diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml new file mode 100644 index 0000000..db558c2 --- /dev/null +++ b/.github/workflows/draft-pdf.yml @@ -0,0 +1,23 @@ +on: [push] + +jobs: + paper: + runs-on: ubuntu-latest + name: Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v2 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + # This should be the path to the paper within your repo. + paper-path: paper/paper.md + - name: Upload + uses: actions/upload-artifact@v1 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. Note, this should be the same directory as the input + # paper.md + path: paper/paper.pdf \ No newline at end of file diff --git a/README.md b/README.md index 246dccb..71011a8 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,10 @@ Linux, OSX, Windows [![Build Status](https://dev.azure.com/mirondanro/thermohub/_apis/build/status/thermohub.thermofun?branchName=master)](https://dev.azure.com/mirondanro/thermohub/_build/latest?definitionId=9&branchName=master) -A code for calculating the standard state thermodynamic properties of substances and reactions at a given temperature and pressure. +A code for calculating the standard state thermodynamic properties of substances and reactions at a given temperature and pressure. + +If you use it in your work please cite the JOSS publication +[![DOI](https://joss.theoj.org/papers/10.21105/joss.04624/status.svg)](https://doi.org/10.21105/joss.04624) - [Code documentation](https://docs.hdoc.io/dmiron/thermofun/?target=_blank) - [Simple C++ API example](#simple-c-api-example) diff --git a/paper/aq17-thermofun.json b/paper/aq17-thermofun.json new file mode 100644 index 0000000..11ad57c --- /dev/null +++ b/paper/aq17-thermofun.json @@ -0,0 +1,21638 @@ +{ + "datasources": [ + "db.thermohub.org" + ], + "date": "01.06.2022 13:47:02", + "elements": [ + { + "atomic_mass": { + "name": "M0i", + "values": [ + 0 + ] + }, + "class_": { + "4": "CHARGE" + }, + "datasources": [ + "AQ17" + ], + "entropy": { + "name": "S0i", + "values": [ + -65.3399963378906 + ] + }, + "symbol": "Zz" + }, + { + "atomic_mass": { + "name": "M0i", + "values": [ + 1.00794994831085 + ] + }, + "class_": { + "0": "ELEMENT" + }, + "datasources": [ + "AQ17" + ], + "entropy": { + "name": "S0i", + "values": [ + 65.3399963378906 + ] + }, + "symbol": "H" + }, + { + "atomic_mass": { + "name": "M0i", + "values": [ + 12.0108003616333 + ] + }, + "class_": { + "0": "ELEMENT" + }, + "datasources": [ + "AQ17" + ], + "entropy": { + "name": "S0i", + "values": [ + 5.73999977111816 + ] + }, + "symbol": "C" + }, + { + "atomic_mass": { + "name": "M0i", + "values": [ + 15.999400138855 + ] + }, + "class_": { + "0": "ELEMENT" + }, + "datasources": [ + "AQ17" + ], + "entropy": { + "name": "S0i", + "values": [ + 102.569000244141 + ] + }, + "symbol": "O" + }, + { + "atomic_mass": { + "name": "M0i", + "values": [ + 22.9897994995117 + ] + }, + "class_": { + "0": "ELEMENT" + }, + "datasources": [ + "AQ17" + ], + "entropy": { + "name": "S0i", + "values": [ + 51.2999992370605 + ] + }, + "symbol": "Na" + }, + { + "atomic_mass": { + "name": "M0i", + "values": [ + 24.3050003051758 + ] + }, + "class_": { + "0": "ELEMENT" + }, + "datasources": [ + "AQ17" + ], + "entropy": { + "name": "S0i", + "values": [ + 32.6699981689453 + ] + }, + "symbol": "Mg" + }, + { + "atomic_mass": { + "name": "M0i", + "values": [ + 26.9815006256104 + ] + }, + "class_": { + "0": "ELEMENT" + }, + "datasources": [ + "AQ17" + ], + "entropy": { + "name": "S0i", + "values": [ + 28.2999992370605 + ] + }, + "symbol": "Al" + }, + { + "atomic_mass": { + "name": "M0i", + "values": [ + 28.0855007171631 + ] + }, + "class_": { + "0": "ELEMENT" + }, + "datasources": [ + "AQ17" + ], + "entropy": { + "name": "S0i", + "values": [ + 18.8099994659424 + ] + }, + "symbol": "Si" + }, + { + "atomic_mass": { + "name": "M0i", + "values": [ + 35.4529991149902 + ] + }, + "class_": { + "0": "ELEMENT" + }, + "datasources": [ + "AQ17" + ], + "entropy": { + "name": "S0i", + "values": [ + 111.540000915527 + ] + }, + "symbol": "Cl" + }, + { + "atomic_mass": { + "name": "M0i", + "values": [ + 39.0983009338379 + ] + }, + "class_": { + "0": "ELEMENT" + }, + "datasources": [ + "AQ17" + ], + "entropy": { + "name": "S0i", + "values": [ + 64.6800003051758 + ] + }, + "symbol": "K" + }, + { + "atomic_mass": { + "name": "M0i", + "values": [ + 40.0779991149902 + ] + }, + "class_": { + "0": "ELEMENT" + }, + "datasources": [ + "AQ17" + ], + "entropy": { + "name": "S0i", + "values": [ + 41.5900001525879 + ] + }, + "symbol": "Ca" + } + ], + "reactions": [], + "substances": [ + { + "Pst": 100000, + "TPMethods": [ + { + "method": { + "32": "water_eos_iapws95_reaktoro" + } + }, + { + "method": { + "25": "water_diel_jnort91_reaktoro" + } + } + ], + "Tst": 298.15, + "aggregate_state": { + "4": "AS_AQUEOUS" + }, + "class_": { + "3": "SC_AQSOLVENT" + }, + "datasources": [ + "Johnson et al. 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"J/(mol*K)" + ], + "values": [ + 29.345325469971 + ] + }, + "sm_volume": { + "errors": [ + 0 + ], + "units": [ + "J/bar" + ], + "values": [ + 2478.9699707031 + ] + }, + "symbol": "O2" + } + ], + "thermodataset": [ + "aq17" + ] +} diff --git a/paper/compile_paper.sh b/paper/compile_paper.sh new file mode 100755 index 0000000..0b90f44 --- /dev/null +++ b/paper/compile_paper.sh @@ -0,0 +1,5 @@ +docker run --rm \ + --volume $PWD:/data \ + --user $(id -u):$(id -g) \ + --env JOURNAL=joss \ + openjournals/inara diff --git a/paper/examples.ipynb b/paper/examples.ipynb new file mode 100644 index 0000000..9d55ad3 --- /dev/null +++ b/paper/examples.ipynb @@ -0,0 +1,127 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e3da8efc", + "metadata": {}, + "outputs": [], + "source": [ + "import thermofun as fun" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "13b94f94", + "metadata": {}, + "outputs": [], + "source": [ + "database = fun.Database('aq17-thermofun.json')\n", + "engine = fun.ThermoEngine(database)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f0c85f6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G0 -545301.2878298083 J/mol\n" + ] + } + ], + "source": [ + "# T(K) P(Pa) symbol\n", + "Ca_ion = engine.thermoPropertiesSubstance(473, 2000e5, 'Ca+2')\n", + "print(f'G0 {Ca_ion.gibbs_energy.val} J/mol')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a36c08e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drS of (Cal = Ca+2 + CO3-2) is -259.12288450418015\n", + "drG of (Cal = Ca+2 + CO3-2) is 59914.092580924975\n", + "logK of (Cal = Ca+2 + CO3-2) is -8.988976334909019\n" + ] + } + ], + "source": [ + "# T(K) P(Pa) reaction equation\n", + "R = engine.thermoPropertiesReaction(348.15, 1e5, 'Calcite = Ca+2 + CO3-2')\n", + "print(f'drS of (Cal = Ca+2 + CO3-2) is {R.reaction_entropy.val}')\n", + "print(f'drG of (Cal = Ca+2 + CO3-2) is {R.reaction_gibbs_energy.val}')\n", + "print(f'logK of (Cal = Ca+2 + CO3-2) is {R.log_equilibrium_constant.val}')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "717d85a2", + "metadata": {}, + "outputs": [], + "source": [ + "batch = fun.ThermoBatch(database)\n", + "batch.setPropertiesUnits(['temperature', 'pressure'],['degC','bar'])\n", + "batch.setPressureIncrement(0,0,0)\n", + "batch.setTemperatureIncrement(0,300, 5)\n", + "substances = ['Na+', 'Mg+2', 'Ca+2', 'SiO2@']\n", + "properties = ['heat_capacity_cp','entropy', 'volume']\n", + "batch.thermoPropertiesSubstance(substances, properties).toCSV('results.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7721f824", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": false, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": false, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/paper/figure1.png b/paper/figure1.png new file mode 100644 index 0000000..76cef88 Binary files /dev/null and b/paper/figure1.png differ diff --git a/paper/functions.py b/paper/functions.py new file mode 100644 index 0000000..9211a52 --- /dev/null +++ b/paper/functions.py @@ -0,0 +1,87 @@ +import pandas as pd +import matplotlib as mpl +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D +from itertools import cycle +from collections import OrderedDict + + +def plot_substances_properties_vs_temperature(results_csv_file, substances, lables, pressure=0, property=''): + + # plot settings + mpl.rcParams['lines.linewidth']=2 + mpl.rcParams['axes.labelsize']=20 + mpl.rcParams['axes.linewidth']=2 + mpl.rcParams['font.size']=18 + mpl.rcParams['figure.figsize']=[9,9] + mpl.rcParams['figure.subplot.left'] = 0.2 + + +# fig = plt.figure() + + plt.rc('grid', linestyle="--", color='gray') + plt.grid(True) + markers = [m for m in Line2D.markers] + data = pd.read_csv(results_csv_file) + if pressure != 0.0: + data = data.loc[data.iloc[:, 2] == pressure] + c_cycle = cycle(plt.rcParams['axes.prop_cycle'].by_key()['color']) + for s, substance in enumerate(substances): + data_select = data.loc[data['Symbol']==substance] + c = next(c_cycle) + m_cycle = cycle(markers) + next(m_cycle) + next(m_cycle) + for column in data_select.columns[3:]: # loop over properties + plt.plot(data_select.iloc[:, 1], data_select[column], color=c, + marker=next(m_cycle), label=lables[s], markersize=12, markeredgecolor="w") + handles, labels = plt.gca().get_legend_handles_labels() + by_label = OrderedDict(zip(labels, handles)) + #legend1 = plt.legend(by_label.values(), by_label.keys(), loc=9, bbox_to_anchor=(1.15, 1)) #, loc=legend_loc) + plt.legend() #, loc=legend_loc) + + #plt.gca().add_artist(legend1) + plt.xlabel("Temperature [$^\circ$C]") + plt.ylabel(property) + + return plt + +def plot_properties_vs_temperature(results_csv_file, substances, pressure=0): + +# plot settings + mpl.rcParams['lines.linewidth']=2 + mpl.rcParams['axes.labelsize']=20 + mpl.rcParams['axes.linewidth']=2 + mpl.rcParams['font.size']=18 + mpl.rcParams['figure.figsize']=[9,9] + +# fig = plt.figure() + + plt.rc('grid', linestyle="--", color='gray') + plt.grid(True) + markers = [m for m in Line2D.markers] + data = pd.read_csv(results_csv_file) + if pressure != 0.0: + data = data.loc[data.iloc[:, 2] == pressure] + c_cycle = cycle(plt.rcParams['axes.prop_cycle'].by_key()['color']) + for s, substance in enumerate(substances): + data_select = data.loc[data['Symbol']==substance] + c = next(c_cycle) + m_cycle = cycle(markers) + next(m_cycle) + next(m_cycle) + for column in data_select.columns[3:]: # loop over properties + plt.plot(data_select.iloc[:, 1], data_select[column], color=c, + marker=next(m_cycle), label=column + ' '+ substance, markersize=12, markeredgecolor="w") + handles, labels = plt.gca().get_legend_handles_labels() + by_label = OrderedDict(zip(labels, handles)) + legend1 = plt.legend(by_label.values(), by_label.keys(), loc=9, bbox_to_anchor=(1.15, 1)) #, loc=legend_loc) + lines = plt.gca().get_lines() + + plt.gca().add_artist(legend1) + plt.xlabel("Temperature ($^\circ$C)") + plt.ylabel("ThermoProp") + + return plt + + diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 0000000..7452ace --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,259 @@ +@misc{Leal:2015, + author = {Leal, Allan M.M.}, + howpublished = {\texttt{https://reaktoro.org}}, + title = {{Reaktoro: An open-source unified framework for modeling chemically reactive systems}}, + url = {https://reaktoro.org}, + year = {2015} +} +@misc{Leal:2018, + author = {Leal, Allan M.M.}, + howpublished = {\texttt{https://autodiff.github.io}}, + title = {{{autodiff, a modern, fast and expressive {C++} library for automatic differentiatio}}n}, + url = {https://autodiff.github.io}, + year = {2018} +} +@article{Bell:Deiters:Leal:2022, + abstract = {This work uses advanced numerical techniques (complex differentiation and automatic differentiation) to efficiently and accurately compute all the required thermodynamic properties of an equation of state without any analytical derivatives─particularly without any handwritten derivatives. It avoids the tedious and error-prone process of symbolic differentiation, thus allowing for more rapid development of new thermodynamic models. The technique presented here was tested with several equations of state (van der Waals, Peng-Robinson, Soave-Redlich-Kwong, PC-SAFT, and cubic-plus-association) and high-accuracy multifluid models. A minimal set of algorithms (critical locus tracing and vapor-liquid equilibrium tracing) were implemented in an extensible and concise open-source C++ library: teqp (for Templated EQuation of state Package). This work demonstrates that highly complicated equations of state can be implemented faster yet with minimal computational overhead and negligible loss in numerical precision compared with the traditional approach that relies on analytical derivatives. We believe that the approach outlined in this work has the potential to establish a new computational standard when implementing computer codes for thermodynamic models.}, + author = {Bell, Ian H. and Deiters, Ulrich K. and Leal, Allan M.M.}, + doi = {10.1021/acs.iecr.2c00237}, + file = {:C:/Users/Allan/Documents/Mendeley Desktop/Bell, Deiters, Leal - 2022 - Implementing an Equation of State without Derivatives teqp.pdf:BINARY}, + issn = {15205045}, + journal = {Industrial and Engineering Chemistry Research}, + number = {17}, + pages = {6010--6027}, + title = {{Implementing an Equation of State without Derivatives: teqp}}, + volume = {61}, + year = {2022} +} +@article{Kulik2013, + abstract = {Reactive mass transport (RMT) simulation is a powerful numerical tool to advance our understanding of complex geochemical processes and their feedbacks in relevant subsurface systems. Thermodynamic equilibrium defines the baseline for solubility, chemical kinetics, and RMT in general. Efficient RMT simulations can be based on the operator-splitting approach, where the solver of chemical equilibria is called by the mass transport part for each control volume whose composition, temperature, or pressure has changed. Modeling of complex natural systems requires consideration of multiphase-multicomponent geochemical models that include nonideal solutions (aqueous electrolytes, fluids, gases, solid solutions, and melts). Direct Gibbs energy minimization (GEM) methods have numerous advantages for the realistic geochemical modeling of such fluid-rock systems. Substantial improvements and extensions to the revised GEM interior point method algorithm based on Karpov's convex programming approach are described, as implemented in the GEMS3K C/C++ code, which is also the numerical kernel of GEM-Selektor v.3 package (http://gems.web.psi.ch). GEMS3K is presented in the context of the essential criteria of chemical plausibility, robustness of results, mass balance accuracy, numerical stability, speed, and portability to high-performance computing systems. The stand-alone GEMS3K code can treat very complex chemical systems with many nonideal solution phases accurately. It is fast, delivering chemically plausible and accurate results with the same or better mass balance precision as that of conventional speciation codes. GEMS3K is already used in several coupled RMT codes (e.g., OpenGeoSys-GEMS) capable of high-performance computing. © 2012 Springer Science+Business Media B.V.}, + author = {Dmitrii A. Kulik and Thomas Wagner and Svitlana V. Dmytrieva and Georg Kosakowski and Ferdinand F. Hingerl and Konstantin V. Chudnenko and Urs R. Berner}, + doi = {10.1007/s10596-012-9310-6}, + issn = {14200597}, + issue = {1}, + journal = {Computational Geosciences}, + keywords = {Fluid-rock interaction,Geochemical modeling,Gibbs energy minimization,Nonideal systems,Reactive mass transport}, + month = {8}, + pages = {1-24}, + publisher = {Springer Netherlands}, + title = {GEM-Selektor geochemical modeling package: Revised algorithm and GEMS3K numerical kernel for coupled simulation codes}, + volume = {17}, + url = {http://link.springer.com/10.1007/s10596-012-9310-6}, + year = {2013}, +} +@article{Miron2019, + abstract = {
Thermodynamic properties of aqueous species are essential for modeling of fluid-rock interaction processes. The Helgeson-Kirkham-Flowers (HKF) model is widely used for calculating standard state thermodynamic properties of ions and complexes over a wide range of temperatures and pressures. To do this, the HKF model requires thermodynamic and electrostatic models of water solvent. In this study, we investigate and quantify the impact of choosing different models for calculating water solvent volumetric and dielectric properties, on the properties of aqueous species calculated using the HKF model. We identify temperature and pressure conditions at which the choice of different models can have a considerable effect on the properties of aqueous species and on fluid mineral equilibrium calculations. The investigated temperature and pressure intervals are 25–1000°C and 1–5 kbar, representative of upper to middle crustal levels, and of interest for modeling ore-forming processes. The thermodynamic and electrostatic models for water solvent considered are: Haar, Gallagher and Kell (1984), Wagner and Pruß (2002), and Zhang and Duan (2005), to calculate water volumetric properties, and Johnson and Norton (1991), Fernandez and others (1997), and Sverjensky and others (2014), to calculate water dielectric properties. We observe only small discrepancies in the calculated standard partial molal properties of aqueous species resulting from using different water thermodynamic models. However, large differences in the properties of charged species can be observed at higher temperatures (above 500°C) as a result of using different electrostatic models. Depending on the aqueous speciation and the reactions that control the chemical composition, the observed differences can vary. The discrepancy between various electrostatic models is attributed to the scarcity of experimental data at high temperatures. These discrepancies restrict the reliability of the geochemical modeling of hydrothermal and ore formation processes, and the retrieval of thermodynamic parameters from experimental data at elevated temperatures and pressures.
}, + author = {G. D. Miron and Allan M. M. Leal and Alina Yapparova}, + doi = {10.1155/2019/5750390}, + issn = {1468-8115}, + journal = {Geofluids}, + month = {2}, + pages = {1-24}, + publisher = {Hindawi}, + title = {Thermodynamic Properties of Aqueous Species Calculated Using the HKF Model: How Do Different Thermodynamic and Electrostatic Models for Solvent Water Affect Calculated Aqueous Properties?}, + volume = {2019}, + url = {https://www.hindawi.com/journals/geofluids/2019/5750390/}, + year = {2019}, +} +@article{Miron2020, + abstract = {Most of the available thermodynamic data concerning radioactive waste disposal are restricted to values of reaction equilibrium constants (logK298°) at 25 °C and 1 bar. Simple estimation methods such as isocoulombic reactions can be used for extrapolating the properties of reactions involving aqueous species and minerals to elevated temperatures. The aim of this study was to validate the applicability of various alternative isocoulombic reactions to estimate logKT° values of aqueous complexation reactions for lanthanides and actinides to elevated temperatures while taking advantage of new additional literature data, and to identify criteria for choosing the “best” reactions. For each chemical species of interest, a systematic approach using dedicated software and database allowed us to identify the isocoulombic reactions and types of extrapolation that yield the best estimates of standard thermodynamic properties at elevated temperatures, when very limited or no experimental data are available. We have tested aqueous complexation reactions for selected lanthanides and actinides of different valences with chloride, fluoride, sulfate, carbonate, nitrate, phosphate and silicate ligands. “Model” complexation reactions, having known temperature trends, were systematically combined with complex formation reactions of interest whose temperature trends are unknown, into many alternative isocoulombic reactions. For each ion, we investigated which of the generated isocoulombic reactions provide the best estimates for logeKT° of the reaction of interest at elevated temperatures in order to compile the guidelines for choosing the optimal ones, then applying these guidelines to “prediction” subsets. In most cases, knowing only logeKT° at 25 °C (for the reaction of interest), it was possible to obtain rather accurate estimates of logeKT° values at elevated temperatures using isocoulombic reactions that exchange ions with similar charge and hydration properties (hydrated ionic radius and structure of the hydration shell) and known logmKT° of model reactions. These ions and their complexes interact with the solvent in comparable ways, so that their similar heat capacity and entropy effects largely cancel out on both sides of an “optimal” isocoulombic reaction.}, + author = {G. D. Miron and Dmitrii A. Kulik and Tres Thoenen}, + doi = {10.1016/j.gca.2020.07.020}, + issn = {00167037}, + journal = {Geochimica et Cosmochimica Acta}, + keywords = {Actinides,Aqueous complexation,Isocoulombic reactions,Lanthanides,Temperature extrapolations}, + month = {10}, + pages = {119-142}, + publisher = {Elsevier Ltd}, + title = {Generating isocoulombic reactions as a tool for systematic evaluation of temperature trends of thermodynamic properties: Application to aquocomplexes of lanthanides and actinides}, + volume = {286}, + year = {2020}, +} +@article{Leal2017, + abstract = {We present an overview of novel numerical methods for chemical equilibrium and kinetic calculations for complex non-ideal multiphase systems. The methods we present for equilibrium calculations are based either on Gibbs energy minimization (GEM) calculations or on solving the system of extended law of mass-action (xLMA) equations. In both methods, no
}, + author = {Allan M. M. Leal and Dmitrii A. Kulik and William R. Smith and Martin O. Saar}, + doi = {10.1515/pac-2016-1107}, + issn = {1365-3075}, + issue = {5}, + journal = {Pure and Applied Chemistry}, + keywords = {17,ISSP,chemical equilibrium,chemical kinetics,geochemical modeling,numerical methods,reactive transport}, + month = {1}, + pages = {597-643}, + publisher = {De Gruyter}, + title = {An overview of computational methods for chemical equilibrium and kinetic calculations for geochemical and reactive transport modeling}, + volume = {89}, + url = {http://www.degruyter.com/view/j/pac.2017.89.issue-5/pac-2016-1107/pac-2016-1107.xml}, + year = {2017}, +} +@article{Leal2016, + abstract = {We present an extended law of mass-action (xLMA) method for multiphase equilibrium calculations and apply it in the context of reactive transport modeling. This extended LMA formulation differs from its conventional counterpart in that (i) it is directly derived from the Gibbs energy minimization (GEM) problem (i.e., the fundamental problem that describes the state of equilibrium of a chemical system under constant temperature and pressure); and (ii) it extends the conventional mass-action equations with Lagrange multipliers from the Gibbs energy minimization problem, which can be interpreted as stability indices of the chemical species. Accounting for these multipliers enables the method to determine all stable phases without presuming their types (e.g., aqueous, gaseous) or their presence in the equilibrium state. Therefore, the here proposed xLMA method inherits traits of Gibbs energy minimization algorithms that allow it to naturally detect the phases present in equilibrium, which can be single-component phases (e.g., pure solids or liquids) or non-ideal multi-component phases (e.g., aqueous, melts, gaseous, solid solutions, adsorption, or ion exchange). Moreover, our xLMA method requires no technique that tentatively adds or removes reactions based on phase stability indices (e.g., saturation indices for minerals), since the extended mass-action equations are valid even when their corresponding reactions involve unstable species. We successfully apply the proposed method to a reactive transport modeling problem in which we use PHREEQC and GEMS as alternative backends for the calculation of thermodynamic properties such as equilibrium constants of reactions, standard chemical potentials of species, and activity coefficients. Our tests show that our algorithm is efficient and robust for demanding applications, such as reactive transport modeling, where it converges within 1–3 iterations in most cases. The proposed xLMA method is implemented in + , a unified open-source framework for modeling chemically reactive systems.}, + author = {Allan M.M. Leal and Dmitrii A. Kulik and Georg Kosakowski and Martin O. Saar}, + doi = {10.1016/j.advwatres.2016.08.008}, + issn = {03091708}, + journal = {Advances in Water Resources}, + keywords = {Equilibrium,Gibbs energy minimization,LMA,Multiphase,Reactive transport,Speciation}, + month = {10}, + pages = {405-422}, + publisher = {Elsevier Ltd}, + title = {Computational methods for reactive transport modeling: An extended law of mass-action, xLMA, method for multiphase equilibrium calculations}, + volume = {96}, + year = {2016}, +} +@article{Miron2016, + abstract = {? 2016 Elsevier Ltd.A large amount of critically evaluated experimental data on mineral solubility, covering the entire Na-K-Al-Si-O-H-Cl system over wide ranges in temperature and pressure, was used to simultaneously refine the standard state Gibbs energies of aqueous ions and complexes in the framework of the revised Helgeson-Kirkham-Flowers equation of state. The thermodynamic properties of the solubility-controlling minerals were adopted from the internally consistent dataset of Holland and Powell (2002; Thermocalc dataset ds55). The global optimization of Gibbs energies of aqueous species, performed with the GEMSFITS code (Miron et al., 2015), was set up in such a way that the association equilibria for ion pairs and complexes, independently derived from conductance and potentiometric data, are always maintained. This was achieved by introducing reaction constraints into the parameter optimization that adjust Gibbs energies of complexes by their respective Gibbs energy effects of reaction, whenever the Gibbs energies of reactant species (ions) are changed. The optimized thermodynamic dataset is reported with confidence intervals for all parameters evaluated by Monte Carlo trial calculations. The new thermodynamic dataset is shown to reproduce all available fluid-mineral phase equilibria and mineral solubility data with good accuracy and precision over wide ranges in temperature (25-800 ?C), pressure (1 bar to 5 kbar) and composition (salt concentrations up to 5 molal). The global data optimization process adopted in this study can be readily repeated any time when extensions to new chemical elements and species are needed, when new experimental data become available, or when a different aqueous activity model or equation of state should be used. This work serves as a proof of concept that our optimization strategy is feasible and successful in generating a thermodynamic dataset reproducing all fluid-mineral and aqueous speciation equilibria in the Na-K-Al-Si-O-H-Cl system within their experimental uncertainties. The new dataset resolves the long-standing discrepancies between thermodynamic data of minerals and those of aqueous ions and complexes, by achieving an astonishing degree of consistency between a large number of fluid-mineral equilibrium data. All of this at the expense of changing the standard state properties of aqueous species, mainly the Gibbs energy of formation. Using the same strategy, the core dataset for the system Na-K-Al-Si-O-H-Cl can be extended with additional rock-forming elements such as Ca, Mg, Fe, Mn, Ti, S, C, B. In future, the standard-state properties of minerals and aqueous species should be simultaneously optimized, to create the next-generation of fully internally consistent data for fluid-mineral equilibria. Although we employ the widely used HKF equations for this study, the same computational approach can be readily applied to any other speciation-based equation of state for multicomponent aqueous solutions.}, + author = {G.D. Miron and T. Wagner and D.A. Kulik and C.A. Heinrich}, + doi = {10.1016/j.gca.2016.04.026}, + issn = {00167037}, + journal = {Geochimica et Cosmochimica Acta}, + keywords = {[Aqueous species, Fluid-rock interaction, Hydrothe}, + title = {Internally consistent thermodynamic data for aqueous species in the system Na-K-Al-Si-O-H-Cl}, + volume = {187}, + year = {2016}, +} +@article{Miron2017, + abstract = {This study presents an internally consistent thermodynamic dataset for aqueous species in the system Ca-Mg-Na-K-Al-Si-O-H-C-Cl, obtained by adding species of calcium, magnesium and carbon to the core system Na-K-Al-Si-O-H-Cl (Miron and others, 2016). Critically evaluated experimental data on mineral solubility (Ca and Mg hydroxides, Ca and Mg silicates, anorthite, Ca and Mg carbonates) in water and aqueous electrolyte solutions over wide ranges in temperature and pressure were added to the database of experimental data. The complete experimental dataset was then used to simultaneously refine the standard state Gibbs energies of all aqueous ions and complexes in the framework of the revised Helgeson-Kirkham-Flowers (HKF) equation of state. The thermodynamic properties of the solubility-controlling minerals were accepted from the internally consistent dataset of Holland and Powell (1998; updated Thermocalc dataset ds55). The association equilibria of important hydroxide, chloride, carbonate and silicate complexes were critically reviewed, and their standard state properties and HKF parameters were independently derived from conductance, potentiometric and, in a few cases, solubility measurements. In a global optimization of standard Gibbs energies of aqueous species, performed with the GEMSFITS code (Miron and others, 2015), the association equilibria for aqueous complexes were always maintained. The new thermodynamic dataset reproduces all available fluidmineral phase equilibria and mineral solubility data in the system Ca-Mg-Na-K-Al-Si-OH-C-Cl with good accuracy over wide ranges in temperature (25-800 °C), pressure (1 bar - 5 kbar) and composition (salt concentrations up to 5 molal). This makes it possible to perform geochemical and reactive transport modeling of processes in natural and engineered georeservoirs over wide ranges of conditions with an unprecedented level of accuracy and reliability and to address processes of fluid flow and fluid-rock interaction in the Earth's crust from a new perspective. Using the same strategy as applied in the present study, the internally consistent thermodynamic dataset can be further extended with additional major and trace elements, and the data refinement process can be repeated when new experimental data or next-generation equation of state or activity models for aqueous solutions become available.}, + author = {G.D. Miron and T. Wagner and D.A. Kulik and B. Lothenbach}, + doi = {10.2475/07.2017.01}, + issn = {00029599}, + issue = {7}, + journal = {American Journal of Science}, + keywords = {Aqueous species,Experimental data evaluation,Fluid-rock interaction,Hydrothermal geochemical modeling,Optimization,Thermodynamic database}, + pages = {755-806}, + title = {An internally consistent thermodynamic dataset for aqueous species in the system Ca-Mg-Na-K-Al-Si-O-H-C-Cl to 800 °c and 5 kbar}, + volume = {317}, + year = {2017}, +} +@article{JOHNSON1992, +title = {SUPCRT92: A software package for calculating the standard molal thermodynamic properties of minerals, gases, aqueous species, and reactions from 1 to 5000 bar and 0 to 1000°C}, +journal = {Computers & Geosciences}, +volume = {18}, +number = {7}, +pages = {899-947}, +year = {1992}, +issn = {0098-3004}, +doi = {10.1016/0098-3004(92)90029-Q}, +url = {https://www.sciencedirect.com/science/article/pii/009830049290029Q}, +author = {James W. Johnson and Eric H. Oelkers and Harold C. Helgeson}, +keywords = {SUPCRT92, Equations of state, Standard molal thermodynamic properties, Chemical equilibrium, Minerals, Gases, Aqueous species, HO, Thermodynamics, Geochemistry}, +abstract = {Recent advances in theoretical geochemistry permit calculation of the standard molal thermodynamic properties of a wide variety of minerals, gases, aqueous species, and reactions from 1 to 5000 bar and 0 to 1000°C. The SUPCRT92 software package facilitates practical application of these recent theories, equations, and data to define equilibrium constraints on geochemical processes in a wide variety of geologic systems. The SUPCRT92 package is composed of three interactive FORTRAN 77 programs, SUPCRT92, MPRONS92, and CPRONS92, and a sequential-access thermodynamic database, SPRONS92.DAT. The SUPCRT92 program reads or permits user-generation of its two input files, CON and RXN, retrieves data from the direct-access equivalent of SPRONS92.DAT, calculates the standard molal Gibbs free energy, enthalpy, entropy, heat capacity, and volume of each reaction specified on the RXN file through a range of conditions specified on the CON file, and writes the calculated reaction properties to the output TAB file and, optionally, to PLT files that facilitate their graphical depiction. Calculations can be performed along the liquid side of the H2O vaporization boundary by specifying either temperature (T) or pressure (P), and in the single-phase regions of fluid H2O by specifying either T and P, T and H2O density, T and log K, or P and log K. SPRONS92.DAT, which contains standard molal thermodynamic properties at 25°C and 1 bar, equation-of-state parameters, heat capacity coefficients, and phase transition data for approximately 500 minerals, gases, and aqueous species, can be augmented or otherwise modified using MPRONS92, and converted to its direct-access equivalent using CPRONS92.} +} +@article{ZIMMER2016, +title = {SUPCRTBL: A revised and extended thermodynamic dataset and software package of SUPCRT92}, +journal = {Computers & Geosciences}, +volume = {90}, +pages = {97-111}, +year = {2016}, +issn = {0098-3004}, +doi = {10.1016/j.cageo.2016.02.013}, +url = {https://www.sciencedirect.com/science/article/pii/S0098300416300371}, +author = {Kurt Zimmer and Yilun Zhang and Peng Lu and Yanyan Chen and Guanru Zhang and Mehmet Dalkilic and Chen Zhu}, +keywords = {SUPCRT92, Arsenic, Geological carbon sequestration, Thermodynamic properties, Geochemical modeling, Chemical equilibrium}, +abstract = {The computer-enabled thermodynamic database associated with SUPCRT92 (Johnson et al., 1992) enables the calculation of the standard molal thermodynamic properties of minerals, gases, aqueous species, and reactions for a wide range of temperatures and pressures. However, new data on the thermodynamic properties of both aqueous species and minerals have become available since the database’s initial release in 1992 and its subsequent updates. In light of these developments, we have expanded SUPCRT92’s thermodynamic dataset and have modified the accompanying computer code for thermodynamic calculations by using newly available properties. The modifications in our new version include: (1) updating the standard state thermodynamic properties for mineral end-members with properties from Holland and Powell (2011) to improve the study of metamorphic petrology and economic geology; (2) adding As-acid, As-metal aqueous species, and As-bearing minerals to improve the study of environmental geology; (3) updating properties for Al-bearing species, SiO2°(aq) and HSiO3-, boehmite, gibbsite, and dawsonite for modeling geological carbon sequestration. The new thermodynamic dataset and the modified SUPCRT92 program were implemented in a software package called SUPCRTBL, which is available online at www.indiana.edu/~hydrogeo/supcrtbl.html.} +} +@article{AWOLAYO2022, +title = {PyGeochemCalc: A Python package for geochemical thermodynamic calculations from ambient to deep Earth conditions}, +journal = {Chemical Geology}, +volume = {606}, +pages = {120984}, +year = {2022}, +issn = {0009-2541}, +doi = {10.1016/j.chemgeo.2022.120984}, +url = {https://www.sciencedirect.com/science/article/pii/S0009254122002789}, +author = {Adedapo N. Awolayo and Benjamin M. Tutolo}, +keywords = {Helgeson-Kirkham-Flowers equation of state, LogK-density extrapolation, Solid-solutions, IAPWS95 equation of state, DEW model, Thermodynamic properties, Variable-chemistry clays, Geochemical modeling, Thermodynamic database}, +abstract = {Over the past half century, techniques for evaluating the thermodynamics of water-rock interactions from ambient to deep Earth conditions have advanced incredibly and in myriad directions. As these tools for analyzing the thermodynamic states of geochemical species as a function of temperature, pressure, and composition have multiplied, so too have the possibilities for tracing water-rock interaction from ambient to deep conditions on Earth and beyond. Yet, the aqueous geochemical community still lacks a centralized platform for incorporating this constantly updating thermodynamic data into aqueous geochemical models. Here, we introduce PyGeochemCalc (PyGCC), a community-driven, open-source Python package that meets this need by providing a consolidated set of functions for calculating the thermodynamic properties of gas, aqueous, and mineral (including solid solutions and variable-formula clays) species, as well as reactions amongst these species, over a broad range of temperature and pressure conditions. The PyGCC package utilizes the revised Helgeson-Kirkham-Flowers (HKF) equation of state, and newly proposed density-based extrapolations based upon it, to calculate the thermodynamic properties of aqueous species; a choice of equations of state and electrostatic models (including the Deep Earth Water (DEW) model) to calculate thermodynamic and dielectric properties of water; and heat capacity functions to calculate thermodynamic properties of minerals and gases. Additionally, PyGCC integrates these functions to generate thermodynamic databases for various geochemical programs, including the Geochemist's Workbench (GWB), EQ3/6, TOUGHREACT, and PFLOTRAN, with straightforward possibilities for extension to other simulators. The various functions in the package can also be modularly utilized, and introduced into other modeling tools, as desired. In this paper, we detail the capabilities of PyGCC and the equations it relies on for calculating thermodynamic properties of water, aqueous species, and gases. Although the fundamental thermodynamic data necessary for state-of-the-science PyGCC calculations will necessarily evolve as our collective geochemical knowledge base expands, PyGCC's open source, community-driven design will allow for users to keep pace via rapid implementation of these advancements in this modern geochemical tool.} +} +@ARTICLE{Dick2019, +AUTHOR={Dick, Jeffrey M.}, +TITLE={CHNOSZ: Thermodynamic Calculations and Diagrams for Geochemistry}, +JOURNAL={Frontiers in Earth Science}, +VOLUME={7}, +YEAR={2019}, +URL={https://www.frontiersin.org/articles/10.3389/feart.2019.00180}, +DOI={10.3389/feart.2019.00180}, +ISSN={2296-6463}, +ABSTRACT={Thermodynamic calculations are an essential tool for many areas of geochemistry. Thermodynamics provides a framework for the quantitative description and prediction of the solubilities and relative stabilities of different minerals, metal transport in hydrothermal fluids, and geobiochemical reactions that drive microbial metabolism and contribute to the compositional variation of proteins. Accessible and up-to-date software and databases are important for the development and reproducible application of thermodynamic models. CHNOSZ is a free package for R that has been frequently updated since its first release in 2008. The package provides an integrated set of functions to calculate the standard molal thermodynamic properties and chemical affinities of reactions. It uses the graphical capabilities of R to produce high-quality chemical activity diagrams for aqueous species and predominance diagrams including Eh-pH (Pourbaix) and logfO2 -T diagrams. The extensive database utilizes the well-known revised Helgeson-Kirkham-Flowers (HKF) equations for aqueous species. Recent additions to the database include the Berman equations for minerals, the Deep Earth Water model, which extends the applicability of the HKF equations to higher pressures, and the Akinfiev-Diamond model for aqueous nonelectrolytes. The package comes with many types of documentation, including technical help pages with short code examples, longer code demos, and in-depth vignettes combining code, text and graphics, giving users a wide array of starting points for their own research. This paper provides a concise overview of the package and illustrates the new features using examples selected from the literature. Although the package does not provide a complete chemical speciation model, numerous examples from the package demonstrate the ease of reproducing selected published calculations and sometimes identifying issues with existing datasets and models.} +} +@inproceedings{Bastrakov2019, +author = {Bastrakov, Evgeniy and Dick, Jeffrey}, +year = {2019}, +month = {08}, +pages = {}, +title = {GeoTPD — an interactive online tool for geochemical modelling for the broad geological community. Goldschmidt Abstracts, 2019} +} +@misc{Chan2021, + doi = {10.48550/ARXIV.2105.14096}, + url = {https://arxiv.org/abs/2105.14096}, + author = {Chan, Andrew and Daswani, Mohit Melwani and Vance, Steven}, + keywords = {Geophysics (physics.geo-ph), FOS: Physical sciences, FOS: Physical sciences}, + title = {DEWPython: A Python Implementation of the Deep Earth Water Model and Application to Ocean Worlds}, + publisher = {arXiv}, + year = {2021}, + copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International} +} +@software{megawatsoft, title={Thermodynamic properties of fluids}, year={2022}, url={https://www.megawatsoft.com/}, author = {megawatsoft}, journal={Thermodynamic Properties of Fluids}} +@Article{Huber2022, +author={Huber, Marcia L. +and Lemmon, Eric W. +and Bell, Ian H. +and McLinden, Mark O.}, +title={The NIST REFPROP Database for Highly Accurate Properties of Industrially Important Fluids}, +journal={Industrial {\&} Engineering Chemistry Research}, +year={2022}, +month={Oct}, +day={26}, +publisher={American Chemical Society}, +volume={61}, +number={42}, +pages={15449-15472}, +issn={0888-5885}, +doi={10.1021/acs.iecr.2c01427}, +url={https://doi.org/10.1021/acs.iecr.2c01427} +} +@article{Bell2014, + author = {Bell, Ian H. and Wronski, Jorrit and Quoilin, Sylvain and Lemort, Vincent}, + title = {Pure and Pseudo-pure Fluid Thermophysical Property Evaluation and + the Open-Source Thermophysical Property Library CoolProp}, + journal = {Industrial \& Engineering Chemistry Research}, + volume = {53}, + number = {6}, + pages = {2498--2508}, + year = {2014}, + doi = {10.1021/ie4033999}, + URL = {http://pubs.acs.org/doi/abs/10.1021/ie4033999}, + eprint = {http://pubs.acs.org/doi/pdf/10.1021/ie4033999} + } +@article{Martin2022, doi = {10.21105/joss.04757}, url = {https://doi.org/10.21105/joss.04757}, year = {2022}, publisher = {The Open Journal}, volume = {7}, number = {79}, pages = {4757}, author = {Christopher Martin and Joseph Ranalli and Jacob Moore}, title = {PYroMat: A Python package for thermodynamic properties}, journal = {Journal of Open Source Software} } +@software{bell, +author={Caleb Bell and Contributors}, +year = {2021}, +title={Thermo: Chemical properties component of Chemical Engineering Design Library (ChEDL)}, +url = {https://github.com/CalebBell/thermo} +} +@software{cantera, + author = "David G. Goodwin and Harry K. Moffat and Ingmar Schoegl and Raymond L. + Speth and Bryan W. Weber", + title = "Cantera: An Object-oriented Software Toolkit for Chemical + Kinetics, Thermodynamics, and Transport Processes", + year = 2022, + note = "Version 2.6.0", + howpublished = "\url{https://www.cantera.org}", + doi = {10.5281/zenodo.6387882} +} +@software{thermoengine, + author = {Thermoengine, Code Contributors To}, + title = {{ThermoEngine: Software for Model Building and + Computational Thermodynamics Supporting + Applications in the Earth Sciences}}, + month = may, + year = 2022, + publisher = {Zenodo}, + version = {1.0.0}, + doi = {10.5281/zenodo.6527840}, + url = {https://doi.org/10.5281/zenodo.6527840} +} + + + + diff --git a/paper/paper.ipynb b/paper/paper.ipynb new file mode 100644 index 0000000..b848523 --- /dev/null +++ b/paper/paper.ipynb @@ -0,0 +1,330 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "18f66811-a4c2-4a8a-8817-6b6a57ab1c8b", + "metadata": {}, + "outputs": [], + "source": [ + "import thermofun as fun\n", + "import thermohubclient as client\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8ce44077-ed07-45fa-8db8-80ba23725e43", + "metadata": {}, + "outputs": [], + "source": [ + "#dbc = client.DatabaseClient()\n", + "#dbc.saveDatabase('aq17')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c6e0d78d-57f8-4c87-9471-35d340237c45", + "metadata": {}, + "outputs": [], + "source": [ + "batch = fun.ThermoBatch('aq17-thermofun.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7be922d2-689a-488a-9477-4a220ad4961c", + "metadata": {}, + "outputs": [], + "source": [ + "batch.setPropertiesUnits([\"temperature\", \"pressure\"],[\"degC\",\"bar\"])\n", + "batch.setPropertiesDigits([\"heat_capacity_cp\",\"entropy\", \"volume\", \"temperature\", \"pressure\", \"logKr\"], [4, 4, 4, 0, 2, 3])\n", + "substances = [\"Na+\", \"Ca+2\", \"SiO2@\", \"CO3-2\", 'OH-']\n", + "lables = [\"Na$^{+}$\", \"Ca$^{2+}$\", \"SiO$_{2(aq)}$\", \"CO$_{3}^{2-}$\", 'OH$^-$']\n", + "properties = [\"heat_capacity_cp\"]\n", + "batch.setPressureIncrement(0,0,0)\n", + "batch.setTemperatureIncrement(0,250, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "1c87fe09-31b0-4396-a070-533628fb85cd", + "metadata": {}, + "outputs": [], + "source": [ + "batch.thermoPropertiesSubstance(substances, properties).toCSV(\"results.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "537f8e81-0b21-43d8-8c3d-c2cdbe65d2a0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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