Modelling a chemical plant using grey-box models employing the support vector regression and artificial neural network

In this work, the performances of a nonlinear dynamic industrial process are examined using grey-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 concentrations, feed temperatures, and cooling temperatures of the 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 the models, ANN, SVR, first principle (FP)-ANN serial structure, FP-ANN parallel structure, FP-SVR serial structure, and FP-SVR parallel structure.