CO₂-driven pH control in the enzymatic hydrolysis of urea: stochastic model predictive control under uncertainty

Enzymatic 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.

Content

DDC Class

660: Chemistry; Chemical Engineering

Funding References

Funding(s)

SFB 1615 - SMARTe Reaktoren für die Verfahrenstechnik der Zukunft

SFB 1615 - Teilprojekt B06: Systematische Multiskalenmodellierung und Designkonzept für SMARTe Reaktoren

SFB 1615 - Teilprojekt C05: Adaptive und lernende Regelungskonzepte für SMART-e Reaktoren