A hybrid data-driven and mechanistic model soft sensor for estimating CO2 concentrations for a carbon capture pilot plant

• Published in: Computers in industry 2022-12, Vol.143, p.103747, Article 103747
• Main Authors: Zhuang, Yilin, Liu, Yixuan, Ahmed, Akhil, Zhong, Zhengang, del Rio Chanona, Ehecatl A., Hale, Colin P., Mercangöz, Mehmet
• Format: Article
• Language: English
• Subjects: Carbon capture pilot plant; Data-driven modeling; Long short term memory; Soft sensor
• ISSN: 0166-3615; 1872-6194
• DOI: 10.1016/j.compind.2022.103747

Integrating post-combustion carbon capture and storage (CCS) facilities into fossil fuel power plants is considered an important step for reaching global carbon emission reduction targets. When the number of gas analyzers in such CCS units is limited, variations in the load of the power plant pose a challenge to determine the gaseous CO2 concentration profile in the absorber. A dynamic hybrid model for estimating the carbon capture absorber’s gaseous CO2 concentration profile has been proposed in this study. The model is built using actual process data collected from a carbon capture pilot plant and it combines data-driven and reaction kinetic (mechanistic) modeling approaches to act as a soft sensor along the absorber column. A subset of the process data is used for training the data-driven models and for estimating the parameters of the mechanistic model respectively. Dimensionality reduction techniques are applied to the data-driven model to reduce the input size and hence the size of the dynamic model elements. The outputs of the two models are fused by comparing the computed covariance matrices. A particular challenge for this work is that the collected process data has missing spatial labels and temporal values for the CO2 concentration measurements. The presented models are obtained using an encoding and interpolation approach for the missing information. For comparison, an alternative approach based on semi-supervised learning has been implemented. The performance of the resulting soft sensors is verified by using process data from previously unseen operating conditions. The soft sensor based on the proposed hybrid model outperforms the soft sensor trained by semi-supervised autoencoder. Overall the results indicate that the proposed approach can estimate the CO2 concentration percentage with an average root mean squared error of 0.123. •Models are built and tested by process data of a carbon capture pilot plant.•A pseudo reaction can approximate the CO2-MEA reaction dynamics in the absorber.•Reduced-order methods can increase the performance of the data-driven model.•Error covariance matrices can fuse the outputs robustly.