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This paper presents an efficient approach for state estimation ofpost-combustion CO2 capture plants (PCCPs) by using reduced-order neuralnetwork models. The method involves extracting lower-dimensional featurevectors from high-dimensional operational data of the PCCP and constructing areduced-order process model using proper orthogonal decomposition (POD).Multi-layer perceptron (MLP) neural networks capture the dominant dynamics ofthe process and train the network parameters with low-dimensional data obtainedfrom open-loop simulations. The proposed POD-MLP model can be used as the basisfor estimating the states of PCCPs at a significantly decreased computationalcost. For state estimation, a reduced-order extended Kalman filtering (EKF)scheme based on the POD-MLP model is developed. Our simulations demonstratethat the proposed POD-MLP modeling approach reduces computational complexitycompared to the POD-only model for nonlinear systems. Additionally, thePOD-MLP-EKF algorithm can accurately reconstruct the full state information ofPCCPs while significantly improving computational efficiency compared to theEKF based on the original PCCP model.

State estimation of a carbon capture process through POD model reduction and neural network approximation