Hospital Cuts Freshwater Use by 36% with Mt. Fuji
Haber deployed Mt. Fuji® at a multi-specialty hospital to transform cooling tower operations from timer-based control to continuous, AI-driven optimization. By combining real-time water chemistry, equipment telemetry, and operational data, Mt. Fuji optimized blowdown, chemical dosing, and cooling performance—reducing freshwater consumption, energy losses, and chemical usage while improving HVAC efficiency.
The Challenge
Cooling towers play a critical role in HVAC performance, directly influencing energy efficiency, water consumption, and system reliability. At the hospital, multiple chillers were supported by cooling towers operating primarily through manual, timer-based control.
This static approach could not adapt to changing water chemistry or cooling loads, resulting in:
- Low Cycles of Concentration (CoC)
- Excessive blowdown and freshwater consumption
- Inconsistent chemical dosing
- Higher energy losses
- Reduced heat transfer efficiency
- Increased chiller load
- Limited visibility into system performance
- Reactive operational decision-making
Haber's Approach
Mt. Fuji continuously ingested live cooling tower and chiller data including conductivity, pH, ORP, temperature, and other operating parameters through standard BMS/IBMS protocols such as Modbus and OPC UA.
Mt. Fuji combined real-time telemetry with operational context including equipment manuals, SOPs, maintenance logs, audit reports, and operator inputs. This created a unified view of the cooling tower, condenser loop, and chiller systems.
Specialized AI agents continuously optimized key operating functions:
- Blowdown Optimization Agent — optimized conductivity-based blowdown to maximize Cycles of Concentration while minimizing water wastage.
- Chemical Dosing Agent — adjusted chemical dosing based on real-time water chemistry and system load.
- Performance Agent — linked cooling-water conditions with chiller performance to maintain efficient heat transfer.
Mt. Fuji continuously learned from changing operating conditions and system behavior, transforming the cooling system from static timer-based operation into dynamic, condition-based control.
Evidence-Backed Results
| Metric | Before | After | Change |
|---|---|---|---|
| Freshwater Consumption Index | 100 | 64 | 36% Reduction |
| Cooling Tower Blowdown Index | 100 | 3 | 97% Reduction |
Business Impact
Mt. Fuji transformed cooling tower management from static, timer-based operation into a dynamic optimization system. The deployment delivered measurable improvements across water, energy, and chemical efficiency.
Conclusion
Mt. Fuji transformed hospital cooling tower operations from static, timer-based control into a continuously optimized system. By combining real-time telemetry, unified asset intelligence, and specialized AI agents, the hospital reduced freshwater, chemical, and energy losses while improving HVAC efficiency and operational reliability.
