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

arXiv: https://arxiv.org/abs/2304.05514 · HTML: https://mlsanity.com/paper/2304.05514/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2023.

## Built on by (most cited first, 2 of 2)

- [Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation](https://mlsanity.com/paper/2405.11752/) (2024)
- [Smart energy management: process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems](https://mlsanity.com/paper/2410.04743/) (2024)

## Abstract (arXiv)

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