Santra, SrimantaSrimantaSantraPaschalidis, LeandrosLeandrosPaschalidisSkiborowski, MirkoMirkoSkiborowskiFaulwasser, TimmTimmFaulwasser2026-07-242026-07-242026-07-09Industrial & Engineering Chemistry Research 65 (28): 15071–15088 (2026)https://hdl.handle.net/11420/64025Enzymatic urea hydrolysis is a mild route to ammonia production, but urease activity is strongly pH dependent. In buffer-free operation, pH can drift substantially because ammonia and inorganic carbon rapidly repartition through acid−base equilibria. Detailed process-modeling background, experimental protocol, and parameter identification context are reported in a companion manuscript by Dittmer, K. R. (2026). Here, in contrast, we develop an uncertainty-aware modeling and control framework for CO₂-driven pH regulation during urease-catalyzed urea hydrolysis. A control-oriented mechanistic model couples pHdependent urease kinetics with substrate and product inhibition, fast speciation with electroneutrality-based pH computation, and gas−liquid mass transfer of NH3 and CO₂ in a headspace. Parametric uncertainty in kinetic and transfer parameters is propagated using nonintrusive polynomial chaos expansion. Building on these predictions, we propose a stochastic model predictive control (SMPC) scheme that tracks a pH reference under input constraints while enforcing chance constraints on pH safety bounds. Simulations demonstrate uncertainty-aware pH regulation without buffer salts under the considered parametric uncertainty scenarios, providing a foundation for automated operation of enzymatic reactors under uncertainty.en1520-50452026281507115088American Chemical Society (ACS)https://creativecommons.org/licenses/by/4.0/Technology::660: Chemistry; Chemical EngineeringJournal Article10.1021/acs.iecr.6c0101310.15480/882.17589