针对对二甲苯(-pxylene,PX)氧化反应过程中影响主要副产物对羧基苯甲醛(4-carboxybenzaldehyde,4-CBA)含量的因素众多且呈高度非线性的特征,提出了多层前向型神经网络(multi-layer feedforward network,MLFN)与偏最小二乘回归(partial least squares regression,PLSR)相结合的建模方法,建立反应产物中4-CBA含量关联模型。MLFN-PLSR采用三层网络结构和尽量多的隐节点,通过MLFN充分提取样本数据信息;然后采用PLSR消除隐含层输出冗余信息,建立具有良好预测精度的模型。与MLFN相比,最佳性能模型的预测偏差平方和均值下降了12.11%、模型平均预测偏差平方和均值下降了8.37%。与PLSR相比,最佳性能模型的预测偏差平方和均值下降了70.62%。
【英文摘要】
Due to the fact that there exist many factors having highly-nonlinear and complex effects on the concentration of 4-carboxybenzaldehyde(4-CBA),the most important intermediate product of p-xylene(PX) oxidation reaction,a novel approach integrating multi-layer feedforward network(MLFN) with partial least squares regression(PLSR) was proposed to develop a model of 4-CBA concentration in the PX oxidation product.A three-layer network consisting of an input layer,a single hidden layer and an output layer was sel...
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