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Soft sensor for the prediction of oxygen content in boiler flue gas using neural networks and extreme gradient boosting

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Abstract

Oxygen content in the flue gas system of power plants is an essential factor affecting boiler efficiency. Accurate oxygen content measurement is vital in evaluating boiler combustion efficiency. The device measuring oxygen content in flue gases at an oil refinery uses a Zirconia oxygen analyzer. This sensor utilization without sensor redundancy makes the oxygen content measurement conducted manually. Workers’ manual measurement is risky because it is a high-risk work area. In addition, the oxygen content in flue gas also indicates boiler combustion efficiency and the amount of other harmful gases produced by the boiler. This paper proposes a soft sensor using artificial neural networks (ANN) and extreme gradient boosting (XGBoost) to predict oxygen content. The dataset used is collected from the historical data of the distributed control system of an oil refinery system boiler. The experimental results show that the one hidden layer ANN model achieves an MAE of 0.0715 and RMSE of 0.0935, while the XGBoost model with hyperparameter tuning and seven features achieves an MAE of 0.0452 and RMSE of 0.0642. The results suggest that the XGBoost model with hyperparameter tuning and seven features outperforms the one hidden layer ANN model. The use of the seven features of the XGBoost model is the result of optimization between computational complexity and system performance.

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Data availability

The dataset used in the study is available from the corresponding author on reasonable request for non-commercial use.

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Acknowledgements

We thank the Faculty of Engineering, Universitas Gadjah Mada, for providing facilities for this research.

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Authors and Affiliations

  1. Intelligent and Embedded System Research Group, Department of Nuclear Engineering and Engineering Physics, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika 2, Yogyakarta, Indonesia

    Eko David Kurniawan, Nazrul Effendy, Agus Arif & Kenny Dwiantoro

  2. Project Development Division, PT. Pertamina (Persero), Jakarta, Indonesia

    Nidlom Muddin

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  1. Eko David Kurniawan
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Correspondence to Nazrul Effendy.

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Kurniawan, E.D., Effendy, N., Arif, A. et al. Soft sensor for the prediction of oxygen content in boiler flue gas using neural networks and extreme gradient boosting. Neural Comput & Applic 35, 345–352 (2023). https://doi.org/10.1007/s00521-022-07771-8

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