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

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.