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EnVisA - Energy Visualisation with Artificial Intelligence

Reference number
Coordinator SWERIM AB
Funding from Vinnova SEK 5 026 000
Project duration November 2020 - April 2024
Status Completed
Venture AI - Leading and innovation
Call AI in the service of climate

Important results from the project

EnVisA focuses on developing a AI based prediction-visualization tool for process gas supply and finally testing it in the industrial trial, to guide operators in CHP for more efficient and sustainable usage of process gas as an energy source. We have achieved the project objectives, including predicting surplus process gases with AI, real-time process gas (energy) visualization, integrating the prediction-visualization tool, testing and improving it in the industrial trial.

Expected long term effects

We have achieved the follow results: 1. Develop an efficient data collection process that realize real-time data capture and storage. 2. Develop an visualization process that enable real-time gas (energy) display. 3. Develop a AI-based prediction model that can conduct real-time prediction for necessary process gas factors for achieving the goal of the EnVisA project. 4. Integrate the prediction-visualization tool. 5. Test and improve the prediction-visualization tool successfully in the industrial trial.

Approach and implementation

The EnVisA project spans from Q4 2020 to Q2 2024, the following WPs have been finished. WP1 manages the project, holds meetings, and disseminates results. WP2 defines the problem scope, including visualization requirements, needs of operators and data access. WP3 handles data collection and system setup, ensuring fast connectivity. WP4 develops a real-time process gas visualization system. WP5 creates a prediction module for surplus process gases. WP6 conducts industrial trials to test the developed model. WP7 performs a benefit analysis.

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 3 June 2024

Reference number 2020-03397