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Item type:Publication, Access status: with files , Grammar-guided symbolic regression as tool for model development of enzymatic reactions(Elsevier, 2026-09-23); ; ; The transition of the chemical industry toward renewable biocatalysis requires robust kinetic models to scale and optimize enzymatic processes. However, deriving these models typically relies on manual, trial-and-error fitting of predefined equations, while data-driven machine learning approaches often produce accurate but physically meaningless “black-box” models. Here, we present an adaptive kinetic model evaluation framework that automates the discovery of enzymatic rate laws directly from batch progress curves. By employing Grammar-Guided Symbolic Regression (GGSR) the algorithm autonomously constructs rate equations using only physically permissible kinetic building blocks. The framework’s reliability was first validated against 13 synthetic kinetic mechanisms, confirming structural and parametric identifiability. The pipeline was then applied to a real experimental case study of glucose-fructose isomerization using Streptomyces murinus isomerase. The algorithm successfully recovered a reversible kinetic equation featuring an apparent uncompetitive-type interaction term providing specific activities and Michaelis constants that align well with literature. This work demonstrates that GGSR can bridge the gap between data-driven automation and mechanistically interpretable biochemical engineering.Publicationtype: Journal Article16 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Model-based optimization of enzyme cocktails for sustainable lignocellulosic biomass saccharification(Elsevier, 2026-09-16); ; Lignocellulosic biomass has a high potential as resource for the chemical industry, but its conversion to fermentable sugars requires efficient pre-treatment and a subsequent enzymatic saccharification. Here, we present a model-based workflow for the design of enzyme cocktails for lignocellulosic biomass saccharification. Saccharification performance depends on several factors, such as substrate structure and pre-treatment severity, enzyme synergy, and inhibition effects. Because of this complexity, optimization by experimental trial-and-error is expensive and time-consuming, and enzyme mixtures optimized for one substrate often lack transferability to other feedstocks. To address these challenges, this work couples a stochastic simulator of lignocellulose hydrolysis with multi-objective optimization, explicitly resolving the trade-off between conversion speed and enzyme usage as a proxy for costs. The results demonstrate the feasibility and potential of model-based optimisation for rational enzyme cocktail design.Publicationtype: Journal Article5 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Kinetic modeling and optimization of complex enzymatic reactions: a practical guide for chemists and biotechnologists(Royal Society of Chemistry (RSC), 2026-09-03); ; ; 118 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Integral experiments and magnetic resonance Imaging of lab-scale liquid distributors(Wiley, 2026-07-15); ; ; ; Liquid maldistribution in packed beds can significantly reduce process efficiency and is strongly influenced by the liquid distributor design. In this work, four lab-scale liquid distributors based on gravity- and pressure-driven operating principles were investigated at two liquid loads. Their performance was evaluated using flow maps and the maldistribution factor, revealing substantial differences between designs. The distributor providing the most homogeneous distribution was further analyzed regarding flow stability, gas load, and packing interaction. Compared to a single-point liquid distribution, the proposed distributor reduced maldistribution and wall flow. Magnetic resonance imaging measurements showed a progressive deterioration of the flow homogeneity along the packing height.Publicationtype: Journal Article36 20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , CO₂-driven pH control in the enzymatic hydrolysis of urea: stochastic model predictive control under uncertainty(American Chemical Society (ACS), 2026-07-09); ; ; 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.Publicationtype: Journal Article51 50 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , CO₂-driven pH control in the enzymatic hydrolysis of urea: a coupled modeling-experiment approach(American Chemical Society (ACS), 2026-06-25); ; ; ; Enzymatic biotransformations can offer sustainable alternatives to conventional chemical processes, but their activity often strongly depends on the pH of the reaction solution. The urease-catalyzed hydrolysis of urea provides a mild route for ammonia production; however, this ammonia production inherently increases the pH in a buffer-free system, rapidly decreasing urease activity. In this work, we combine modeling and experiments to develop a buffer-free pH control strategy for the enzymatic hydrolysis of urea that relies on the dissolution of gaseous carbon dioxide (CO₂). CO₂, which is actually a byproduct of enzymatic urea hydrolysis, is deliberately added to regulate the pH and enhance productivity. A kinetic model for urea hydrolysis is coupled with a thermodynamic model of the acid–base equilibria in the aqueous phase to analyze and design the process. Bayesian optimization is applied to calculate the optimal partial pressures for CO₂ in the gas phase to maximize productivity. The resulting concept is successfully demonstrated experimentally, highlighting a practical approach with minimal downstreaming requirements to control pH in enzymatic reactions.Publicationtype: Journal Article77 112 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Optimization-based design of distillation processes with embedded pressure drop and HETP correlationsTo improve the energy efficiency of distillation processes, various process intensification concepts have been proposed, including direct heat integration and thermal coupling. Identifying the most suitable alternative for a given separation task requires a rigorous and consistent techno-economic optimization. Superstructure models typically rely on isobaric operation and fixed HETP values, in order to avoid treating column hydraulics when solving the already challenging mixed-integer nonlinear optimization problems. In order to overcome this limitation and evaluate the effect of the simplification, the current work extends a rigorous equilibrium-stage superstructure model to account for tray-specific pressure drop and HETP values. A polylithic solution approach is implemented to improve the convergence for the resulting optimization problems. The proposed approach is demonstrated for the optimization of heat-integrated distillation sequences operated at close to atmospheric and vacuum conditions, enabling a closer investigation of the impact of the classically applied simplifications. As the results illustrate that the overall energy demand and total annualized costs are only marginally affected for the considered wide-boiling mixtures the evaluation of competing process configurations will not be affected by the simplifications. However, considerable changes in column height and heat exchanger areas are observed, particularly under vacuum operation, such that column hydraulics should be considered in design optimization, in case column size restrictions or other considerations require a more accurate equipment sizing.Publicationtype: Conference Paper12 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Reactor network synthesis of enzymatic cascades using superstructure optimizationWhile classical heuristics can be applied to decide on the preferred reactor concept for simple reaction schemes, more complex reaction networks require more sophisticated methods, such as the multilevel reactor design approach or superstructure optimization. Based on an analysis of the existing methods a nonlinear programming framework for a superstructure-based reactor network synthesis is presented, emphasizing numerical robustness and flexible network representation without relying on integer decisions. The approach, which is implemented in GAMS, allows for the combination of continuous stirred-tank and cross-flow reactor models. An exemplary application for the classical Van de Vusse reaction is first shown for validation, prior to the application to an enzymatic cascade based on the Weimberg pathway. Assuming fast co-factor regeneration, the performance of the resulting PFR cascade, which can also be interpreted as a sequence of batch reactions, is compared with a commonly applied single batch reactor. The results show that the two-reactor configuration consistently achieves higher product formation by mitigating inhibition effects, demonstrating the potential of reactor network synthesis for complex reaction systems like enzymatic cascades.Publicationtype: Conference Paper10 24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A neural model of pinch-based multicomponent distillation for applications in flowsheet synthesisThis work presents a data-driven surrogate modeling framework for predicting distillation behavior assuming an infinite number of stages and distillation limits informed by residue-curve topology and pinch-point feasibility analysis. The framework provides a direct mapping from feed composition and distillate-to-feed ratio (D/F) to distillate and bottom product compositions, making it suitable for flowsheet synthesis and optimization applications. The approach combines three components: a classifier that identifies feasible singular-point splits, a boundary regression model that predicts D/F limits separating pure- and mixed-product operating regimes, and a neural network that interpolates product compositions in the intermediate regime. The method is demonstrated for the ternary system ethanol, benzene, and water at 1 atm using data generated from rigorous vapor–liquid–liquid equilibrium analysis. Results show that the framework provides reliable predictions for pure splits while retaining smooth interpolation behavior in the mixed regime.Publicationtype: Conference Paper17 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , The steam barrier as a design constraint in carbon capture: pathways to low-temperature regenerationPoint-source carbon capture is conventionally based on steam stripping to regenerate chemical solvents, creating a dependence on high-grade thermal energy and limiting integration with low-temperature heat sources. Steam-based regeneration is highly effective because it simultaneously supplies heat, promotes CO desorption, and facilitates production of a high-purity CO stream. Nevertheless, the widespread use of steam stripping should not be interpreted as evidence that high-temperature regeneration is the only viable pathway for CO release. In this Perspective, we examine solvent regeneration through the lens of energy quality and temperature-sensitive thermodynamic control variables, highlighting why CO desorption can occur at temperatures substantially below those required for conventional steam stripping. We connect observations across buffer systems, sterically hindered and tertiary amines, thermomorphic solvents, and enzymatically assisted capture to a common underlying mechanism: temperature-dependent acid–base equilibria and phase behavior that enable equilibrium-driven desorption without vaporization. Within this framework, carbonic anhydrase emerges not as a marginal absorber-side additive, but as a kinetic enabler that becomes viable under mild, low-temperature desorption conditions. Viewed through this lens, the steam barrier is revealed as one of multiple design options rather than a physical inevitability, opening systematic pathways toward carbon capture systems compatible with low-grade heat and improved exergy efficiency.Publicationtype: Journal Article77 107