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How to Plan Water for an AI Data Center Before It Delays Your Project

Ricardo Tomaz

21 Oct 2026

How to Plan Water for an AI Data Center Before It Delays Your Project

Teams building artificial intelligence (AI) data centers plan power first. Cooling comes next. Water often comes third.

That order can cost you time. Water affects permitting, community acceptance, cooling performance, and operating cost. Planning it early gives you more options and fewer surprises. This guide walks through why late planning creates risk and how to plan cooling water in three stages.

Why Late Water Planning Creates Risk

A cooling tower needs makeup water and produces a bleed stream you must manage. Both streams affect your site design.

Scale is easy to underestimate. As a rough guide, 1 megawatt (MW) of cooling load equals about 4 gallons per minute (GPM) of cooling water demand. A 100 MW site can need more than 500,000 gallons per day. [VERIFY WITH ENGINEERING]

When water design starts after the engineering, procurement, and construction (EPC) team engages, choices narrow. You may face redesign, permitting delays, or capacity limits that earlier planning could have avoided.

Stage 1: Source Smarter

Start with your source water. Surface water, well water, and municipal supply differ in solids, hardness, and organics. Treatment should follow the water you have.

Hardness deserves attention. Calcium and magnesium leave scale that causes costly breakdowns in cooling towers and other water handling equipment. Front-end treatment reduces that risk.

Depending on your source, front-end treatment may include:

  • Clarification to remove suspended solids
  • Softening to reduce hardness
  • Filtration to polish the water

Water filtered to less than 1 milligram per liter (mg/L) of total suspended solids (TSS) may go directly to a cooling tower.

Stage 2: Treat and Optimize

Your cooling loop needs protection while it runs. Side-stream filtration removes solids from the recirculating water. Cleaner loop water helps protect chillers and towers and can reduce blowdown. [VERIFY WITH ENGINEERING]

Treat this stage as an operating decision. Ask how much maintenance your team can absorb and how much downtime your site can tolerate. The answers shape the right level of filtration.

Stage 3: Recover and Reuse

Cooling tower bleed and process waste do not have to leave the site. You can treat and reuse them.

A typical reuse path for a bleed stream includes:

  1. Clarification to remove suspended solids
  2. Filtration to polish the clarified water
  3. Reverse osmosis (RO) to remove dissolved solids

The treated water can return to the loop. Plan for the concentrate that RO leaves behind. Depending on volume and chemistry, you may need further concentrate management.

Reuse lowers freshwater intake and disposal volume. Lower intake also supports local sustainability goals and can ease conversations with permitting authorities and neighbors.

Modular, factory-built treatment systems can shorten commissioning and installation time. They also scale as your campus grows. [VERIFY WITH ENGINEERING]

Questions to Ask Before the RFP

Water decisions go further when you make them early. Before you issue a request for proposal (RFP), ask:

  • What is the quality and reliability of your source water?
  • How much makeup water will each phase of the campus require?
  • What will you do with cooling tower bleed and process waste?
  • Which streams can you reuse, and at what quality?
  • Who will support the systems after start-up?

A process-driven approach starts with these answers. Our AI data center water treatment page outlines this approach. Our team can help you map your water needs before design is locked.

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Ricardo Tomaz

Ricardo Tomaz

Ricardo Tomaz is VP of Finance & Operations at Ztudium, the 4IR technology company behind Businessabc, Citiesabc, and IntelligentHQ. He owns the group's finance and operations function, spanning financial strategy, budgeting, reporting, and multi-entity operations, and works directly with leadership to align commercial goals with data-driven insight.

Working at the core of a technology group operating across AI and blockchain, Ricardo writes on the practical intersection of finance and emerging technology: how automation and digital transformation are reshaping business operations, what data-driven decision-making looks like in practice, and the questions of governance and ethics that come with AI-led systems.

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