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UID:pretalx-amfc2026-AYKSCK@modelica.simtek.cc
DTSTART;TZID=CST:20260922T135000
DTEND;TZID=CST:20260922T141500
DESCRIPTION:The transition to intelligent process industries requires high-
 fidelity digital twins capable of dynamic transient simulation. While Mode
 lica excels in multi-physical modeling\, standard fluid libraries often st
 ruggle with complex thermodynamic phase equilibria\, frequently\nencounter
 ing Jacobian singularities during multicomponent property calculations. To
  address this bottleneck\, this study proposes a novel native Modelica med
 ia modeling strategy that fundamentally reconstructs complex thermodynamic
  models\, including Peng-\nRobinson (PR) and SRK equations of state\, alon
 gside activity coefficient models like NRTL. By optimizing the mathematica
 l expressions of core non-linear equations and introducing an improved ini
 tialization mechanism\, the proposed framework effectively mitigates numer
 ical oscillations at phase boundaries. Validated through a dynamic simulat
 ion of a synthetic natural gas production process\, the system demonstrate
 s significantly enhanced numerical robustness and high accuracy in vapor-l
 iquid equilibrium calculations without initialization failures\, providing
  a reliable foundation for complex process industry digital twins.
DTSTAMP:20261004T070733Z
LOCATION:Modelica Technology & AI (R1001)
SUMMARY:Reconstruction of Complex Thermodynamic Equations of State in Model
 ica and Its Application in Chemical Process Simulation - Xiangcheng Wan\, 
 Jiawei Tan\, Yingru Zhao
URL:https://modelica.simtek.cc/amfc2026/talk/AYKSCK/
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