Abundant time-series dynamic data can be accumulated from a chemical plant during long term operations. In our previous work, these plant data were directly implemented for the purpose of model predictive control. In this work, fractal analysis is performed to reduce the size of a time-series data set for high quality nonlinear model predictive control. Results in this study indicate that on-line identification of nonlinear models is unnecessary if the disturbances to the process satisfy the fractal-equivalence condition. Simulation examples, including the dual composition control of a high-purity distillation column demonstrate that the nonlinear model predictive scheme is guile useful for those cases in which linear model predictive controller has failed.