In-situ FT-IR quantitative analysis of amine concentrations and CO2 loading amount in solvent mixtures for CO2 capture

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.