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气体传感器阵列的线性和非线性建模方法开发用于过程控制应用程序
Riadh Lakhmi1, Marc Fischer1, Quentin Darves-Blanc1
1Mines Saint-Etienne, Univ Lyon, CNRS, UMR 5307 LGF, Centre SPIN, F-42023 Saint-Etienne, France.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
一个带有机器学习模型的多传感器阵列准确地检测到Power to X过程中的气体混合物. 人工神经网络 (ANN) 与部分最小平方 (PLS) 回归相比,显示出优异的甲检测.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 传感器技术 传感器技术
- 机器学习 机器学习
背景情况:
- 动力到X技术需要强大的过程监控工具.
- 这些过程中的关键气体包括 (H2),一氧化碳 (CO),甲 (CH4) 和二氧化碳 (CO2).
- 非选择性传感器需要先进的数据分析来准确确定气体混合物成分.
研究的目的:
- 开发和评估用于Power to X过程监控的多传感器阵列的数据处理模型.
- 为了比较线性 (MLR-PLS) 和非线性 (ANN) 模型在预测气体度方面的性能.
- 评估传感器平台的长期稳定性和预测准确性.
主要方法:
- 构建一个多传感器矩阵,利用具有不同传导原理的商业传感器.
- 开发和比较多线性回归-部分最小方形 (MLR-PLS) 和人工神经网络 (ANN) 模型用于气体混合物分析.
- 使用实验数据验证模型性能,并在老式传感器平台上进行评估.
主要成果:
- 无论是MLR-PLS还是ANN模型,都实现了对H2,CO和CO2的良好度预测.
- 与MLR-PLS相比,ANN证明了甲 (CH4) 的更高的预测性能.
- 陈旧的传感器平台显示,PLS预测受到度偏移的影响,而ANN预测的灵敏度下降.
结论:
- 多传感器阵列与机器学习相结合,为Power to X应用中气体混合物监测提供了可行的解决方案.
- 由于ANN模型能够处理非线性传感器响应,因此可以提供更准确的甲检测.
- 传感器老化会影响模型性能,因此需要制定应对灵敏度下降和度偏移的策略,以实现长期监测可靠性.
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