Efenwengbe Nicholas Aminaho
Data Collector
Design of a hybrid artificial intelligence system for real-time quantification of impurities in gas streams: application in CO2 capture and storage. [Dataset]
Contributors
Ndukaegho Sabastine Aminaho
Data Collector
Professor Mamdud Hossain m.hossain@rgu.ac.uk
Data Collector
Professor Nadimul Faisal N.H.Faisal@rgu.ac.uk
Data Collector
Konyengwaehie Augustus Aminaho
Accompanist
Abstract
This study proposed a new sensor calibration methodology and the design of a hybrid artificial intelligence system for real-time quantification of impurities in gas streams. Furthermore, machine learning models were developed in this study to explore how impurities in gas streams can be quantified. In the case study for the machine learning models, a nitrogen gas (N2) system with impurities was used for the demonstration. The study compares the performance of sensors in quantifying the concentrations of component gases in binary gas and multi-component gas mixtures. The binary gas mixture is made up of N2 and NO2, while the multi-component gas mixtures are mixtures of N2 with two-gas, three-gas, or four-gas combinations of NO, NO2, C3H8, and NH3; each gas mixture has a constant 10% O2 by volume (as oxygen reduction is a requirement in electrochemical sensors). The hybrid artificial intelligence system of quantifying gas concentrations is scalable and can be applied in complex gas mixtures found in different industries. For instance, in a CO2 gas stream, the binary gas mixture could represent a mixture of CO2 with NO2; while the multi-component gas mixtures could represent CO2 with two-gas, three-gas, or four-gas combinations of NO, NO2, C3H8, and NH3.
Citation
AMINAHO, E.N., AMINAHO, N.S., HOSSAIN, M., FAISAL, N.H. and AMINAHO, K.A. 2025. Design of a hybrid artificial intelligence system for real-time quantification of impurities in gas streams: application in CO2 capture and storage. [Dataset]. Gas science and engineering [online], 134, article number 205546. Available from: https://doi.org/10.1016/j.jgsce.2025.205546
Acceptance Date | Jan 9, 2025 |
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Online Publication Date | Jan 11, 2025 |
Publication Date | Feb 28, 2025 |
Deposit Date | Jan 17, 2025 |
Publicly Available Date | Jan 17, 2025 |
Publisher | Elsevier |
DOI | https://doi.org/10.1016/j.jgsce.2025.205546 |
Keywords | Gas mixtures; Gas streams; Gas pipelines; Sensors; Sensor malfunction; Artificial intelligence; Machine learning |
Public URL | https://rgu-repository.worktribe.com/output/2663051 |
Related Public URLs | https://rgu-repository.worktribe.com/output/2662175 (Journal article associated with this Supplementary Data) |
Type of Data | XLSX files and the accompanying TXT file. |
Collection Date | Oct 31, 2023 |
Collection Method | This study proposes a hybrid artificial intelligence system for real-time quantification of impurities in gas streams. This gas monitoring technology can be applied in different gas systems as well as in carbon capture, utilization, and storage. Experimental data used in this study were from the work of Javed et al. (2022). They measured the responses of mixed-potential electrochemical sensors (MPES) to gas mixtures. In the present study, a regression model based on Artificial Neural Network (ANN) algorithm was developed, using Python programming language, to quantify the concentrations of gas components using data from the work of Javed et al. (2022) and to demonstrate the design of a hybrid artificial intelligence system for real-time quantification of impurities in gas streams. Data from experiments conducted by Javed et al. (2022) are used in developing the machine learning model. The original data separated into training and testing data were concatenated into a single dataset with 107103 data points. The ANN and XGBoost models predicted the gas concentrations with minimal error. For both models, the error is lowest (MAE, MdAE, maximum error, and RMSE) in the prediction of NO2 concentration, followed by NO concentrations, but highest in the prediction of C3H8 concentration. The R2 value for both ANN and XGBoost models is greater than 99%, and the mean absolute percentage error for the XGBoost model is less than that of the ANN model. Also, the R2 value of the XGBoost model is higher. The ANN and XGBoost models predicted NO2 concentrations, in the binary gas mixture, with minimal error. Also, for both models, the deviation between the metrics of performance evaluation for the training and testing datasets is negligible. This study proposes a hybrid artificial intelligence system for real-time monitoring of gas concentrations in a gas stream and to identify when sensors need to be recalibrated. |
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Publisher Licence URL
https://creativecommons.org/licenses/by-nc/4.0/
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