An in-situ FT-IR based quantitative analysis model has been designed to track the internal state of solvent mixtures in a CO₂ capture process. This approach is much faster and easier than using GC or NMR, but conventional linear multivariate analysis is not suitable due to the poor resolution of FT-IR. The conventional PLS regression also exhibits bad performance due to its inability to reflect the nonlinear behavior like peak shift, which is a common characteristic of the systems involving reactions. This paper proposes the artificial neural networks (ANNs) as an alternative nonlinear regression method. Two feature extraction methods, PCA and POD, are applied to reduce the redundancy and dimension of the input data as a preprocessing step. The neural network approach displayed higher accuracies in cross-validation and also in in-situ experiments compared to the PLS regression in a performance test involving three models. In particular, the POD-ANN method showed outstanding results with under 5 % relative error. This model can fulfill the function of an online monitoring system for CO₂ capture processes and can provide information on water and solvent loss from evaporation or degradation. Furthermore, it can be utilized for control and fault detection techniques to maintain long-term operational stability of the system.

设计了基于现场FT-IR的定量分析模型来跟踪CO ₂中溶剂混合物的内部状态捕获过程。这种方法比使用GC或NMR更快，更容易，但是常规的线性多变量分析由于FT-IR的分辨率较差而不合适。由于传统的PLS回归无法反映非线性行为（例如峰位移），这也表现出较差的性能，这是涉及反应的系统的共同特征。本文提出了人工神经网络（ANN）作为替代的非线性回归方法。应用PCA和POD这两种特征提取方法可减少预处理步骤中输入数据的冗余度和维数。与涉及三个模型的性能测试中的PLS回归相比，神经网络方法在交叉验证和原位实验中显示出更高的准确性。特别是，POD-ANN方法显示了出色的结果，相对误差低于5％。该模型可以实现CO在线监测系统的功能。_2个捕获过程，可以提供有关蒸发或降解引起的水和溶剂损失的信息。此外，它可用于控制和故障检测技术，以保持系统的长期运行稳定性。