A Deep Learning Model for Measuring Oxygen Content of Boiler Flue Gas

Abstract

The oxygen content of boiler flue gas is a valid indicator of boiler efficiency and emissions. Measuring the oxygen content of boiler flue gas is time consuming and costly. To overcome the latter shortcomings, a novel deep belief network algorithm based hybrid prediction model for the oxygen content of boiler flue gas is proposed. First, the algorithm is used to build a model based on the historical data collected from the distribution control system. The variables are divided into control variables and state variables to meet the needs of advanced control requirement. Then, a lasso algorithm is used to select variables highly related to the oxygen content as the inputs of the prediction model. Two basic models based on the deep-belief network are established, one using control variables, and the other, state variables. Finally, the two basic models are combined with a least square support vector machine to improve prediction accuracy of the oxygen content of boiler flue gas. To test the accuracy of the proposed algorithm, experiments based on three industrial datasets are performed. Performance of the comparison of the proposed deep belief algorithm is compared with five machine learning algorithms. Computational experience has shown that the model derived with the deep-belief algorithm produced better accuracy than the models generated by the other algorithms.