The critical assessment of vapour pressure estimation methods for use in modelling the formation of atmospheric organic aerosol

Abstract. A selection of models for estimating vapour pressures have been tested against experimental data for a set of compounds selected for their particular relevance to the formation of atmospheric aerosol by gas-liquid partitioning. The experimental vapour pressure data (all <100 Pa) of 45 multifunctional compounds provide a stringent test of the estimation techniques, with a recent complex group contribution method providing the best overall results. The effect of errors in vapour pressures upon the formation of organic aerosol by gas-liquid partitioning in an atmospherically relevant example is also investigated. The mass of organic aerosol formed under typical atmospheric conditions was found to be very sensitive to the variation in vapour pressure values typically present when comparing estimation methods.

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Estimating fusion properties for functionalised acids

Atmospheric Chemistry and Physics ◽

10.5194/acp-11-8385-2011 ◽

2011 ◽

Vol 11 (16) ◽

pp. 8385-8394 ◽

Cited By ~ 6

Author(s):

S. Compernolle ◽

K. Ceulemans ◽

J.-F. Müller

Keyword(s):

Vapour Pressure ◽

Estimation Error ◽

Estimation Method ◽

Organic Aerosol ◽

Estimation Methods ◽

Fusion Temperature ◽

Subcooled Liquid ◽

Fusion Data ◽

Simple Estimation ◽

Data Estimation

Abstract. Multicomponent organic aerosol (OA) is likely to be liquid, or partially liquid. Hence, to describe the partitioning of these components, their liquid vapour pressure is desired. Functionalised acids (e.g. diacids) can be a significant part of OA. But often measurements are available only for solid state vapour pressure, which can differ by orders of magnitude from their liquid counterparts. To convert such a sublimation pressure to a subcooled liquid vapour pressure, fusion properties (two out of these three quantities: fusion enthalpy, fusion entropy, fusion temperature) are required. Unfortunately, experimental knowledge of fusion properties is sometimes missing in part or completely, hence an estimation method is required. Several fusion data estimation methods are tested here against experimental data of functionalised acids, and a simple estimation method is developed, specifically for this family of compounds, with a significantly smaller estimation error than the literature methods.

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The sensitivity of secondary organic aerosol (SOA) component partitioning to the predictions of component properties – Part 3: Investigation of condensed compounds generated by a near-explicit model of VOC oxidation

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-21055-2011 ◽

2011 ◽

Vol 11 (7) ◽

pp. 21055-21090 ◽

Cited By ~ 4

Author(s):

M. H. Barley ◽

D. Topping ◽

D. Lowe ◽

S. Utembe ◽

G. McFiggans

Keyword(s):

Activity Coefficients ◽

Vapour Pressure ◽

Molar Mass ◽

Emission Scenario ◽

Secondary Organic Aerosol ◽

Organic Aerosol ◽

Estimation Methods ◽

Explicit Model ◽

Voc Emission ◽

Aerosol Properties

Abstract. Calculations of the absorptive partitioning of secondary organic aerosol components were carried out using a number of methods to estimate vapour pressure and non-ideality. The sensitivity of predicted condensed component masses, volatility, O:C ratio, molar mass and functionality distribution to the choice of estimation methods was investigated in mixtures of around 2700 compounds generated by a near explicit mechanism of atmospheric VOC degradation. The sensitivities in terms of all metrics were comparable to those previously reported (using 10 000 semi-randomly generated compounds). In addition, the change in predicted aerosol properties and composition with changing VOC emission scenario was investigated showing key dependencies on relative anthropogenic and biogenic contributions. Finally, the contribution of non-ideality to the changing distribution of condensed components was explored in terms of the shift in effective volatility by virtue of component activity coefficients, clearly demonstrating both enhancement and reduction of component masses associated with negative and positive deviations from ideality.

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Novel methods for predicting gas–particle partitioning during the formation of secondary organic aerosol

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-13189-2014 ◽

2014 ◽

Vol 14 (23) ◽

pp. 13189-13204 ◽

Cited By ~ 17

Author(s):

F. Wania ◽

Y. D. Lei ◽

C. Wang ◽

J. P. D. Abbatt ◽

K.-U. Goss

Keyword(s):

Functional Groups ◽

Vapour Pressure ◽

Secondary Organic Aerosol ◽

Prediction Method ◽

Organic Aerosol ◽

Estimation Methods ◽

Minor Effect ◽

Normal Alkanes ◽

Sorption Data ◽

Order Of Magnitude

Abstract. Several methods have been presented in the literature to predict an organic chemical's equilibrium partitioning between the water insoluble organic matter (WIOM) component of aerosol and the gas phase, Ki,WIOM, as a function of temperature. They include (i) polyparameter linear free energy relationships calibrated with empirical aerosol sorption data, as well as (ii) the solvation models implemented in SPARC and (iii) the quantum-chemical software COSMOtherm, which predict solvation equilibria from molecular structure alone. We demonstrate that these methods can be used to predict Ki,WIOM for large numbers of individual molecules implicated in secondary organic aerosol (SOA) formation, including those with multiple functional groups. Although very different in their theoretical foundations, these methods give remarkably consistent results for the products of the reaction of normal alkanes with OH, i.e. their partition coefficients Ki,WIOM generally agree within one order of magnitude over a range of more than ten orders of magnitude. This level of agreement is much better than that achieved by different vapour pressure estimation methods that are more commonly used in the SOA community. Also, in contrast to the agreement between vapour pressure estimates, the agreement between the Ki,WIOM estimates does not deteriorate with increasing number of functional groups. Furthermore, these partitioning coefficients Ki,WIOM predicted SOA mass yields in agreement with those measured in chamber experiments of the oxidation of normal alkanes. If a Ki,WIOM prediction method was based on one or more surrogate molecules representing the solvation properties of the mixed OM phase of SOA, the choice of those molecule(s) was found to have a relatively minor effect on the predicted Ki,WIOM, as long as the molecule(s) are not very polar. This suggests that a single surrogate molecule, such as 1-octanol or a hypothetical SOA structure proposed by Kalberer et al. (2004), may often be sufficient to represent the WIOM component of the SOA phase, greatly simplifying the prediction. The presented methods could substitute for vapour-pressure-based methods in studies such as the explicit modelling of SOA formation from single precursor molecules in chamber experiments.

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Novel methods for predicting gas-particle partitioning during the formation of secondary organic aerosol

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-14-21341-2014 ◽

2014 ◽

Vol 14 (15) ◽

pp. 21341-21385 ◽

Cited By ~ 2

Author(s):

F. Wania ◽

Y. D. Lei ◽

C. Wang ◽

J. P. D. Abbatt ◽

K.-U. Goss

Keyword(s):

Functional Groups ◽

Vapour Pressure ◽

Secondary Organic Aerosol ◽

Organic Aerosol ◽

Basis Sets ◽

Estimation Methods ◽

Minor Effect ◽

Normal Alkanes ◽

Sorption Data ◽

Better Than

Abstract. Several methods have been presented in the literature to predict an organic chemical's equilibrium partitioning between the water insoluble organic matter (WIOM) component of aerosol and the gas phase, Ki, WIOM as a function of temperature. They include (i) polyparameter linear free energy relationships calibrated with empirical aerosol sorption data, as well as (ii) the solvation models implemented in SPARC and (iii) the quantum-chemical software Cosmotherm, which predict solvation equilibria from molecular structure alone. We demonstrate that these methods can be used to predict Ki, WIOM for large numbers of individual molecules implicated in secondary organic aerosol (SOA) formation, including those with multiple functional groups. Although very different in their theoretical foundations, these methods give remarkably consistent results for the products of the reaction of normal alkanes with OH, i.e. their partition coefficients Ki, WIOM generally agree within one order of magnitude over a range of more than ten orders of magnitude. This level of agreement is much better than that achieved by different vapour pressure estimation methods that are more commonly used in the SOA community. Also, in contrast to the agreement between vapour pressure estimates, that between the Ki, WIOM estimates does not deteriorate with increasing number of functional groups. Furthermore, these partitioning coefficients Ki, WIOM are found to predict the SOA mass yield in chamber experiments of the oxidation of normal alkanes as good or better than a vapour pressure based method. If a Ki, WIOM prediction method was based on one or more surrogate molecules representing the solvation properties of the mixed OM phase of SOA, the choice of those molecule(s) was found to have a relatively minor effect on the predicted Ki, WIOM, as long as the molecule(s) are not very polar. This suggests that a single surrogate molecule, such as 1-octanol or a hypothetical SOA structure proposed by Kalberer et al. (2004), may often be sufficient to represent the WIOM component of the SOA phase, greatly simplifying the prediction. The presented methods could substitute for vapour pressure based methods in studies such as the explicit modeling of SOA formation from single precursor molecules in chamber experiments or the assignment of SOA-forming molecules to volatility basis sets.

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The sensitivity of secondary organic aerosol (SOA) component partitioning to the predictions of component properties – Part 3: Investigation of condensed compounds generated by a near-explicit model of VOC oxidation

Atmospheric Chemistry and Physics ◽

10.5194/acp-11-13145-2011 ◽

2011 ◽

Vol 11 (24) ◽

pp. 13145-13159 ◽

Cited By ~ 18

Author(s):

M. H. Barley ◽

D. Topping ◽

D. Lowe ◽

S. Utembe ◽

G. McFiggans

Keyword(s):

Activity Coefficients ◽

Vapour Pressure ◽

Molar Mass ◽

Emission Scenario ◽

Secondary Organic Aerosol ◽

Organic Aerosol ◽

Estimation Methods ◽

Explicit Model ◽

Voc Emission ◽

Aerosol Properties

Abstract. Calculations of the absorptive partitioning of secondary organic aerosol components were carried out using a number of methods to estimate vapour pressure and non-ideality. The sensitivity of predicted condensed component masses, volatility, O:C ratio, molar mass and functionality distribution to the choice of estimation methods was investigated in mixtures of around 2700 compounds generated by a near explicit mechanism of atmospheric VOC degradation. The sensitivities in terms of all metrics were comparable to those previously reported (using 10 000 semi-randomly generated compounds). In addition, the change in predicted aerosol properties and composition with changing VOC emission scenario was investigated showing key dependencies on relative anthropogenic and biogenic contributions. Finally, the contribution of non-ideality to the changing distribution of condensed components was explored in terms of the shift in effective volatility by virtue of component activity coefficients, clearly demonstrating both enhancement and reduction of component masses associated with negative and positive deviations from ideality.

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Excess Properties of Liquid Cyclohexane/Hydrocarbon Mixtures II. Excess Gibbs Energy Determined from Total Vapour Pressure Data

Berichte der Bunsengesellschaft für physikalische Chemie ◽

10.1002/bbpc.19860900111 ◽

1986 ◽

Vol 90 (1) ◽

pp. 69-70 ◽

Cited By ~ 5

Author(s):

H. Wagner ◽

R. N. Lichtenthaler

Keyword(s):

Gibbs Energy ◽

Vapour Pressure ◽

Excess Gibbs Energy ◽

Excess Properties ◽

Pressure Data ◽

Hydrocarbon Mixtures

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Utilizing Data-Driven Models to Predict Brittleness in Tuscaloosa Marine Shale: A Machine Learning Approach

10.2118/208628-stu ◽

2021 ◽

Author(s):

Jamal Ahmadov

Keyword(s):

Machine Learning ◽

Random Forest ◽

Brittleness Index ◽

Estimation Methods ◽

Gradient Boosting ◽

Average Error ◽

Support Vector ◽

Marine Shale ◽

Effective Manner ◽

Selection Of

Abstract The Tuscaloosa Marine Shale (TMS) formation is a clay- and liquid-rich emerging shale play across central Louisiana and southwest Mississippi with recoverable resources of 1.5 billion barrels of oil and 4.6 trillion cubic feet of gas. The formation poses numerous challenges due to its high average clay content (50 wt%) and rapidly changing mineralogy, making the selection of fracturing candidates a difficult task. While brittleness plays an important role in screening potential intervals for hydraulic fracturing, typical brittleness estimation methods require the use of geomechanical and mineralogical properties from costly laboratory tests. Machine Learning (ML) can be employed to generate synthetic brittleness logs and therefore, may serve as an inexpensive and fast alternative to the current techniques. In this paper, we propose the use of machine learning to predict the brittleness index of Tuscaloosa Marine Shale from conventional well logs. We trained ML models on a dataset containing conventional and brittleness index logs from 8 wells. The latter were estimated either from geomechanical logs or log-derived mineralogy. Moreover, to ensure mechanical data reliability, dynamic-to-static conversion ratios were applied to Young's modulus and Poisson's ratio. The predictor features included neutron porosity, density and compressional slowness logs to account for the petrophysical and mineralogical character of TMS. The brittleness index was predicted using algorithms such as Linear, Ridge and Lasso Regression, K-Nearest Neighbors, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost and Gradient Boosting. Models were shortlisted based on the Root Mean Square Error (RMSE) value and fine-tuned using the Grid Search method with a specific set of hyperparameters for each model. Overall, Gradient Boosting and Random Forest outperformed other algorithms and showed an average error reduction of 5 %, a normalized RMSE of 0.06 and a R-squared value of 0.89. The Gradient Boosting was chosen to evaluate the test set and successfully predicted the brittleness index with a normalized RMSE of 0.07 and R-squared value of 0.83. This paper presents the practical use of machine learning to evaluate brittleness in a cost and time effective manner and can further provide valuable insights into the optimization of completion in TMS. The proposed ML model can be used as a tool for initial screening of fracturing candidates and selection of fracturing intervals in other clay-rich and heterogeneous shale formations.

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Critical Assessment Of The Issues In The Application Of Hilbert Transform To Compute The Logarithmic Decrement

Archives of Metallurgy and Materials ◽

10.1515/amm-2015-0267 ◽

2015 ◽

Vol 60 (2) ◽

pp. 1105-1108

Author(s):

M. Majewski ◽

L.B. Magalas

Keyword(s):

Internal Friction ◽

Hilbert Transform ◽

Computing Methods ◽

Critical Assessment ◽

Logarithmic Decrement ◽

High Dispersion ◽

Mechanical Spectroscopy ◽

Transform Method ◽

Twin Method ◽

Selection Of

Abstract The parametric OMI (Optimization in Multiple Intervals), the Yoshida-Magalas (YM) and a novel Hilbert-twin (H-twin) methods are advocated for computing the logarithmic decrement in the field of internal friction and mechanical spectroscopy of solids. It is shown that dispersion in experimental points results mainly from the selection of the computing methods, the number of oscillations, and noise. It is demonstrated that conventional Hilbert transform method suffers from high dispersion in internal friction values. It is unequivocally demonstrated that the Hilbert-twin method, which yields a ‘true envelope’ for exponentially damped harmonic oscillations is superior to conventional Hilbert transform method. The ‘true envelope’ of free decaying strain signals calculated from the Hilbert-twin method yields excellent estimation of the logarithmic decrement in metals, alloys, and solids.

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