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 selected by MLFN-PLSR and the number of the hidden layer nodes was as large as possible.Firstly

MLFN learned from the training sample.Secondly

PLSR was used to identify PLS components from the hidden-layer node output and remove the correlation among them.And

an optimal prediction ability model with the optimal number of the latent variables was obtained according to the prediction ability of the model for the verified sample.The comparison results showed that the prediction ability of the optimal MLFN-PLSR was 12.11% higher than that of the optimal MLFN and 70.62% higher than that of the optimal PLSR

and the mean prediction ability of MLFN-PLSR was 8.37% higher than that of MLFN.