multifunctional 0.1

Code for handling multifunctional activities in Brightway

multifunctional

Handling multifunctional activities in the Brightway LCA software framework.

Installation

You can install multifunctional via [pip] from [PyPI]:

$ pip install multifunctional

It is also available on anaconda using mamba or conda at the cmutel channel:

mamba install -c conda-forge -c cmutel multifunctional

Usage

Multifunctional activities can lead to linear algebra problems which don't have exactly one solution. Therefore, we commonly need to apply a handling function to either partition such activities, or otherwise manipulate their data such that they allow for the creation of a square and non-singular technosphere matrix.

This library is designed around the following workflow:

1. Multifunctional process(es) are created and saved in a MultifunctionalDatabase. A multifunctional process is any process with multiple functional edges, either outputs (e.g. products) and/or input (e.g. wastes). Each functional edge must be labelled functional=True.
2. The user specifies an allocation strategy for the database:

database_obj.metadata['default_allocation'] = 'price'
database_obj.metadata.flush()

3. Process the database to create the allocated monofunctional processes: database_obj.process().
4. LCA calculations can then be done as normal. See dev/basic_example.ipynb for a simple example.

Substitution

You don't need to use library for substitution, that already works natively in Brightway. Just produce a product which another process also produces (i.e. has the same database name and code), and the production amount of the other process will be reduced as needed to meet the functional unit demand.

Classifying functional edges

There is currently no built-in functionality to determine if an edge is functional based on its attributes - instead we rely on the label functional being manually specified. You can write a function to iterate over datasets and label the functional edges in whatever fashion you choose.

Built-in allocation functions

multifunctional includes the following built-in property-based allocation functions:

• price: Does economic allocation based on the property "price" in each functional edge.
• mass: Does economic allocation based on the property "mass" in each functional edge.
• manual: Does economic allocation based on the property "manual" in each functional edge.

Property-based allocation assumes that each functional edge has a properties dictionary, and this dictionary has the relevant key with a corresponding numeric value. For example, for price allocation, each functional edge needs to have 'properties' = {'price': some_number}.

The allocation strategy equal is also included, though mostly for testing purposes. This divides impacts equally across each functional edge.

Custom property-based allocation functions

To create new property-based allocation functions, add an entry to allocation_strategies using the function property_allocation:

import multifunctional as mf
mf.allocation_strategies['foo'] = property_allocation('bar')

Additions to allocation_strategies are not persisted, so need to be repeated in each Python interpreter.

Custom single-factor allocation functions

To create custom allocation functions which apply a single allocation factor to all nonfunctional inputs and outputs, pass a function to multifunctional.allocation.generic_allocation. This function needs to accept the following input arguments:

• edge_data (dict): Data on functional edge
• node: An instance of multifunctional.MaybeMultifunctionalProcess

The custom function should return a number.

The custom function needs to be curried and added to allocation_strategies. You can follow this example:

import multifunctional as mf
from functools import partial

def allocation_factor(edge_data: dict, node: mf.MaybeMultifunctionalProcess) -> float:
   """Nonsensical allocation factor generation"""
   if edge_data.get("unit") == "kg":
      return 1.2
   elif "silly" in node["name"]:
      return 4.2
   else:
      return 7

mf.allocation_strategies['silly'] = partial(mf.generic_allocation, func=allocation_factor)

Other custom allocation functions

To have complete control over allocation, add your own function to allocation_strategies. This function should take an input of multifunctional.MultifunctionalProcess, and return a list of dictionaries. These dictionaries can follow the normal ProcessWithReferenceProduct data schema, but the new node datasets need to also include the following:

• multifunctional_parent_id: Integer database id of the source multifunctional process
• type: Should always be "readonly_process"

Furthermore, the code of the allocated processes (allocated_product_code) must be written to each functional edge (and that edge saved so this data is persisted). See the code in multifunctional.allocation.generic_allocation for an example.

How does it work?

Recent Brightway versions allow users to specify which graph nodes types should be used when building matrices, and which types can be ignored. We create a multifunctional process node with the type multifunctional, which will be ignored when creating processed datapackages. However, in our database class MultifunctionalDatabase we change the function which creates these processed datapackages to load the multifunctional processes, perform whatever strategy is needed to handle multifunctionality, and then use the results of those handling strategies (e.g. monofunctional processes) in the processed datapackage.

We also tell MultifunctionalDatabase to load a new ReadOnlyProcessWithReferenceProduct process class instead of the standard Activity class when interacting with the database. This new class is read only because the data is generated from the multifunctional process itself - if updates are needed, either that input process or the allocation function should be modified.

Contributing

Contributions are very welcome. To learn more, see the Contributor Guide.

License

Distributed under the terms of the BSD 3 Clause license, multifunctional is free and open source software.

Issues

If you encounter any problems, please file an issue along with a detailed description.

Building the Documentation

You can build the documentation locally by installing the documentation Conda environment:

conda env create -f docs/environment.yml

activating the environment

conda activate sphinx_multifunctional

and running the build command:

sphinx-build docs _build/html --builder=html --jobs=auto --write-all; open _build/html/index.html