Abundant time-series dynamic data can be accumulated from a chemical plant during longterm operations. In our previous work, these plant data were directly implemented for the purpose of model predictive control. However, a large amount of time-series data is required to perform high-quality nonlinear 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 quite useful for those cases in which the linear model predictive controller has failed.