MAT-dp Core

MATerial Demand Projections Model

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

Please install poetry - see here

To install all the project dependencies

poetry install

Then go the examples folder

cd examples

Then run the pizza box example

poetry run python3 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(pizza_box))