Patent benchmark: enzymatic cannabinoid synthesis (US9359625B2) — a BRINE process flowsheet
Real patent replication: US9359625B2's THCA-synthase-catalyzed conversion of cannabigerolic acid (CBGA) into either THCA or CBCA depending on operating pH — closing the genuine enzyme-kinetics gap this session's fact-check found (only Monod biomass-growth kinetics existed anywhere in MaximaLabs, no Michaelis-Menten). The patent's own disclosed pH-selectivity data anchors this model: it reports 'catalysis at a lower pH... favored THCA... while... neutral pH... favored CBCA,' a ~10:1 THCA:CBCA ratio at pH 5.0, and CBCA dominant at pH 7.0 — fit here as a single-ionizable-group pH-titration switch (pKa_switch=6.0), which reproduces the reported 10:1 ratio at pH 5.0 exactly (that's how pKa_switch was chosen) and gives ~10:1 CBCA:THCA at pH 7.0 (matching the patent's 'CBCA exclusively' qualitatively, not to an exact published ratio, since the patent gives no numeric ratio at pH 7.0 to match). Reactor volume is sized to clear the patent's own disclosed '>20% conversion' commercial threshold (reaches ~25.0% here).
Modeling assumptions & limitations
- 1The patent discloses no Km/kcat, so Vmax/Km are illustrative screening values consistent with the reported conversion/selectivity, not independently measured enzyme kinetics — see the Patent Benchmarks docs page.
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- Rigorous BRINE thermodynamics, solved by the same engine every simulation runs on.
- 1 unit operations modeled: Synthase Reactor.
- Focus areas: Patent benchmark, Enzyme kinetics, Michaelis-Menten, pH selectivity.
- Thermodynamics
- BRINE
- Components
- water, cbga, thca, cbca
- Unit operations
- Synthase Reactor
Opens in a new tab, loaded straight into the app — no setup.
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("patent-cannabinoid-enzymatic-synthesis")
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
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