AI-Integrated Manufacturing Systems for Thermal Energy Storage Tank Production in Hyperscale Data Center Infrastructure
Keywords:
AI manufacturing toolkit, Chilled water thermal storage, Data center cooling, Hyperscale infrastructure, Industry 4.0, Modular fabrication, Peak demand managementAbstract
The rapid proliferation of artificial intelligence (AI)-driven hyperscale data centers has intensified thermal management demands to levels that conventional chilled water systems cannot efficiently address. Thermal energy storage (TES) systems — large stratified chilled water tanks enabling peak-load shifting and demand reduction — have emerged as critical infrastructure for these facilities. This paper examines how Smith Industries deployed an integrated AI toolkit to transform its manufacturing operations for large-scale TES tank production, covering digital travelers, automated bill-of-materials generation, real-time production dashboards, and predictive bottleneck identification. Operational outcomes include a 20–40% chiller runtime reduction in deployed facilities, a 50–70% reduction in on-site fabrication time through modular delivery, and production quality rejection rates below 2%. The framework demonstrates that AI-driven manufacturing integration is not merely a productivity enhancement but a precondition for the scalable, high-quality production required to meet surging data center thermal infrastructure demand.
DOI: https://doi.org/10.17762/ijisae.v14i1s.8322
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