<p>Microstructure and mechanical properties of aluminum alloys evolve sensitively with containing solute and second-phase particles as well as temperature variation during post-deformation annealing process. In this paper, a novel cellular automaton-based static recrystallization model for aluminum alloys was developed, which incorporated both the solute drag and double-edge effects of second-phase particles, namely particle-stimulated nucleation and Smith-Zener pinning resistance. By integrating both oriented nucleation and oriented growth theories, the orientations of recrystallized nuclei have been determined probabilistically in this model. The recrystallization behaviors and microstructure evolution of cold-rolled Al-Mn alloys with different states of solute content and particles during annealing are simulated. The model demonstrates predictive capability not only for grain size and recrystallization kinetics, but also for grain morphology evolution and texture component development. The successful reproduction of recrystallized microstructures under various conditions demonstrates that the proposed method provides an effective way for both explaining and predicting complex microstructural behaviors during static recrystallization. This can enable precise control over post-rolling annealing processes to tailor aluminum alloys' microstructure and thus mechanical properties.</p>