AI-Assisted CO2 Mineralization Process Design and Optimization

Research & Development · Ecology

What it collects

About a measurement

Anonymized data

Experimental process measurements including operating parameters such as temperature, pressure, reaction time, and CO2 concentration for CO2 mineralization reactions with industrial waste feedstocks. Data combines publicly available datasets with laboratory experimental results.

Government of Canada AI and Data Use Registry — 2526-NRCan-RNCan-014

Operational data

Anonymized data

Publicly available scientific literature and datasets on CO2 mineralization combined with experimental data generated at Natural Resources Canada laboratories. No personal data is processed.

Government of Canada AI and Data Use Registry — 2526-NRCan-RNCan-014

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 & Development

Ecology

This tool uses machine learning to predict and optimize how industrial waste materials can be used to capture and permanently store carbon dioxide (CO2) through a chemical process called mineralization. It is used by Natural Resources Canada scientists and engineers to design better experiments and operating conditions. The system is an internal research tool and is not used to make decisions affecting members of the public.

What it collects and what happens to it

Data taken in

About a measurement

Anonymized data

Experimental process measurements including operating parameters such as temperature, pressure, reaction time, and CO2 concentration for CO2 mineralization reactions with industrial waste feedstocks. Data combines publicly available datasets with laboratory experimental results.

Government of Canada AI and Data Use Registry — 2526-NRCan-RNCan-014

Operational data

Anonymized data

Publicly available scientific literature and datasets on CO2 mineralization combined with experimental data generated at Natural Resources Canada laboratories. No personal data is processed.

Government of Canada AI and Data Use Registry — 2526-NRCan-RNCan-014

Processing

Classification & Prediction

Artificial neural networks (ANN) trained on experimental CO2 mineralization data to predict process outcomes such as carbonation efficiency and mineral yield under varying operating conditions. Coupled with factorial design and ANOVA to identify significant process parameters.

Government of Canada AI and Data Use Registry — 2526-NRCan-RNCan-014

Optimization

Factorial design and analysis of variance (ANOVA) coupled with the ANN model to optimize combinations of influencing parameters for CO2 mineralization. Outlier screening using Grubb's test ensures data quality before optimization runs.

Government of Canada AI and Data Use Registry — 2526-NRCan-RNCan-014

What it does

Deciding (Analytical AI)

Human decides

Predicts and scores the interaction of influencing parameters (e.g., temperature, pressure, feedstock composition) for CO2 mineralization, generating recommendations that researchers review to select optimal process designs.

Government of Canada AI and Data Use Registry — 2526-NRCan-RNCan-014

Outputs

A recommendation or prediction

Anonymized data

Predicted optimal parameter combinations and process performance forecasts for CO2 mineralization (e.g., recommended temperature, pressure, and residence time for maximum carbonation efficiency). Outputs are advisory and reviewed by NRCan researchers before any experimental or process decisions are made.

Government of Canada AI and Data Use Registry — 2526-NRCan-RNCan-014

Run by

Natural Resources Canada (NRCan)

Natural Resources Canada is the federal department that develops and operates this AI tool for CO2 mineralization research. The system was developed by the Government of Canada and is used exclusively by GC employees.

Government of Canada AI and Data Use Registry — 2526-NRCan-RNCan-014

Built by

Not stated by the Helpful Places.

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization

Outputs and results are available to Natural Resources Canada employees and researchers using the tool. The system is described as an internal tool used exclusively by GC employees.

Government of Canada AI and Data Use Registry — 2526-NRCan-RNCan-014

Not available to me

This is an internal research tool for GC employees. Members of the public do not interact with the system and do not have access to its outputs. The register notes that AI use has not been disclosed to affected users.

Government of Canada AI and Data Use Registry — 2526-NRCan-RNCan-014

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

The Helpful Places has not listed specific rights for this system.

Risks and safeguards

Environmental harmThe system's purpose is to support carbon capture research; however, there is a low risk that optimizing industrial CO2 mineralization could inadvertently promote continued reliance on industrial processes that generate waste streams.Safeguard: the tool is positioned within a broader NRCan carbon capture and storage research mandate focused on reducing industrial emissions, and outputs require expert human review before any process scale-up decisions.