Abriliam Consulting — Industrial Energy Management
The cooling tower is the plant's primary heat rejection pathway. Its performance directly impacts chiller efficiency — every degree of excess approach temperature raises condenser pressure and increases compressor work. This notebook evaluates tower performance through approach temperature analysis, free cooling potential, and fan power optimization.
The condenser water delta-T and tons rejected follow expected load patterns. The tower must reject not only the building's cooling load but also the chiller's compressor heat — typically 1.2 to 1.3 times the evaporator load. Stable delta-T indicates the CW loop is sized appropriately for the load range.
Free-cooling POSSIBLE: 0 rows (0.0%) using approach = 3.0°C
Free cooling (also called water-side economizer or strainer cycle) allows the cooling tower to directly cool the chilled water loop when outdoor conditions are cold enough — bypassing the chiller entirely. The analysis compares outdoor air temperature against the free-cooling threshold (CHW supply setpoint minus tower approach).
The time-series view highlights periods where free cooling is feasible (green shading). For a summer dataset, these opportunities are limited to nighttime and early morning hours during cooler periods. In a year-round analysis, shoulder seasons would show substantially more free cooling potential.
Plotting evaporator tons against chiller kW/ton over time reveals the inverse relationship between load and efficiency. During high-load periods, the chiller operates near its design point with good kW/ton. During low-load periods, fixed losses dominate and efficiency degrades. The efficiency spikes correlate with the low-delta-T syndrome identified in Notebook 05.
C:\Users\mikea\AppData\Local\Temp\ipykernel_20836\2091952480.py:26: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.
d_sma["dp_kpa"] = d[DP_COL].rolling(f"{WINDOW_HOURS}H", min_periods=3).mean()
C:\Users\mikea\AppData\Local\Temp\ipykernel_20836\2091952480.py:27: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.
d_sma["eff"] = d[EFF_COL].rolling(f"{WINDOW_HOURS}H", min_periods=3).mean()
C:\Users\mikea\AppData\Local\Temp\ipykernel_20836\2091952480.py:28: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.
d_sma["lf"] = d["load_factor"].rolling(f"{WINDOW_HOURS}H", min_periods=3).mean()
The 10-hour moving averages smooth out hourly noise to reveal underlying trends. The differential pressure (blue) shows a clear step-change — corresponding to the hydraulic regime shift detected in Notebook 04. Chiller efficiency (red) trends upward (worse) after the regime change, while load factor (green) follows seasonal weather patterns.
The shaded area between CW supply temperature and wet-bulb represents the tower's approach — the gap the tower cannot close. A widening approach (growing shaded area) directly correlates with degraded plant efficiency (green line). This confirms the causal chain: tower degradation raises condenser water temperature, which increases chiller lift and power consumption.
Tower fan power shows a gradual upward trend independent of CW flow, suggesting the fans are working harder to maintain (or failing to maintain) approach temperatures as the tower degrades. The fan power increase without a corresponding CW flow increase is characteristic of fouled fill or degraded fan performance.
The 24-hour SMA of tower fan power reveals the night-time control bias — fan power has a persistent overnight baseline that is higher than daytime minimums, even though cooling loads are much lower at night. This suggests an aggressive fan control schedule or a fixed-speed fan that doesn't modulate down sufficiently during low-load conditions.
Coloring the approach time-series by wet-bulb bin normalizes for weather effects. The upward drift in approach is visible across all wet-bulb conditions — this is not simply a weather-driven phenomenon. The tower is genuinely degrading over the 8-week period, likely due to biological fouling, scale buildup, or mechanical degradation of the tower fill.
The scatter plots of fan power vs approach and chiller kW/ton vs CW supply temperature quantify the cascading impact: higher approach drives higher CW supply temperature, which drives higher chiller power consumption. Each degree of excess approach costs measurable energy.