【摘要】 根据BP(Back-Propagation)神经网络原理,以气、液表观流率为输入变量,液相传质系数为输出变量,建立神经网络模型,并利用改进的LM(Levenberg-Marquardt)算法对网络进行了训练和优化。结果表明,BP神经网络能够较好地预测滴流状态下H2O吸收CO2液相传质系数。更多还原

【Abstract】 Based on the principle of BP（Back-Propagation）neural network,a neural network model was established,with gas and liquid velocities as inputs,liquid-side mass transfer coefficient as（output）.By using Levenberg-Marquardt（LM）algorithm,the net was trained and optimized.Results showed that BP neural（network） could predict liquid-side mass transfer coefficient for CO₂ absorbed by H₂O in the trickled condition.更多还原