Case Study: Energy Optimisation in a Regulated Manufacturing Plant
How a regulated manufacturing site used IIoT monitoring to understand compressor and chiller performance, cut avoidable energy use, and build a carbon baseline.
A Compressed air systems and chillers represented a significant share of the plant’s total energy consumption, but decisions were based mainly on monthly bills and manual observations. The site faced several issues:
Challenge
A large, regulated manufacturing plant operating in a major industrial hub relies heavily on compressed air and chilled water systems as critical utilities for production. To support its cost-optimisation and decarbonisation goals, the site deployed an IIoT 4.0 wireless monitoring solution on key utility equipment to gain real-time visibility into energy use, loading and anomalies.
Customer Overview
No continuous visibility into the loading and energy consumption of individual compressors and the chiller.
Limited ability to correlate daily and weekly energy use with production to identify avoidable losses.
Risk of motor overloading, peak load penalties and unplanned failures due to undetected anomalies in current, loading and phase balance.
Lack of machine-level carbon footprint data, making it difficult to establish a baseline and track emissions trends over time.
A wireless, circuit-level IIoT system was installed on one chiller and two air compressors at the plant. The installation included one power sensor on the chiller and three current sensors on each of the two compressors, enabling continuous measurement of power draw, current, loading and energy consumption. Using these measurements, the team could visualise site energy consumption for the monitored machines and correlate daily and weekly trends with production data to identify excessive energy loss.
Solution
Benchmark operating cost per hour for each compressor and compare cost, loading and usage patterns.
Analyse weekly and daily energy consumption for both compressors and the chiller, including week‑over‑week variance and heat maps.
Detect anomalies such as overloading, excess consumption, current spikes, phase imbalance and frequent chiller motor cycling.
Estimate weekly machine-level carbon emissions to support carbon baselining and benchmarking across sites.
Large energy variance: Weekly energy consumption showed significant swings, with some weeks recording reductions greater than 70% and others large increases, highlighting opportunities to optimise loading, scheduling and setpoints.
Key Findings
The uncovered several important insights:
Under‑ and over‑loading: One compressor was consistently under‑loaded during multiple weeks, whereas the other frequently operated at 78–96% loading and at times above 100% of the motor’s rated capacity, indicating overloading and reliability risk.
Impact of pressure setpoint changes: A reduction in compressed air pressure setpoint (from 6.8–7.3 bar to 6.5–7.0 bar) was correlated with changes in power draw for both compressors, demonstrating the impact of setpoint optimisation on energy demand.
Carbon footprint visibility: Weekly carbon emissions from the monitored machines were estimated in the range of roughly 25–28 tCO2e, giving the plant a quantitative baseline for future decarbonisation efforts.
Peak load and chiller behaviour: Current spikes on the chiller (for example, up to 542 A) were linked to increases in site peak load, and events such as frequent chiller motor cycling and phase imbalance were identified as early warning signs requiring corrective action.
Compressor cost and loading imbalance: One compressor had an operating cost per hour around 28% higher than the other, while also running roughly 70% more hours in the month.
Improved peak load management: Understanding the contribution of individual machines to site peak demand enables targeted actions to reduce peak load charges and smooth overall demand.
Results and benefits
By combining continuous monitoring with detailed energy analytics, the project delivered clear, actionable opportunities for optimisation and risk reduction:
Energy cost optimisation: By shifting load away from the higher‑cost, heavily loaded compressor towards the lower‑cost unit and optimising operating hours, the plant identified a clear path to reduce monthly compressor energy costs.
Lower overload and failure risk: Continuous monitoring pinpointed periods when compressors and the chiller operated close to or above rated capacity, providing early warning so maintenance teams could act before issues evolved into failures.
Data-driven decarbonisation: Machine‑level carbon estimates and week‑over‑week variance give a solid baseline to track the impact of future energy and emissions projects.