Online prediction of combustion temperature field in a furnace from operating parameters and multi-point temperature data

Abstract

The paper proposed a prediction method of combustion temperature field in a coal-fired boiler of a 350 MW unit through deep learning. The method utilizes operating parameters and multi-point temperature data as inputs for online predicting temperature field. Firstly, to establish the mapping relationship between temperature field and operating parameters as well as multi-point temperature data, a data set was constructed. In the data set, the temperature fields were obtained through the inversion of thermal radiation imaging model, while the operating parameters were collected from the distributed control system of the unit. Then, a transpose convolutional neural network (TCNN) model was developed to obtain the mapping relationship based on the data set. In the simulation study, multi-point temperature data were obtained through the forward calculation of the thermal radiation imaging model. The impact of the quantity and location of multi-point temperature data on generalization ability of the TCNN model was analyzed. In the experimental study, multi-point temperature data were measured by image probes. A comparative analysis was conducted to evaluate generalization ability of the TCNN model with and without the addition of multi-point temperature data, benchmarking against existing methods. With the addition of multi-point temperature data, the mean absolute percentage errors of predicted temperature fields are all less than 1.6 % at four stable loads, while the maximum relative error of average value of predicted temperature field decreases from 7.24 % to 2.77 % during variable load process. The proposed prediction method has promising potential for online combustion monitoring in the furnace.