Overview
Plant-Wide Digital Twin Metrics
Total Cooling
-- TR
Total Power
-- kW
Plant COP
--
Chillers Running
--
C1 Evap ΔT
-- K
C2 Evap ΔT
-- K
12H Plant Efficiency Trend
Historical Plant COP over the last 12 hours.
6.2
Financial & Sustainability Impact
Real-TimeHourly Operating Cost
₹ --
/ hrBased on ₹7.75/kWh
Carbon Footprint
-- kg
CO₂ / hrGrid factor: 0.42 kg CO₂/kWh
Proj. Annual Savings
₹ --
/ yrvs Baseline 0.85 kW/TR (8760 hrs)
chiller1 RTHD
chiller2 RTHD
Chiller Energy Intelligence
Live thermodynamic calculations, active alerts & efficiency benchmarks.
Efficiency Scorecard
| Metric | Actual | Status |
|---|---|---|
| kW/TR | -- | - |
| COP | -- | - |
| Evap ΔT | -- | - |
| Motor Load | -- | - |
Energy Intelligence
Cooling Load vs PowerAsset Performance
TR DistributionEfficiency Index
kW/TR Trend
Benchmark: 0.65 kW/TRAlerts & Risks
| Chiller | Anomaly |
|---|---|
| No alerts reported | |
System Status: Checking...
Monitoring live thermal parameters and compressor lifts to generate recommendations.
Phase Voltage Monitoring
chiller1 RTHD UT-004/WCR-002
Capacity vs Limit
Chiller Digital Twin
Live operational flows and sensor values
Diagnostic Insights (RCA)
Warning: Condenser Heat Transfer Inefficient. Inspect for tube fouling or check cooling tower water treatment.
Electrical Health
Current Lift
Pressure Lift
System Properties & Raw Data
Diagnostic
Normal
Diagnostic
Normal
Diagnostic
Normal
chiller2 RTHD UT-004/WCR-003
Capacity vs Limit
Chiller Digital Twin
Live operational flows and sensor values
Diagnostic Insights (RCA)
Warning: Condenser Heat Transfer Inefficient. Inspect for tube fouling or check cooling tower water treatment.
Electrical Health
Current Lift
Pressure Lift
System Properties & Raw Data
Diagnostic
Normal
Diagnostic
Normal
Diagnostic
Normal
Performance Analytics
Historical diagnostics and performance trends
Event Audit Log
Operational Lifecycle
Baseline Power Curve Regression
Scikit-LearnkW/TR vs Load (Unsupervised Isolation)
Predictive Maintenance Forecast
Linear TrendCondenser Fouling Degradation Rate (90 Days)
Projected Critical Threshold (3.5°C)
Analyzing...
-- days remaining
C1 Approach
--.- K
C2 Approach
--.- K
C1 Superheat
--.- K
C2 Superheat
--.- K
Approach Analytics
Cond Sat Temp vs Leaving Water Temp
Discharge RCA
Superheat (Discharge T – Cond Sat T)
VFD Efficiency
Power Draw vs Running Capacity
Thermal Circuit
Evaporator & Condenser Circuit Temps
REPORTS
Exportable logs and interval-based grid data for chillers
Interval Data Report
Estimated ₹8 / kWh| Time | Power (kW) | Energy (kWh) | Cost (₹) | Capacity (%) | ||||
|---|---|---|---|---|---|---|---|---|
| Chiller 1 | Chiller 2 | Chiller 1 | Chiller 2 | Chiller 1 | Chiller 2 | Chiller 1 | Chiller 2 | |
System Event & Stress Logs
| Timestamp | Source | Category | Event | Severity |
|---|
Return on Investment (ROI) Analysis
Comprehensive financial analysis of the chiller optimization program at this facility. Evaluating savings from predictive maintenance, optimal loading, and energy efficiency.
Annual Energy Savings
₹ Loading...L
18.2% reduction
Maintenance Cost Avoided
₹ Loading...L
Due to Predictive Alerts
Total Carbon Offset
Loading... tCO2
Equivalent to 2,100 trees
Estimated Payback Period
Loading... Mo
Based on current implementation
Cumulative Cost Savings (12 Months Projection)
Savings Breakdown by Category
Key AI Insights & Recommendations
Optimal Load Distribution
Running Chiller 1 at 85% capacity rather than splitting the load 50/50 with Chiller 2 avoids co-balancing inefficiencies. This strategy accounts for 60% of the energy savings realized so far.
Preventive Maintenance (Condenser Fouling)
Predictive analytics identified early condenser fouling on Chiller 2. Scheduling a descaling operation prior to the peak summer load avoided an estimated ₹2.5L in excess energy draw and potential downtime.
Set-Point Optimization
Dynamically adjusting the chilled water setpoint based on ambient weather and production schedules yielded an additional 8% efficiency gain. Continued use of the AI schedule is highly recommended.
TGNPDCL Tariff Utilization
Calculations are based on the latest TGNPDCL industrial tariff (₹7.75/kWh). By reducing peak demand spikes, the plant is also saving on potential maximum demand penalties.
AI-Supervised Autonomous Mode
Based on a 30-day historical baseline, transitioning from conservative manual staging to AI-driven predictive scheduling is highly recommended. The AI optimizes load distribution across both chillers in real-time, matching efficiency curves to significantly save energy, extend asset life, and reduce maintenance costs.
Estimated Monthly Impact
Manual Short-Cycles
Loading... events/mo
Excessive wear on compressor starters.
AI Projected Cycles
Loading... events/mo
71% reduction in wear & tear
Peak Demand Spikes
Loading... spikes >250kW
Simultaneous staging of C1 & C2.
Set-Point Deviation
Loading... °C avg variance
Due to reactive manual adjustments.
Energy Profile: Reactive vs. Predictive
Comparing a typical 24-hour manual operational day vs. AI optimized baseline.
Chiller Staging & Co-Balancing
Manual 50/50 split vs. AI Optimal Load Distribution across both chillers for maximum efficiency.
Operational Strategy Comparison
30-Day Historical Review| Aspect | Current Manual Strategy | AI Recommended Strategy | Business Impact |
|---|---|---|---|
| Chiller Staging (Lead/Lag) | Operators typically run both Chiller 1 and Chiller 2 at 40-50% capacity during moderate loads to "play it safe." | Dynamically distribute load across both chillers based on real-time efficiency curves, prioritizing the most efficient operating points for each compressor. | 12% Energy Saving. Extends Remaining Useful Life (RUL) and reduces maintenance cost by balancing wear & tear intelligently. |
| Set-Point Management | Static set-point of 7.0°C regardless of ambient weather or actual process demand. Changed only if process complains. | Dynamic set-point reset. Automatically raising the chilled water set-point by 1-2°C during cooler ambient hours or lower production shifts. | ~6% Efficiency Gain. Every 1°C increase in leaving chilled water temperature improves compressor efficiency by ~3%. |
| Peak Demand Control | Both chillers often start simultaneously after a brief power outage or morning startup, causing a massive kW spike. | Staggered, soft-starting routines predictive of process load, utilizing the thermal inertia of the chilled water loop to delay staging. | Avoids Maximum Demand Penalties from TGNPDCL tariffs due to sudden draw spikes. |
| Maintenance Trigger | Scheduled calendar-based maintenance (every 6 months) or reactive maintenance when High Pressure (HP) trip occurs. | Condition-based. The AI schedules tube descaling precisely when the "Condenser Approach" exceeds 2.5K, indicating micro-fouling. | Zero Unplanned Downtime. Prevents running highly inefficient fouled chillers for months. |