Illustrative scenario
Refrigeration condenser fouling detection
Illustrative scenario — representative of common industrial IoT applications
Cooling towersEnergy & electricityFood & Beverage ProcessingControlled Environment Agriculture
Rising head pressure at constant load — a fouled condenser, priced.
A food storage and processing site
How it went.
Every study on this site runs the same four beats, in the same order.
01
Alert
A fouling condenser makes the compressor work harder for the same cooling. It never trips an alarm. It just quietly raises the electricity bill.
02
Cost
Head pressure vs. load — The ratio drifts long before the system trips on high pressure.
03
Fix
Head pressure transducer on the refrigeration circuit · Compressor current monitoring · Ratio trending with a cleaning threshold
04
Restored
A cleaning schedule based on the measured penalty
In full.
Illustrative scenario — representative of common industrial IoT applications.
The problem
A fouling condenser makes the compressor work harder for the same cooling. It never trips an alarm. It just quietly raises the electricity bill.
What would be installed
- Head pressure transducer on the refrigeration circuit
- Compressor current monitoring
- Ratio trending with a cleaning threshold
What changes
Before — A cleaning schedule based on the calendar After — A cleaning schedule based on the measured penalty
The number
Head pressure vs. load — The ratio drifts long before the system trips on high pressure. Illustrative scenario, not a client result. Figure is representative, not measured. [TOM-REVIEW]
In one line
Maintenance on condition, not on the calendar — and a number attached to the delay.
What the data looked like.
Hover or arrow-key across the trace to read any interval. The flagged point is the one that started the conversation.
| Interval | Rack 1 (kPa/kW) |
|---|---|
| D1 | 13 |
| D2 | 12 |
| D3 | 12 |
| D4 | 12 |
| D5 | 13 |
| D6 | 13 |
| D7 | 12 |
| D8 | 13 |
| D9 | 13 |
| D10 | 13 |
| D11 | 13 |
| D12 | 13 |
| D13 | 13 |
| D14 | 13 |
| D15 | 13 |
| D16 | 13 |
| D17 | 13 |
| D18 | 13 |
| D19 | 13 |
| D20 | 13 |
| D21 | 13 |
| D22 | 13 |
| D23 | 13 |
| D24 | 13 |
| D25 | 13 |
| D26 | 13 |
| D27 | 13 |
| D28 | 13 |
| D29 | 13 |
| D30 | 13 |
| D31 | 13 |
| D32 | 13 |
| D33 | 13 |
| D34 | 14 |
| D35 | 13 |
| D36 | 13 |
| D37 | 13 |
| D38 | 14 |
| D39 | 14 |
| D40 | 14 |
| D41 | 14 |
| D42 | 14 |
| D43 | 14 |
| D44 | 14 |
| D45 | 14 |
| D46 | 14 |
| D47 | 14 |
| D48 | 14 |
| D49 | 14 |
| D50 | 14 |
| D51 | 14 |
| D52 | 14 |
| D53 | 14 |
| D54 | 14 |
| D55 | 14 |
| D56 | 14 |
| D57 | 14 |
| D58 | 14 |
| D59 | 14 |
| D60 | 14 |
| D61 | 14 |
| D62 | 14 |
| D63 | 14 |
| D64 | 14 |
| D65 | 14 |
| D66 | 15 |
| D67 | 14 |
| D68 | 14 |
| D69 | 14 |
| D70 | 15 |
| D71 | 15 |
| D72 | 15 |
| D73 | 15 |
| D74 | 15 |
| D75 | 15 |
| D76 | 15 |
| D77 | 15 |
| D78 | 15 |
| D79 | 15 |
| D80 | 15 |
| D81 | 15 |
| D82 | 15 |
| D83 | 15 |
| D84 | 15 |
| D85 | 15 |
| D86 | 15 |
| D87 | 15 |
| D88 | 15 |
| D89 | 15 |
| D90 | 15 |
| Before | A cleaning schedule based on the calendar |
|---|---|
| After | A cleaning schedule based on the measured penalty |
Illustrative dataset — a representative operational profile, seeded and deterministic, not measured at any site.
What changed.
Before
A cleaning schedule based on the calendar
After
A cleaning schedule based on the measured penalty
Head pressure
vs. load
The ratio drifts long before the system trips on high pressure.
Source: Illustrative scenario, not a client result. Figure is representative, not measured. [TOM-REVIEW]
Where to go next.
The sector, the sibling studies, and the calculator that runs this same arithmetic on your own numbers.
Cooling towersWaterManufacturing — automotive, plastics & rubberBuilding Materials & Heavy Industry
Cooling tower blowdown optimization
A plastics manufacturing plant
Blowdown running on a timer instead of on conductivity, discharging good water.
1 setpointchanged
Illustrative scenario — representative of common industrial IoT applications
Energy & electricityBulk Handling & Rail Logistics
Night setback that never happened
A distribution warehouse
The overnight setback had silently stopped working — and it was worth about a third of the load.
~35%of overnight electrical load
Illustrative scenario — representative of common industrial IoT applications
Energy & electricityManufacturing — automotive, plastics & rubber
Three plants, one unit of production, one outlier
A three-plant manufacturer
Same product, same equipment — one plant used about 40% more energy per unit.
~40%more energy per unit
Illustrative scenario — representative of common industrial IoT applications
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