========= Chemicals

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Key Features & Capabilities

The chemicals library features an extensive compilation of pure component chemical data that can serve engineers, scientists, technicians, and anyone working with chemicals. The chemicals library facilitates the retrieval and calculation of:

• Chemical constants including formula, molecular weight, normal boiling and melting points, triple point, heat of formation, absolute entropy of formation, heat of fusion, similarity variable, dipole moment, acentric factor, etc.

• Assorted information of safety and toxicity of chemicals.

• Methods (and their respective coefficients) for the calculation of temperature and pressure dependent chemical properties including vapor pressure, heat capacity, molar volume, thermal conductivity, surface tension, dynamic viscosity, heat of vaporization, relative permittivity, etc.

• Methods to solve thermodynamic phase equilibrium, including flash routines, vapor-liquid equilibrium constant correlations, and both numerical and analytical solutions for the Rachford Rice and Li-Johns-Ahmadi equations. Rashford Rice solutions for systems of 3 or more phases are also available.

Data for over 20,000 chemicals are made available as local databanks in this library. All databanks are loaded on-demand, saving loading time and RAM. For example, if only data on the normal boiling point is required, the chemicals library will only load normal boiling point datasets. This on-demand loading feature makes the chemicals library an attractive dependence for software modeling chemical processes. In fact, The Biorefinery Simulation and Techno-Economic Analysis Modules (BioSTEAM) <https://biosteam.readthedocs.io/en/latest/>_ is reliant on the chemicals library for the simulation of unit operations.

The chemicals library also supports integration with Numba <https://numba.pydata.org/>, a powerful accelerator that works well with NumPy; Pint <https://pint.readthedocs.io/en/stable/> Quantity objects to keep track of units of measure; and NumPy vectorized <https://numpy.org/doc/stable/reference/generated/numpy.vectorize.html>_ functions.

If you need to know something about a chemical, give chemicals a try.

Installation

Get the latest version of chemicals from https://pypi.python.org/pypi/chemicals/

If you have an installation of Python with pip, simple install it with:

$ pip install chemicals

If you are using conda <https://docs.conda.io/en/latest/>_, you can install chemicals from conda-forge channel:

$ conda install -c conda-forge chemicals

To get the git version, run:

$ git clone git://github.com/CalebBell/chemicals.git

Documentation

chemicals's documentation is available on the web:

http://chemicals.readthedocs.io/

Getting Started

The library is designed around SI units. The retrieval of constant chemical properties is done by CASRN:

.. code-block:: python

>>> from chemicals import CAS_from_any, MW, Tb, Tm, Tc, Pc, Vc, Hfus, Hfs, Hfl, Hfg, S0s, S0l, S0g
>>> # Search for CASRN by a common name or a unique identifier such as the IchI key
>>> CAS_water = CAS_from_any('Water')
>>> MW(CAS_water)   # Molecular weight [g/mol]
18.01528
>>> Tb(CAS_water)   # Normal boiling point [K]
373.124
>>> Tm(CAS_water)   # Melting point [K]
273.15
>>> Tc(CAS_water)   # Critical temperature [K]
647.096
>>> Pc(CAS_water)   # Critical pressure [Pa]
22064000.0
>>> Vc(CAS_water)   # Critical volume [m^3/mol]
5.59480372671e-05
>>> Hfus(CAS_water) # Heat of fusion [J/mol]
6010.0
>>> Hfs('101-81-5')  # Solid heat of formation of Diphenylmethane, [J/mol] 
71500.0
>>> Hfl(CAS_water)  # Liquid heat of formation [J/mol]
-285825.0
>>> Hfg(CAS_water)  # Gas heat of formation [J/mol] 
-241822.0
>>> S0s('101-81-5')  # Absolute solid enthalpy of formation of Diphenylmethane [J/mol/K]
239.3
>>> S0l(CAS_water)  # Absolute liquid enthalpy of formation [J/mol/K]
70.0
>>> S0g(CAS_water)  # Absolute gas enthalpy of formation [J/mol/K]
188.8

Such "lookup functions" search through many databanks until the value for the given CASRN is found. When no value is available, None is returned. Note that chemicals is not a project to exhaustively obtain recommended property values for all properties and chemicals; it is a collection of cited and openly published data and equations.

You can optionally pass a "method" to select which from databank to retrieve data:

.. code-block:: python

>>> Tb(CAS_water, method='YAWS')
373.15

To view all available methods for a given chemical, just use the functions with "_methods" tagged at the end of the name:

.. code-block:: python

>>> from chemicals import Tb_methods
>>> Tb_methods(CAS_water)
['HEOS', 'CRC_INORG', 'COMMON_CHEMISTRY', 'WEBBOOK', 'YAWS', 'WIKIDATA']

The databanks can also be accessed through their respective module:

from chemicals.critical import critical_data_Yaws critical_data_Yaws # doctest: +SKIP Chemical Tc Pc Vc omega Zc CASRN
100-00-5 p-chloronitrobenzene 751.00 3980000.0 0.000432 0.491 0.275355 100-01-6 p-nitroaniline 851.00 4420000.0 0.000406 0.782 0.253621 100-10-7 p-dimethylaminobenzaldehyde 832.00 3070000.0 0.000471 0.527 0.209027 100-18-5 p-diisopropylbenzene 689.00 2450000.0 0.000598 0.390 0.255749 100-21-0 terephthalic acid 1113.00 3950000.0 0.000424 1.059 0.180981 ... ... ... ... ... ... ... 99814-65-0 1-pentadeceN-3-ol 713.00 1500000.0 0.000850 NaN 0.215149 999-21-3 diallyl maleate 693.00 2330000.0 0.000606 0.789 0.245054 999-52-0 3-chloroheptane 621.08 2693000.0 0.000476 0.418 0.248495 999-78-0 4,4-dimethyl-2-pentyne 552.81 3416000.0 0.000378 0.191 0.281303 999-97-3 hexamethyldisilazane 544.00 1920000.0 0.000613 0.510 0.260213

[7549 rows x 6 columns]

An extensive set of functions for calculating all sorts of chemical properties are available along with their respective coefficients for a wide range of chemicals:

from chemicals import Antoine from chemicals.vapor_pressure import Psat_data_AntoinePoling antoine_coefficients = Psat_data_AntoinePoling.loc[CAS_water] # For calculating saturated vapor pressure antoine_coefficients # doctest: +SKIP Chemical water A 10.1 B 1.69e+03 C -43 Tmin 273 Tmax 473 Name: 7732-18-5, dtype: object A, B, C = float(antoine_coefficients['A']), float(antoine_coefficients['B']), float(antoine_coefficients['C']) T = 373.15 # Temperature [K] Antoine(T, A, B, C) # Vapor pressure [Pa] 101047.2535

To use JIT compiled functions, import the numba module:

from chemicals import numba # doctest: +SKIP numba.Antoine(T, A, B, C) # doctest: +SKIP 101047.2535

To use Quantity objects, import the units module:

from chemicals import units from chemicals.units import u units.Antoine(T u.K, A, B u.K, C * u.K) <Quantity(101047.254, 'pascal')>

To use vectorized functions, import the vectorized module:

from chemicals import vectorized vectorized.Antoine([300, 350], A, B, C) array([ 3546.98, 41603.98 ])

Roadmap

The authors' main development item is to provide the latest methods for the calculation of chemical properties (both thermodynamic and transport) and extending the local databank.

Latest source code

The latest development version of chemicals's sources can be obtained at

https://github.com/CalebBell/chemicals

Bug reports

To report bugs, please use the chemicals's Bug Tracker at:

https://github.com/CalebBell/chemicals/issues

License information

See LICENSE.txt for information on the terms & conditions for usage of this software, and a DISCLAIMER OF ALL WARRANTIES.

Although not required by the chemicals license, if it is convenient for you, please cite chemicals if used in your work. Please also consider contributing any changes you make back, and benefit the community.

Citation

To cite chemicals in publications use:

Caleb Bell, Yoel Rene Cortes-Pena, and Contributors (2016-2024). Chemicals: Chemical properties component of Chemical Engineering Design Library (ChEDL)
https://github.com/CalebBell/chemicals.