Abstract:
Abstract: Oxygen content is an important parameter reflecting the internal combustion of circulating fluidized bed boiler， for the problem that oxygen content is difficult to predict， a soft measurement model is proposed to improve the bidirectional long and short-term memory network. Firstly， the parameters of input variables such as coal feed， air intake and other input variables related to oxygen content are determined by the input-output correlation coefficient method. Secondly， the output oxygen content soft measurement model is established based on the bidirectional long and short-term memory network， and the BiLSTM prediction model is able to learn the past and the future information， which can better capture the global dependency information. Then， Tent chaotic sequences and Cauchy’s mutation strategy are introduced to optimize the honey-badger algorithm’s initial population， local optimization and global optimization abilities； the improved honey badger optimization algorithm is applied to BiLSTM prediction model parameter optimization， which in turn optimizes the hyperparameters of the BiLSTM model and ensures the accuracy of the measurement model. Finally， the proposed model is applied to the actual output prediction of circulating fluidized bed boiler， and MAE， MSE， MAPE， and RMSE are used as the evaluation indexes， and the experimental results show that the error accuracies of the THBA-BiLSTM neural network proposed in this paper are 1.57e-2， 3.5e-4， 4.1e-3， and 1.87e-2， which are significant enhancement effects relative to the other four models.

Key words: Key words: circulating fluidized bed boilers, honey badger optimization algorithm, bidirectional long and short-term memory network, neural network, flue gas oxygen content prediction