Direct Air Capture Example

Direct Air Capture Example#

This script illustrates how to run a simulation with ETHOS.RESKit.DAC.

import reskit as rk
import pandas as pd
import numpy as np

Preparations#

In ETHOS.RESKit.DAC one can simulate each DAC plant individually with its own characteristics. The DAC model, i.e., the relationship of weather conditions (T/RH) to energy demand and water production/consumption need to be clear and can be added to reskit.dac.data. Otherwise predefined DAC models from literature, i.e., Sendi (10.1016/j.oneear.2022.09.003) or Jajjawi (http://dx.doi.org/10.2139/ssrn.5230783) can be used. If nothing is specified the Jajjawi model will be used.

latitude
longitude
capacity [tCO2/h]

These specifications are given in the form of a pandas DataFrame.

# Create Placements DataFrame with DAC locations and specifications

placements = pd.DataFrame(
    {
        "lon": [5.985195, 5.5, 5.5, 6.3],
        "lat": [50.797254, 50.794208, 50, 51],
        "capacity": [1, 10, 5, 5],  # capacity of the DAC plant to simulate in t/h
    }
)
placements

	lon	lat	capacity
0	5.985195	50.797254	1
1	5.500000	50.794208	10
2	5.500000	50.000000	5
3	6.300000	51.000000	5

import os

# Load the data for weather depending DAC performance
data = pd.read_csv(os.path.join(rk.dac.data.DATAFOLDER, "LT_jajjawi.csv"), index_col=0)
data

	T	RH	totalElectricity	totalThermal	relProd	waterDesorption
0	-10.0	5	-0.573642	-3.576070	0.408678	0.136511
1	-10.0	10	-0.540579	-3.145415	0.497106	0.274053
2	-10.0	15	-0.492924	-2.852538	0.587032	0.396858
3	-10.0	20	-0.471066	-2.672325	0.672805	0.507330
4	-10.0	25	-0.460583	-2.568317	0.749496	0.610986
...	...	...	...	...	...	...
135	50.0	80	-1.003423	-4.271715	0.710683	4.188082
136	50.0	85	-1.066476	-4.581134	0.718601	4.590336
137	50.0	90	-1.102669	-4.948372	0.691001	4.957865
138	50.0	95	-1.177765	-5.245909	0.726581	5.367870
139	50.0	100	-1.219029	-5.726162	0.655221	5.757885

140 rows × 6 columns

Run the simulation workflow#

RESKit will run the simulation and create an xarray Dataset with the simulation results for you. Apart from the capacity_factor (relative productivity), RESKit also includes the conversion factors (per ton CO2) and intermediate data used to determine the capacity factor (such as air temperature and relative humidity)

RESKit will output the capacity factor of each location for every hour of the simulated year.

reskit_xr["capacity_factor"].isel(time=slice(0, 400)).plot.line(x="time")

[<matplotlib.lines.Line2D at 0x794151ac2360>,
 <matplotlib.lines.Line2D at 0x794151ac1910>,
 <matplotlib.lines.Line2D at 0x794151ac12e0>,
 <matplotlib.lines.Line2D at 0x794151aa32f0>]

../../_images/5b059676d19723461fb0f7263934ca34bc1f3ed95f2876d2cc88623a21e03063.png

As well as the conversion factors:

reskit_xr["conversion_factor_heat"].isel(time=slice(0, 400)).plot.line(x="time")

[<matplotlib.lines.Line2D at 0x794151922780>,
 <matplotlib.lines.Line2D at 0x794151922120>,
 <matplotlib.lines.Line2D at 0x79415193cda0>,
 <matplotlib.lines.Line2D at 0x7941487044a0>]

../../_images/68dd4c76efe8b5b05801432f5af6311f20c48be51ffa6b6eed54dfc73f393bf8.png

For an electrified liquid solvent (High temperature, HT)-DAC Plant:

reskit_xr2["conversion_factor_electricity"].isel(time=slice(0, 400)).plot.line(x="time")

[<matplotlib.lines.Line2D at 0x794148766780>,
 <matplotlib.lines.Line2D at 0x7941487668d0>,
 <matplotlib.lines.Line2D at 0x794148723cb0>,
 <matplotlib.lines.Line2D at 0x7941487a00b0>]

../../_images/d975e1b45b22c9f94ac2f93567d5c684dec052e76ce9cf72d46f1ce5e5178baa.png

Direct Air Capture Example

Contents

Direct Air Capture Example#

Preparations#

Run the simulation workflow#