Skip to content

Latest commit

 

History

History
194 lines (126 loc) · 6.53 KB

README.md

File metadata and controls

194 lines (126 loc) · 6.53 KB
Raw

Mat-dp core

Material Demand Projections Model

DOI badge

Welcome to the Mat-dp core. This repo represents the core of the Mat-dp project, which aims to deliver user-friendly and open-access software to study the environmental implications of materials used for building low-carbon systems.

Installation and launch

You can find mat-dp-core in PyPi. You can then install it using:

pip install mat-dp-core

There is an examples folder you can access either navigating to it or using the following command:

cd examples

You can then run examples, such as the pizza box example called test.py

Concepts

Definitions

The following terms will be used frequently:

Resource - A resource to be produced or consumed, such as steel or aluminium.

Process - A process which produces and/or consumes resources.

Constraint - A condition the system is placed under.

  • Run Ratio Constraint - A constraint that fixes the ratio of runs between two processes - e.g. wind and solar will run at a ratio of 1:2.

  • Resource Constraint - A constraint on the amount of resource produced, e.g. we must produce at least 10 energy.

  • Run Eq Constraint - A constraint that specifies the number of runs a process must make.

Objective - The objective function is the property of the system which will be minimised. This could be something like the number of runs of the system, or the total cost.

Measurement - a measurement taken of the solved system, determining the

Usage - High Level

Introduction

The below describes a practical example of using MAT-dp. Imagine...

  • Pizza boxes are made from cardboard and recycled cardboard. (process/resource)
  • There are different processes for making them, which have different ratios of cardboard:recycled_cardboard . (process)
  • We wish to priorites the process that uses the most recycled cardboard, but not so as to eliminate the less efficient version. (ratio constraint)
  • We then, rather inefficiently, burn them to produce energy. (process)
  • We must produce at least 8 kWh of energy to survive the frosty winters. (resource constraint)
  • We wish to only generate the minimum amount of cardboard and pizza boxes. (objective)
  • How many pizza boxes must we burn to survive? (measurement)

Step 1: Define resources

Firstly we must define all the resources we wish to use, with their name and units.

from mat_dp_core import Resources

resources = Resources()
cardboard = resources.create("cardboard", unit="m2")
recycled_cardboard = resources.create("recycled_cardboard", unit="m2")
pizza_box = resources.create("pizza_box")
energy = resources.create("energy", unit="kWh")

Step 2: Define processes

We must now take these resources and use them to define our processes. These are defined by a name and the resources that they produce and consume.

from mat_dp_core import Processes
processes = Processes()
cardboard_producer = processes.create("cardboard producer", (cardboard, +1))
recycled_cardboard_producer = processes.create(
    "recycled cardboard producer", (recycled_cardboard, +1)
)
pizza_box_producer = processes.create(
    "pizza box producer",
    (recycled_cardboard, -0.5),
    (cardboard, -2),
    (pizza_box, 1),
)
recycled_pizza_box_producer = processes.create(
    "recycled pizza box producer",
    (recycled_cardboard, -3),
    (cardboard, -1),
    (pizza_box, 1),
)
power_plant = processes.create("power plant", (pizza_box, -1), (energy, 4))
energy_grid = processes.create("energy grid", (energy, -2))

Step 3: Define constraints

Now we need to define the constraints of the problem. We want to specify we take equal amounts of pizza boxes from each producer (Run ratio constraint), and that we only require 8 kWh of energy (Resource constraint):

from mat_dp_core import EqConstraint
constraints = [
    EqConstraint(
        "recycled pizza box ratio",
        pizza_box_producer - recycled_pizza_box_producer,
        0,
    ),
    EqConstraint("required energy", energy_grid, 8),
]

Step 4: Define an objective function

Once we've established all of our constraints, we must define an objective function. The below example specifies we minimise the total number of runs:

# Minimise total number of runs
objective = (
    cardboard_producer
    + recycled_cardboard_producer
    + pizza_box_producer
    + recycled_pizza_box_producer
    + power_plant
    + energy_grid
)

Step 5: Make a measurement

We must now measure the number of pizza boxes to burn.

from mat_dp_core import Measure

measurement = Measure(resources, processes, constraints, objective)

print(measurement.resource(resource = pizza_box))

For a more stylised version of the print statement, the following may be used:

for process in measurement.resource(resource=pizza_box):
    print(str(process[0].name).ljust(50) + ":  " + str(round(process[1], 1)))

Visualising the documentation

To view the documentation in html format, go to this website or run the documentation through mkdocs using the following command at the root of the repository:

poetry run mkdocs serve

Contributing to Mat-dp

Contributions are welcome!

If you see something that needs to be improved, open an issue in the respective section of the repository. If you have questions, need assistance or need better instructions for contributing, please get in touch via e-mail mentioning "Mat-dp" in the subject.

Developers of mat-dp-core need to make changes using poetry with the following instructions:

Please install poetry- please see here

Then, install mat-dp-core with:

poetry add mat-dp-core

To install all the project dependencies

poetry install

Then go the examples folder

cd examples

Then run the pizza box example to test everything works.

poetry run python3 test.py

For any questions on how to use the software, please refer to the documentation. It contains useful definitions and examples of using the software. Please contact us by e-mail for any other support requried.