Having closely worked with the IDAES team, the main benefit of developing the process simulation framework in an AML environment like Pyomo is being able to conduct advanced analysis and design techniques (e.g., robust optimization, flexibility analysis, stochastic optimization, etc.) which require constrained programming. For these types of problems, the team and myself found that shooting methods (i.e., forward simulating the DAEs) didn’t perform well against simultaneous methods (e.g., discretize then optimize).
Depending on your goals, it would be possible in principle to make something analogous to IDAES in Julia. Perhaps using the SciML framework if simulation and unconstrained optimization is your primary motivation, or using JuMP if you have similar objectives to the IDAES team. However, it would be an enormous undertaking. It has taken 40+ IDAES team members 6+ years to get it to where it is now. As a ChemE researcher and a Julia fanboy, I would love something like this to exist but it seems like too large an undertaking, especially considering that the IDAES framework already exists.
I will also note that one fundamental challenge the IDAES team has is that good open-source thermodynamic property data is few and far between. This definitely gives commercial tools like Aspen a big advantage.