Basic example for using AeroMAPS with Jupyter Notebook

Basic example for using AeroMAPS with Jupyter Notebook#

This document represents both an example and a tutorial for using the basic functions of AeroMAPS to simulate and evaluate a transition scenario for the aviation sector. It is written in the form of a Jupyter Notebook to facilitate its use. The user will be able to adapt this notebook and couple it with other notebooks proposed in the documentation.

Load and process#

First, the user has to load the framework and generate a process.

%matplotlib widget
from aeromaps.core.process import create_process
from aeromaps.core.models import (
    models_traffic,
    models_efficiency_top_down,
    models_energy_without_fuel_effect,
    models_offset,
    models_climate_simple_gwpstar,
    models_sustainability,
    models_costs_top_down,
)

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
Cell In[1], line 2
      1 get_ipython().run_line_magic('matplotlib', 'widget')
----> 2 from aeromaps.core.process import create_process
      3 from aeromaps.core.models import (
      4     models_traffic,
      5     models_efficiency_top_down,
   (...)
     10     models_costs_top_down,
     11 )

ImportError: cannot import name 'create_process' from 'aeromaps.core.process' (/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/aeromaps/core/process.py)

models = {
    "models_traffic": models_traffic,
    "models_efficiency_top_down": models_efficiency_top_down,
    "models_energy_without_fuel_effect": models_energy_without_fuel_effect,
    "models_offset": models_offset,
    "models_climate_simple_gwpstar": models_climate_simple_gwpstar,
    "models_sustainability": models_sustainability,
    "models_costs_top_down": models_costs_top_down,
}

process = create_process(models=models)

Set up variables#

The user can then set the different parameters of the model to generate its scenario. The list of the float inputs can be displayed using this command.

process.list_float_inputs()

In the following, some main parameters are for instance set up.

# Air traffic evolution

## Growth rate by category [%]
process.parameters.cagr_passenger_short_range_reference_periods = [2020, 2030, 2040, 2050]
process.parameters.cagr_passenger_short_range_reference_periods_values = [3.0, 2.0, 1.0]
process.parameters.cagr_passenger_medium_range_reference_periods = []
process.parameters.cagr_passenger_medium_range_reference_periods_values = [3.0]
process.parameters.cagr_passenger_long_range_reference_periods = []
process.parameters.cagr_passenger_long_range_reference_periods_values = [3.0]
process.parameters.cagr_freight_reference_periods = []
process.parameters.cagr_freight_reference_periods_values = [3.0]

# Aircraft fleet and operation evolution - Aircraft load factor

## Aircraft load factor in 2050 [%]
process.parameters.load_factor_end_year = 85  # 2019 value: 82.399312

# Aircraft fleet and operation evolution - Aircraft efficiency using the top-down approach

## Drop-in aircraft
### Mean annual efficiency gains by category [%]
process.parameters.energy_per_ask_short_range_dropin_fuel_gain_reference_years = []
process.parameters.energy_per_ask_short_range_dropin_fuel_gain_reference_years_values = [1.5]
process.parameters.energy_per_ask_medium_range_dropin_fuel_gain_reference_years = []
process.parameters.energy_per_ask_medium_range_dropin_fuel_gain_reference_years_values = [1.5]
process.parameters.energy_per_ask_long_range_dropin_fuel_gain_reference_years = []
process.parameters.energy_per_ask_long_range_dropin_fuel_gain_reference_years_values = [1.5]

## Hydrogen aircraft
### Values for setting logistic functions by category
process.parameters.hydrogen_final_market_share_short_range = 50.0  # [%]
process.parameters.hydrogen_introduction_year_short_range = 2035
process.parameters.fleet_renewal_duration = 20.0
### Relative energy consumption for hydrogen aircraft with respect to drop-in aircraft [%]
process.parameters.relative_energy_per_ask_hydrogen_wrt_dropin_short_range_reference_years = []
process.parameters.relative_energy_per_ask_hydrogen_wrt_dropin_short_range_reference_years_values = [
    1.0
]

# Aircraft fleet and operation evolution - Operations

## Values for setting the logistic function
process.parameters.operations_final_gain = 8.0  # [%]
process.parameters.operations_start_year = 2025
process.parameters.operations_duration = 25.0

# Aircraft energy - Introduction of alternative drop-in fuels

## Share of alternative fuels in the drop-in fuel mix (the rest being supplemented by kerosene) [%]
process.parameters.biofuel_share_reference_years = [2020, 2030, 2040, 2050]
process.parameters.biofuel_share_reference_years_values = [0.0, 4.8, 24.0, 35.0]
process.parameters.electrofuel_share_reference_years = [2020, 2030, 2040, 2050]
process.parameters.electrofuel_share_reference_years_values = [0.0, 1.2, 10.0, 35.0]

# Carbon offset
process.parameters.carbon_offset_baseline_level_vs_2019_reference_periods = [2020, 2024, 2050]
process.parameters.carbon_offset_baseline_level_vs_2019_reference_periods_values = [100.0, 85.0]
process.parameters.residual_carbon_offset_share_reference_years = [2020, 2030, 2040, 2050]
process.parameters.residual_carbon_offset_share_reference_years_values = [0.0, 0.0, 20.0, 50.0]

# Environmental limits

## Carbon budgets and Carbon Dioxide Removal [GtCO2]
process.parameters.net_carbon_budget = 850.0
process.parameters.carbon_dioxyde_removal_2100 = 280.0

## Available energy resources in 2050 [EJ]
process.parameters.waste_biomass = 12
process.parameters.crops_biomass = 63
process.parameters.forest_residues_biomass = 17
process.parameters.agricultural_residues_biomass = 57
process.parameters.algae_biomass = 15
process.parameters.available_electricity = 250.0

# Allocation settings

## Aviation share of the global (equivalent) carbon budget [%]
process.parameters.aviation_carbon_budget_allocated_share = 2.6
process.parameters.aviation_equivalentcarbonbudget_allocated_share = 5.1

## Aviation share of the global energy resources (biomass and electricity) [%]
process.parameters.aviation_biomass_allocated_share = 5.0
process.parameters.aviation_electricity_allocated_share = 5.0

# Various environmental settings

## Share of biofuel production pathways (the rest being completed by AtJ processes) [%]
process.parameters.biofuel_hefa_fog_share_reference_years = [2020, 2030, 2040, 2050]
process.parameters.biofuel_hefa_fog_share_reference_years_values = [100, 100, 0.7, 0.7]
process.parameters.biofuel_hefa_others_share_reference_years = [2020, 2030, 2040, 2050]
process.parameters.biofuel_hefa_others_share_reference_years_values = [0.0, 0.0, 3.8, 3.8]
process.parameters.biofuel_ft_others_share_reference_years = [2020, 2030, 2040, 2050]
process.parameters.biofuel_ft_others_share_reference_years_values = [0.0, 0.0, 76.3, 76.3]
process.parameters.biofuel_ft_msw_share_reference_years = [2020, 2030, 2040, 2050]
process.parameters.biofuel_ft_msw_share_reference_years_values = [0.0, 0.0, 7.4, 7.4]

## Emission factors for electricity (2019 value: 429 gCO2/kWh)
process.parameters.electricity_emission_factor_reference_years = [2020, 2030, 2040, 2050]
process.parameters.electricity_emission_factor_reference_years_values = [429.0, 200.0, 100.0, 30.0]

## Share of hydrogen production pathways (the rest being completed by production via coal without CCS) [%]
## Distribution in 2019: Gas without CCS (71%), Coal without CCS (27%), Electrolysis (2%), Others with CCS (0%), Co-products not taken into account
process.parameters.hydrogen_electrolysis_share_reference_years = [2020, 2030, 2040, 2050]
process.parameters.hydrogen_electrolysis_share_reference_years_values = [2, 100, 100, 100]
process.parameters.hydrogen_gas_ccs_share_reference_years = [2020, 2030, 2040, 2050]
process.parameters.hydrogen_gas_ccs_share_reference_years_values = [0, 0, 0, 0]
process.parameters.hydrogen_coal_ccs_share_reference_years = [2020, 2030, 2040, 2050]
process.parameters.hydrogen_coal_ccs_share_reference_years_values = [0, 0, 0, 0]
process.parameters.hydrogen_gas_share_reference_years = [2020, 2030, 2040, 2050]
process.parameters.hydrogen_gas_share_reference_years_values = [71, 0, 0, 0]

Compute#

Once all the parameters have been set up, the user can compute.

process.compute()

Results#

The user can then display the results. The user has access to float outputs but also to annual data outputs, with the possibility of choosing the output.

process.data["float_outputs"]

process.data["vector_outputs"]

process.data["climate_outputs"][["co2_emissions"]]

The user can write the results to an Excel file with the following command.

process.write_excel(file_name="aeromaps_data.xlsx")

Plots#

Lastly, the user can also plot different parameters based on a database of plots. The list of the available plots can be displayed using the following command.

process.list_available_plots()

The user can thus display a figure of this list, for instance the CO₂ emissions of the simulated scenario.

process.plot("air_transport_co2_emissions", save=False)