In this work, the performances of a nonlinear dynamic industrial process are examined using gray-box (GB) models. To understand the dynamics of the system, the transient state is targeted. A white-box (WB) model, holds the prevailing knowledge using some assumptions. The performance of this model is limited. Artificial neural network (ANN) and support vector regression (SVR) which are techniques employed in numerous chemical engineering applications are employed to construct the associated black-box (BB) models. GA is used to optimize the SVR parameters. Dimensional and range extrapolations of different manipulated inputs, feed concentration, feed temperature, and cooling temperature of GB model, and BB model are discussed. The different inputs extrapolation has different results because each input’s effectiveness in the system is different. The results are compared, and the best model is suggested among models ANN, SVR, FP-ANN serial structure, FP-ANN parallel structure, FP-SVR serial structure, and FP-SVR parallel structure.