Estimating rate constants of heterogeneous catalytic reactions without supposition of rate determining surface steps — an application of a genetic algorithm

Propane Pre-Reforming into Methane-Rich Gas over Ni Catalyst: Experiment and Kinetics Elucidation via Genetic Algorithm

Pre-reforming of propane was studied over an industrial nickel-chromium catalyst under pressures of 1 and 5 bar, at a low steam to carbon molar ratio of 1, in the temperature range of 220–380 °C and at flow rates of 4000 and 12,000 h−1. It was shown that propane conversion proceeded more efficiently at low pressure (1 atm) and temperatures above 350 °C. A genetic algorithm was applied to search for kinetic parameters better fitting experimental results in such a wide range of experimental conditions. Power law and Langmuir–Hinshelwood kinetics were considered. It was shown that only Langmuir–Hinshelwood type kinetics correctly described the experimental data and could be used to simulate the process of propane pre-reforming and predict propane conversion under the given reaction conditions. The significance of Langmuir–Hinshelwood kinetics increases under high pressure and temperatures below 350 °C.

Mixed-integer nonlinear optimisation approach to coarse-graining biochemical networks

Quantitative modelling and analysis of biochemical networks is challenging because of the inherent complexities and nonlinearities of the system and the limited availability of parameter values. Even if a mathematical model of the network can be developed, the lack of large-scale good-quality data makes accurate estimation of a large number of parameters impossible. Hence, coarse-grained models (CGMs) consisting of essential biochemical mechanisms are more suitable for computational analysis and for studying important systemic functions. The central question in constructing a CGM is which mechanisms should be deemed 'essential' and which can be ignored. Also, how should parameter values be defined when data are sparse? A mixed-integer nonlinear-programming (MINLP) based optimisation approach to coarse-graining is presented. Starting with a detailed biochemical model with associated computational details (reaction network and mathematical description) and data on the biochemical system, the structure and the parameters of a CGM can be determined simultaneously. In this optimisation problem, the authors use a genetic algorithm to simultaneously identify parameter values and remove unimportant reactions. The methodology is exemplified by developing two CGMs for the GTPase-cycle module of M1 muscarinic acetylcholine receptor, Gq, and regulator of G protein signalling 4 [RGS4, a GTPase-activating protein (GAP)] starting from a detailed model of 48 reactions. Both the CGMs have only 17 reactions, fit experimental data well and predict, as does the detailed model, four limiting signalling regimes (LSRs) corresponding to the extremes of receptor and GAP concentration. The authors demonstrate that coarse-graining, in addition to resulting in a reduced-order model, also provides insights into the mechanisms in the network. The best CGM obtained for the GTPase cycle also contains an unconventional mechanism and its predictions explain an old problem in pharmacology, the biphasic (bell-shaped) response to certain drugs. The MINLP methodology is broadly applicable to larger and complex (dense) biochemical modules.

Mechanism and Kinetics of Direct N2O Decomposition over Fe−MFI Zeolites with Different Iron Speciation from Temporal Analysis of Products

The mechanism of direct N(2)O decomposition over Fe-ZSM-5 and Fe-silicate was studied in the temporal analysis of products (TAP) reactor in the temperature range of 773-848 K at a peak N(2)O pressure of ca. 10 Pa. Several kinetic models based on elementary reaction steps were evaluated to describe the transient responses of the reactant and products. Classical models considering oxygen formation via recombination of two adsorbed monoatomic oxygen species (*-O + *-O --> O(2) + 2*) or via reaction of N(2)O with adsorbed monoatomic oxygen species (N(2)O + *-O --> O(2) + N(2) + *) failed to describe the experimental data. The best description was obtained considering the reaction scheme proposed by Heyden et al. (J. Phys. Chem. B 2005, 109, 1857) on the basis of DFT calculations. N(2)O decomposes over free iron sites (*) as well as over iron sites with adsorbed monoatomic oxygen species (*-O). The latter reaction originates adsorbed biatomic oxygen species followed by its transformation to another biatomic oxygen species, which ultimately desorbs as gas-phase O(2). In line with previous works, our results confirm that the direct N(2)O decomposition is controlled by pathways leading to O(2). Our kinetic model excellently described transient data over Fe-silicalite and Fe-ZSM-5 zeolites possessing markedly different iron species. This finding strongly suggests that the reaction mechanism is not influenced by the iron constitution. The TAP-derived model was extrapolated to a wide range of N(2)O partial pressures (0.01-15 kPa) and temperatures (473-873 K) to evaluate its predictive potential of steady-state performance. Our model correctly predicts the relative activities of two Fe-FMI catalysts, but it overestimates the absolute catalytic activity for N(2)O decomposition.

Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module

Biochemical systems embed complex networks and hence development and analysis of their detailed models pose a challenge for computation. Coarse-grained biochemical models, called reduced-order models (ROMs), consisting of essential biochemical mechanisms are more useful for computational analysis and for studying important features of a biochemical network. The authors present a novel method to model-reduction by identifying potentially important parameters using multidimensional sensitivity analysis. A ROM is generated for the GTPase-cycle module of m1 muscarinic acetylcholine receptor, Gq, and regulator of G-protein signalling 4 (a GTPase-activating protein or GAP) starting from a detailed model of 48 reactions. The resulting ROM has only 17 reactions. The ROM suggested that complexes of G-protein coupled receptor (GPCR) and GAP--which were proposed in the detailed model as a hypothesis--are required to fit the experimental data. Models previously published in the literature are also simulated and compared with the ROM. Through this comparison, a minimal ROM, that also requires complexes of GPCR and GAP, with just 15 parameters is generated. The proposed reduced-order modelling methodology is scalable to larger networks and provides a general framework for the reduction of models of biochemical systems.

Experimental design for fermentation media development: Statistical design or global random search?

The diversity of combinatorial interactions of medium components with the metabolism of cells as well as the large number of medium constituents necessary for cellular growth and production do not permit satisfactory detailed modelling. For this reason, experimental search procedures in simultaneous shaking flask experiments are used to optimise fermentation media. As an alternative to the methods of statistical experimental design employed in this field for many decades, the use of stochastic search procedures has been evaluated recently, since these require neither the unimodality of the response surface nor limitations in the number of medium components under consideration. Genetic algorithms were selected due to their basic capability for efficient exploration of large variable spaces. Using a genetic algorithm, it has been experimentally verified, with the aid of process examples, that process improvements can be achieved both for microbial and enzymatic conversions and for cell cultures despite the large number of medium components under simultaneous consideration (about 10 or more). In exploring a new variable space, process improvements of more than 100% were generally achieved. For initial reaction conditions previously 'optimised' via standard procedures it has been possible in most cases to achieve a further improvement of 20-40% of the target quantity. Although the genetic algorithm can be very efficient for exploration of large variable spaces, it is improbable that a 'global optimum' can be precisely identified because of the relatively small number of shaking flask experiments usually performed. As a consequence, a combination of highly directed random searches to explore the n-dimensional variable space with the genetic algorithm and subsequent application of classical statistical experimental design is recommended for media development.