CAJ | 학술논문

介绍了BP神经网络的构造及基本原理，阐述了利用MATLAB的GUI建立BP模型的方法和步骤，并将其应用于炼油污水回用于循环冷却水系统腐蚀率的预测，建立一个以电导率和pH为输入向量、腐蚀率为输出向量的BP神经网络预测模型。结果表明，采用GUI建立的三层结构的BP神经网络模型，对炼油污水循环冷却水系统的腐蚀率的预测具有较高的预测精度。说明人工神经网络在循环水腐蚀预测中的应用是可行的，具有一定的应用价值。
개소료BP신경망락적구조급기본원리，천술료이용MATLAB적GUI건립BP모형적방법화보취，병장기응용우련유오수회용우순배냉각수계통부식솔적예측，건립일개이전도솔화pH위수입향량、부식솔위수출향량적BP신경망락예측모형。결과표명，채용GUI건립적삼층결구적BP신경망락모형，대련유오수순배냉각수계통적부식솔적예측구유교고적예측정도。설명인공신경망락재순배수부식예측중적응용시가행적，구유일정적응용개치。
The structure and principle of Back-Propagation (BP) neural network were introduced. The method and steps for building the BP neural network by the Graph User Interface (GUI) of MATLAB were also discussed. The BP neural network prediction model was established by using conductivity and pH as input vectors and corrosion rate as output vector, and the model was utilized to predict the corrosion rate of refinery wastewater reused as circulating cooling water. The results indicate that the established prediction model with three-layer structure has the higher forecast accuracy to the corrosion rate of circulating water. So application of the artificial neural network in corrosion rate prediction of circulating cooling water is feasible, and also suggested that it has extensive practicability.