Identification and control of a nonlinear process in the presence of unmeasured load disturbances is important, because most chemical processes are perturbed by load disturbances that are often not measured. In this paper, the absorption principle is first extended to develop an effective identification strategy for a feedforward neural network representation of the process input-output relation in the presence of an unmeasured load disturbance. This developed model can provide an accurate output prediction, irrespective-of the load disturbances, as long as the disturbances can be reasonably approximated by piecewise polynomials. Second, a predictive control scheme is developed on the basis of genetic algorithm optimization, using the above-identified model, for the nonlinear process under the influence of unmeasured loads. Finally, simulations are provided to illustrate the effectiveness of the proposed identification and control scheme.