Co-pilot in Process Nodes
Last updated
Last updated
Welcome to the "Co-Pilot in Process Nodes" section. Here, you will learn how to leverage Emvide’s AI Co-Pilot within the process node wizard to enhance your lifecycle assessment (LCA) workflow. This guide will demonstrate how to use both the Co-Pilot Search and Co-Pilot Compose functionalities to automate and refine your process data input, ensuring a more accurate environmental impact analysis.
In this video, you will learn how to:
Launch the process node wizard.
Use Co-Pilot Compose to automate the generation of process data.
Adjust and validate the AI-generated assumptions for your process nodes.
Launching the Process Node Wizard:
Start by opening the Emvide platform and selecting a product. For demonstration purposes, we will use the "Emvide Co-Pilot Feature Functionality" product, which contains one resource and one process node on the canvas.
To open the process node wizard, hold the Control key and click on the process node. This action will launch the wizard for the selected process.
Using Co-Pilot Compose:
Purpose: Co-Pilot Compose helps automate the creation of process data by making intelligent assumptions based on the descriptions you provide.
Example: For a process like "wheel welding," enter a name and description such as "Wheel welding to attach different metal parts of the bicycle wheel."
Executing the Compose: Click on the Co-Pilot Compose button. The AI will process the description and generate a list of elements required for the emissions factor.
Reviewing Results: The AI might return a process like "welding of arc steel," providing relevant units of measurement and CO2 factors.
Adjusting for Specific Inputs:
Electricity Usage Example: If you want to refine the AI’s assumptions based on specific data you have (e.g., electricity usage), add details such as "This process uses five kilowatt hours of electricity" to the description.
Re-Executing the Compose: Run Co-Pilot Compose again. The AI will adjust its analysis to incorporate the specified electricity usage, providing a more detailed and accurate emissions estimate.
Reviewing and Saving: Review the updated data, which now includes more detailed assumptions about the welding process and energy consumption. Save the data to incorporate it into your LCA.
Understanding and Validating AI Assumptions:
Assumptions and Methodology: It’s crucial to understand the assumptions the AI has made. For example, it might include materials like aluminium alloy rims, rubber tyres, and steel spokes for a wheel welding process.
Proving Methodology: Clearly document the methodology and assumptions used in your LCA. This transparency is vital for validating your analysis and ensuring the credibility of your results.
Refining the Process Data:
If needed, refine the input description further to guide the AI more precisely. The more detailed your description, the more accurate the AI’s output will be.
Save your refined process data to ensure it is integrated into your overall LCA workflow.
Using the Co-Pilot within process nodes allows you to:
Automate the creation of detailed process data.
Refine and validate AI-generated assumptions.
Save time and enhance the accuracy of your LCA results.
By leveraging the Co-Pilot Compose feature, you can improve your process node analyses, ensuring that your lifecycle assessments are thorough and reliable. Remember, the detail in your input descriptions significantly influences the accuracy of the AI’s output.
Explore the other functionalities of Co-Pilot within the Emvide platform by following the links below:
This page provides a comprehensive guide to using the Co-Pilot in process nodes, helping you understand and utilise the AI capabilities to improve your lifecycle assessments.