Skip to content
This is NOT an official site of the Government of Canada. Click here for the official AI registry.

Helpful Places

Government of Canada – DTPR AI Register

Back to your search

AI-Powered Gas Property Forecasting for Electric Arc Furnaces

Research & Development · Planning & Decision-making

What it collects

About a measurement
Anonymized data
  • Historical and future covariates describing electric arc furnace operating conditions: gas composition, flow rate, temperature, and pressure readings from EAF industrial processes.
Operational data
Anonymized data
  • Public and private operational datasets describing EAF process conditions, used as training and inference inputs for the probabilistic forecasting model.
Run by
Natural Resources Canada (NRCan)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Research & DevelopmentPlanning & Decision-making

This tool forecasts stochastic properties — gas composition, flow rate, temperature, and pressure — of electric arc furnace (EAF) operations over time. It is used by Natural Resources Canada modellers and industry partners to support industrial process planning. The system is not disclosed to end users as AI, and its outputs inform modelling work rather than affecting individuals directly.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Historical and future covariates describing electric arc furnace operating conditions: gas composition, flow rate, temperature, and pressure readings from EAF industrial processes.
Operational data
Anonymized data
  • Public and private operational datasets describing EAF process conditions, used as training and inference inputs for the probabilistic forecasting model.

Processing

Classification & Prediction
  • Probabilistic time series forecasting using past and future covariates to predict EAF gas composition, flow rate, temperature, and pressure; employs stochastic modelling approaches to characterize uncertainty in industrial process outputs.

What it does

Deciding (Analytical AI)
Human decides
  • Generates probabilistic forecasts (scores and predicted values) from time series data of industrial EAF operations; outputs are advisory, used by modellers and industry professionals who make their own operational decisions.

Outputs

About a measurement
Anonymized data
  • Stochastic forecasts of EAF gas composition, flow rate, temperature, and pressure as time series outputs, provided to modellers and industry for process planning. No personal data is produced.

Run by

Natural Resources Canada (NRCan)
  • Natural Resources Canada (NRCan) is the federal department that developed and operates this AI tool to provide probabilistic forecasts of electric arc furnace process properties to modellers and industry.

Government of Canada AI Register — 2526-NRCan-RNCan-011

Built by

Government of Canada
  • The Government of Canada is listed as the developer of this AI system, meaning it was built in-house rather than procured from a commercial vendor.

Government of Canada AI Register — 2526-NRCan-RNCan-011

Kept for

Not stated by the Helpful Places.

Shared with

Not available to me
  • Outputs are provided to GC employees and industry partners; there is no individual citizen-facing access mechanism. AI use is not disclosed to end users.
Available to the accountable organization
  • Forecast outputs are available to Natural Resources Canada modellers and designated industry partners who are primary users of the system.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Be Informed of AI UseThe register records that AI use is not disclosed to users (marked 'N'). This means individuals interacting with outputs of this system may not be informed that AI was used to generate forecasts. No notice mechanism is described in the available documentation.
  • Right to Algorithmic TransparencyNo information is provided in the register about public rights to understand how this forecasting system works. The system is documented in the Government of Canada AI Register, which is publicly accessible, but no user-facing transparency mechanism is described.

Risks and safeguards

  • Financial & business harmInaccurate probabilistic forecasts of EAF process properties could lead industry partners to make sub-optimal operational or investment decisions, with potential financial consequences. The stochastic (probabilistic) design of the tool — providing distributions rather than point estimates — is itself a mitigation, signalling inherent uncertainty to users. No additional mitigations are described in the register entry.