The transition to intelligent process industries requires high-fidelity digital twins capable of dynamic transient simulation. While Modelica excels in multi-physical modeling, standard fluid libraries often struggle with complex thermodynamic phase equilibria, frequently
encountering Jacobian singularities during multicomponent property calculations. To address this bottleneck, this study proposes a novel native Modelica media modeling strategy that fundamentally reconstructs complex thermodynamic models, including Peng-
Robinson (PR) and SRK equations of state, alongside activity coefficient models like NRTL. By optimizing the mathematical expressions of core non-linear equations and introducing an improved initialization mechanism, the proposed framework effectively mitigates numerical oscillations at phase boundaries. Validated through a dynamic simulation of a synthetic natural gas production process, the system demonstrates significantly enhanced numerical robustness and high accuracy in vapor-liquid equilibrium calculations without initialization failures, providing a reliable foundation for complex process industry digital twins.