Through separation of the hexane-heptane–octane system in a cross-wall adiabatic dividing wall column, the ef-fects of feed position, side-draw position, liquid split ratio, vapor split ratio and their interactions on the energy consumption were analyzed by Aspen Plus under the constant product purity, and the response surface model for the energy consumption was regressed. Based on the restriction on the optimal operating zone, the comparison of different combinations of surrogate models and optimization methods showed that, the combination of the Kriging model and multi-island genetic algorithm (Kriging-MIGA) had better prediction ability than the combination of the response surface model and partial derivative method (RSM-PD), and RSM-PD had better optimization effect than Kriging-MIGA. With a self-made cross-wall adiabatic dividing wall column, the temperature at measuring points and the energy consumption were measured during experiments, the comparison between measured values and simulated ones demonstrated that the optimized values of variables searched by RSM-PD and Kriging-MIGA could be both used as the optimum technological conditions since the experimental reli-ability was ensured, with the optimum technological conditions shown below: The feed position is 6, the side-draw position is 7, the combinations of liquid split ratio and vapor split ratio are [0.14, 0.5] and [0.16, 0.52], respectively. RSM-PD and Kriging-MIGA can provide the appropriate optimization methods for the dividing wall column.