27,627 research outputs found
Structure–reactivity modeling using mixture-based representation of chemical reactions
© 2017, Springer International Publishing AG. We describe a novel approach of reaction representation as a combination of two mixtures: a mixture of reactants and a mixture of products. In turn, each mixture can be encoded using an earlier reported approach involving simplex descriptors (SiRMS). The feature vector representing these two mixtures results from either concatenated product and reactant descriptors or the difference between descriptors of products and reactants. This reaction representation doesn’t need an explicit labeling of a reaction center. The rigorous “product-out” cross-validation (CV) strategy has been suggested. Unlike the naïve “reaction-out” CV approach based on a random selection of items, the proposed one provides with more realistic estimation of prediction accuracy for reactions resulting in novel products. The new methodology has been applied to model rate constants of E2 reactions. It has been demonstrated that the use of the fragment control domain applicability approach significantly increases prediction accuracy of the models. The models obtained with new “mixture” approach performed better than those required either explicit (Condensed Graph of Reaction) or implicit (reaction fingerprints) reaction center labeling
A COMBUSTION MODEL FOR MULTI-COMPONENT FUELS BASED ON RELATIVE REACTIVITY AND MOLECULAR STRUCTURE
A reliable multi-component surrogate fuel model needs to be able to represent both physical properties and chemical kinetics of a real fuel. However, enhancing the fidelity of a model with detailed description of physical and chemical behavior of all fuel components found in real fuels is limited by the prohibitive computational load to calculate the combustion chemistry of the fuel. Hence, it is desirable to achieve computational efficiency by reducing the number of chemical surrogates at the minimum expense of prediction accuracy. The objective of this work is to develop a model that can simulate the oxidation of multi-component fuels by representing the ignition characteristics of physical surrogate components with fewer chemical surrogates and achieve both computational efficiency and prediction accuracy. The main advantage of the model, called the Reactivity-Adjustment (ReAd) combustion model, is to accurately predict the reactivity of the physical surrogate components that the reaction mechanisms of which are not included in the reaction kinetics model employed in the simulation. The reactivity variation of local mixtures with different compositions is modeled by adjusting the reaction rate constants of selected control-reactions in the reaction mechanism of the representative chemical surrogates. An initial version of the model has been developed employing a single chemical surrogate to represent the combustion of diesel fuel which is modeled as multiple surrogate components to capture the physical properties of the real fuel. The model was extended to consider two more chemical surrogate components to represent the ignition characteristics of other chemical families than n-alkanes. This enabled to avoid the excessive adjustment of reaction rate constants that were necessary when a single chemical surrogate is used to represent the oxidation kinetics of entire multi-component fuels. The model was extensively tested for simulating oxidation processes of many fuels with a variety of fuel reactivity and in various combustion regimes. The results demonstrated that excellent accuracy of the ignition/combustion prediction was achieved while ensuring computational efficiency
When atomic-scale resolution is not enough: Spatial effects in in situ model catalyst studies
We investigate transport effects in in situ studies of defined model
catalysts using a multi-scale modeling approach integrating first-principles
kinetic Monte Carlo simulations into a fluid dynamical treatment. We
specifically address two isothermal flow setups: i) a channel flow with the
gas-stream approaching the single crystal from the side, as is representative
for reactor scanning tunneling microscopy experiments; and ii) a stagnation
flow with perpendicular impingement. Using the CO oxidation at RuO2 (110) as
showcase we obtain substantial variations in the gas-phase pressures between
the inlet and the catalyst surface. In the channel geometry the mass transfer
limitations lead furthermore to pronounced lateral changes in surface
composition across the catalyst surface. This prevents the aspired direct
relation between activity and catalyst structure. For the stagnation flow the
lateral variations are restricted to the edges of the catalyst. This allows to
access the desired structure-activity relation using a simple model.Comment: 22 pages, 7 figure
Reduced chemistry for butanol isomers at engine-relevant conditions
Butanol has received significant research attention as a second-generation
biofuel in the past few years. In the present study, skeletal mechanisms for
four butanol isomers were generated from two widely accepted, well-validated
detailed chemical kinetic models for the butanol isomers. The detailed models
were reduced using a two-stage approach consisting of the directed relation
graph with error propagation and sensitivity analysis. During the reduction
process, issues were encountered with pressure-dependent reactions formulated
using the logarithmic pressure interpolation approach; these issues are
discussed and recommendations made to avoid ambiguity in its future
implementation in mechanism development. The performance of the skeletal
mechanisms generated here was compared with that of detailed mechanisms in
simulations of autoignition delay times, laminar flame speeds, and perfectly
stirred reactor temperature response curves and extinction residence times,
over a wide range of pressures, temperatures, and equivalence ratios. The
detailed and skeletal mechanisms agreed well, demonstrating the adequacy of the
resulting reduced chemistry for all the butanol isomers in predicting global
combustion phenomena. In addition, the skeletal mechanisms closely predicted
the time-histories of fuel mass fractions in homogeneous compression-ignition
engine simulations. The performance of each butanol isomer was additionally
compared with that of a gasoline surrogate with an antiknock index of 87 in a
homogeneous compression-ignition engine simulation. The gasoline surrogate was
consumed faster than any of the butanol isomers, with tert-butanol exhibiting
the slowest fuel consumption rate. While n-butanol and isobutanol displayed the
most similar consumption profiles relative to the gasoline surrogate, the two
literature chemical kinetic models predicted different orderings.Comment: 39 pages, 16 figures. Supporting information available via
https://doi.org/10.1021/acs.energyfuels.6b0185
Designing algorithms to aid discovery by chemical robots
Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery
Phase equilibrium of the CO2/glycerol system: Experimental data by in situ FT-IR spectroscopy and thermodynamic modeling
Phase equilibrium experimental data for the CO2/glycerol system are reported in this paper. The measurements were performed using an in situ FT-IR method for temperatures ranging from 40 ◦C to 200 ◦C and pressures up to 35.0 MPa, allowing determination of the mutual solubility of both compounds. Concerning the CO2 rich phase, it was observed that the glycerol solubility in CO2 was extremely low (in the range of 10−5 in mole fraction) in the pressure and temperature domains investigated here. Conversely, the glycerol rich phase dissolved CO2 at mole fractions up to 0.13. Negligible swelling of the glycerol rich phase has been observed. Modeling of the phase equilibrium has been performed using the Peng–Robinson equation of state (PR EoS) with classical van der Waals one fluid and EoS/GE based mixing rules (PSRK and MHV2). Satisfactory agreement was observed between modeling results and experimental measurements when PSRK mixing rules are used in combination with UNIQUAC model, although UNIFAC predictive approach gives unsatisfactory representation of experimental behavior
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