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A dynamic predictive-control model of a nonlinear and temporal process is considered. Evolutionary computation and data mining algorithms are integrated for solving the model. Data-mining algorithms learn dynamic equations from process data. Evolutionary algorithms are then applied to solve the optimization problem guided by the knowledge extracted by data-mining algorithms. Several properties of the optimization model are shown in detail, in particular, a selection of regressors, time delays, prediction and control horizons, and weights. The concepts proposed in this paper are illustrated with an industrial case study in combustion process.