
- 亚特拉斯提供了一种多功能工具,用于方法选,传感器融合和复杂的多种系统分析.
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