Prediction model of coal catalytic gasification based on the improved BP neural network
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摘要: 采用改进的三层BP神经网络建立了煤催化气化反应失重率、气化初始温度和最大气化速率所对应温度的预测模型。结果表明，采用改进BP神经网络模型在此研究中可达到较高的精度，其最大预测误差分别为5.18% 、5.65% 、2.33%，明显小于归回公式的预测误差。Abstract: Coal catalytic gasification is a very complex process, which was affected by a lot of factors. A prediction model would be helpful for the understanding, design and optimization of such processing. Therefore, a prediction model for the weight loss, the initial temperature of gasification and the temperature of maximum gasification rate during coal catalytic gasification was established by using a three-layer improved Back Propagation (BP) neural network. The model prediction results indicate that the improved BP neural network has a high accuracy. Moreover, the maximum errors between the experimental and the predicted values are 5.18%, 5.65% and 2.33%, respectively, which are much smaller than those predicted by regression equation.
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