Decision-Support Tool for AI Impact on Minnesota’s Water

2026 Fast-Track Grant

Image credit: UMPR. 

This project will develop a tool to help communities, regulators, and data center developers determine whether enough water is available to support large AI data centers in Minnesota. The tool will estimate water use, assess potential effects on water systems, and support compliance with Minnesota House File 16.

Minnesota House File 16, enacted in June 2025, established energy, environmental and economic requirements for large data centers.

The project responds to the rapid expansion of data centers built to support artificial intelligence. AI is a broad term for computer systems that perform tasks such as analyzing data, recognizing patterns and making predictions. Generative AI is a type of AI that creates new content, including text, images, audio and computer code. As generative AI use grows, technology companies are building large data centers with the computing capacity needed to develop and operate these systems. These facilities can use substantial amounts of water to cool their equipment.

The project will:

  • Estimate a data center’s real-time water use based on its equipment and operations.
  • Predict heat and sulfate levels that could affect water systems and infrastructure.
  • Develop a dashboard that helps data center operators and regulators review water-use information and meet requirements under Minnesota House File 16.

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Funding

This project is supported by a Minnesota Sea Grant 2026 Fast-Track Grant.

Project team

Lead Principal Investigator:
Wenkai Guan, Ph.D. 
[email protected]
Assistant Professor, Computer Science
University of Minnesota Morris

Co-Principal Investigators:
Yang (Katie) Zhao, Ph.D. 
[email protected]
Assistant Professor, Department of Electrical and Computer Engineering
University of Minnesota Twin Cities

Zhichao Cao, Ph.D. 
[email protected]
Assistant Professor, School of Computing and Augmented Intelligence
Arizona State University

Tianlong Chen, Ph.D.
[email protected]
Assistant Professor of Computer Science
University of North Carolina at Chapel Hill

Why Sea Grant?

This project directly advances Minnesota Sea Grant’s goals for both Resilient Communities and Economies and Healthy Coastal Ecosystems by giving local leaders and community members predictive, science-based tools needed to navigate an expanding digital economy without sacrificing local waters.

Lead scientist(s)

Wenkai Guan, Ph.D. 
[email protected]
Assistant Professor, Computer Science
University of Minnesota Morris