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Calculating partition coefficients

This workbook shows you how to calculate partition coefficients and sulfide compositions using the model of Kiseeva et al. (2015) and Brenan (2015)
You load in matrix glasses, calculate a temperature using the Python module Thermobar from this liquid composition, and then feed this into the calculate Kiseeva…. function You can download the excel file here:
https://github.com/PennyWieser/PySulfSat/blob/main/docs/Examples/Other_Useful_Functions/Glass_input_example.xlsx

Here we load various python packages

[1]:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import PySulfSat as ss
import Thermobar as pt
pd.options.display.max_columns = None
ss.__version__

[1]:

'0.0.17'

Here we load our glass data using the Thermobar load structure

First, this looks for column headings for each phase.
Here, because we only have liquids, I haven’t bothered to add “_Liq” after each oxide, so you just need to tell the function that.

[2]:

In=pt.import_excel('Glass_input_example.xlsx', suffix="_Liq", sheet_name='Glass_input')
Liqs=In['Liqs'] # This pulls out just the columns it needs for liquids

This calculates temperature using the Sugawara thermometer

At 3.2 kbar presure

[3]:

Sugawara_T=pt.calculate_liq_only_temp(liq_comps=Liqs, equationT="T_Sug2000_eq3_ol", P=3.2)

This calculates the Kd using Kiseeva et al. and the sulfie composition, as well as the Kds from Brenan (2015) for Se and Te

You need to tell it: - the Ni and Cu content of the sulfide - The FeOt content of the liquid - The temperature in Kelvin - The proportion of Fe3Fet in your liquid

[4]:

Calc_Kd=ss.calculate_sulf_kds(Ni_Sulf=2.2,
                                    Cu_Sulf=12.804,
                                       FeOt_Liq=Liqs['FeOt_Liq'],
                                       T_K=Sugawara_T,
                                       Fe3Fet_Liq=0)

[5]:

Calc_Kd.head()

[5]:

	S_Sulf	O_Sulf	Fe_Sulf	Ni_Sulf	Cu_Sulf	DNi	DCu	DAg	DPb	DZn	DCd	DTl	DMn	DIn	DTi	DGa	DSb	DCo	DV	DGe	DCr	DSe_B2015	DTe_B2015
0	28.889774	3.007096	53.099130	2.2	12.804	831.971727	765.753580	805.083251	27.422926	1.417481	57.390194	7.632734	0.348569	10.527493	0.008983	0.022206	11.778163	37.556741	0.147699	0.363550	0.886372	790.758001	6933.354144
1	28.798040	3.063111	53.134849	2.2	12.804	828.505827	765.752606	806.073742	27.017971	1.390299	56.714297	7.527723	0.342776	10.322400	0.009045	0.021577	11.417914	36.983452	0.145170	0.350906	0.866265	766.086469	6662.618410
2	28.717732	3.112149	53.166119	2.2	12.804	810.570719	748.623170	788.909667	26.496807	1.373343	55.661209	7.449138	0.340263	10.121148	0.009152	0.021600	11.243449	36.252072	0.144834	0.347752	0.857510	744.959829	6430.392157
3	28.730993	3.104051	53.160955	2.2	12.804	806.475753	743.475584	783.348165	26.498263	1.378935	55.607301	7.467304	0.341787	10.139992	0.009154	0.021857	11.331808	36.256183	0.145720	0.351747	0.862817	748.418342	6468.427547
4	28.978200	2.953101	53.064699	2.2	12.804	848.358523	780.250434	819.342051	27.981626	1.439479	58.480965	7.726977	0.352340	10.760029	0.008895	0.022365	12.029887	38.342135	0.148752	0.369913	0.899370	815.088357	7199.689937

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