Solids train (crystallize → filter → dry) — a NRTL process flowsheet
An MSMPR crystallizer feeds a cake filter and dryer — the crystal size and cake moisture propagate on the stream's solids payload (pharma / minerals workflow).
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- Rigorous NRTL thermodynamics, solved by the same engine every simulation runs on.
- 3 unit operations modeled: CR, FIL, DRY.
- Focus areas: MSMPR, Crystallization, Cake filtration.
- Thermodynamics
- NRTL
- Components
- ethanol, water
- Unit operations
- CRFILDRY
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Read the step-by-step guideReproduce this exact result from Python — the real client.get_example() → run_and_wait() path, not a mockup.
from flowsim.sdk import FlowSimClient
client = FlowSimClient()
example = client.get_example("solids-train")
sim = client.create_simulation(example["title"], example["flowsheet"])
result = client.run_and_wait(sim["id"])
print(result["status"]) # "converged"
streams = client.streams(sim["id"])Related models
Crystallization with agglomeration (distributed CSD)
An MSMPR crystallizer solved with the rigorous distributed population balance (not just moments): the full crystal-size distribution is computed on a size grid, and an agglomeration (aggregation) kernel combines fine crystals into larger ones — coarsening the mass-weighted mean size (d43) and broadening the distribution (CV rises above the growth-only MSMPR value of ~1.0) while conserving mass exactly. The distribution, its coefficient of variation, and d43 propagate on the solid stream into the filter and dryer — the gPROMS/gCRYSTAL capability the moment model structurally cannot provide.
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