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中文摘要：
利用3层BP神经网络对气流床粉煤气化炉进行模拟研究。以Gibbs自由能最小化方法建立粉煤气化炉数学模型的模拟结果作为BP神经网络训练数据，训练后的BP神经网络模型对模拟数据的预测准确度较好。以Shell粉煤气化炉和国内首套粉煤加压气化中试装置上的实际生产数据作为BP神经网络的训练数据，训练后的BP神经网络模型能预测实际生产数据。
英文摘要：
3-Layer BP neural network was used to simulate entrained-flow pulverized coal gasifier. The simulated results of gasifier by the way of Gibbs free energy minimization were used as training samples of BP neural networks, and the trained BP neural network model could accurately predict these simulated data. Meanwhile, actual operating data of Shell gasifier and the first domestic pilot plant of pulverized gasification were used as training samples of BP neural networks, and the trained BP neural network model could predict actual operating data.
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