Building neural network equipment models using model modifier techniques

In this paper, we address the problem of developing accurate neural network equipment models economically. To this end, we propose model modifier techniques in conjunction with physical-neural network models. Two model modifiers-difference method and source input method-are proposed and evaluated on a horizontal chemical vapor deposition reactor. The results show that the source input method outperforms the difference method. Further, to develop a model of comparable accuracy, the source input method reduces the number of experimental data points to approximately one fourth of those needed without this approach.