Resources

Guides to…

Guides to…

Here are our guides to understanding commonly used terms and phrases within engineering, sustainability, and resource efficiency.

Machine Learning

Machine learning concerns itself with imitating intelligent behaviour in artificial systems. In practice, machine learning involves algorithms that incrementally update, or learn, how to achieve a task by being shown examples in the form of data.

Such algorithms can be…

Supervised – labelled examples in a dataset provide supervision

Unsupervised – no labels provided, and instead tasked with unpicking patterns between datapoints in the dataset

Reinforced – a reinforcing reward is provided for good performance, with respect to the human-defined task

The canonical machine learning use-case is in captioning pictures of cats and dogs a supervised algorithm is shown a dataset of pictures each with a label describing whether the picture represents a cat or dog. The algorithm updates its parameters until it can be shown a new picture and correctly predict its content.

Exergy

Exergy is a measure of resource quality. Resource quality refers to the amount of work a system can perform before it reaches equilibrium with the environment.

The greater the difference between the system’s characteristic (e.g. temperature) and the reference environment, the higher the exergy that can be extracted.

At ambient temperature of 15°C, 1 Joule of heat at 1000°C can produce 0.77 J of work, while 1 J of heat at 30°C only produces 0.05 J of work.

1 J of electricity is pure exergy so it represents more exergy that 1 Joule of heat. Unlike energy, which is transformed, exergy is always destroyed (2nd law of thermodynamics).

Also known as resource efficiency, exergy efficiency expresses how far a process is from its theoretical minimum. The theoretical minimum represents the best possible performance if there are no losses within the system.

Exergy efficiency illustrates how much output can be obtained from the total inputs.

ε = exergy output / exergy input

Traceability

Traceability is the ability to access specific information about a product captured and integrated with its recorded identification throughout the supply chain. 

To execute traceability, a business operator groups raw materials and products as batches or lots and assigns them discrete identifiers. 

The business operator uses the identifiers to record the transformations (e.g. mixing the batches) – noting their inputs, outputs and intrinsic characteristics.

All or part of the recorded information in the business operator is then transferred, with the product, to the next supply chain link.

This creates an information trail that allows us to follow the product movement within business operators and throughout the supply chain. 

The traceability information can be used for various purposes such as:

– inventory management 

– real time product quality monitoring 

–quick recall of affected product lots 

Traceability technologies have great potential to improve sustainable performance by reducing food loss. 

Read more of Refficiency’s Samantha’s work on this here.