TECH TALK
effi ciency. The improved standards reduce the uncertainty of presented data and sales of equipment with sub-standard performance. Effi cient equipment is the fi rst step to effi cient operation, but it is not enough as recommissioning systems, based on performance monitoring, often save 25% of energy and rarely less than 10%. Performance monitoring establishes effi ciencies of the total system, subsystems, and components. 1.2.1 System Effi ciency Index, SEI and sub effi ciencies. The introduction of the System Effi ciency Index addresses the fact that conventional performance indicators depend on operating conditions, making them impractical for fi eld measurements as systems rarely operate at rating conditions.
The System Effi ciency Index, SEI, establishes reference
temperatures to calculate the ideal Carnot COP. The reference temperature for a water-cooled chiller or water- to-water heat pump is the average cooling and chilled water temperatures, corresponding to a system with an indefi nite heat exchanger surface and fl ow. The SEI hardly changes with operating conditions and represents total effi ciency for the unit compared to a loss-free process under the same conditions. Figure 2 shows the System Effi ciency Index, SEI, a
powerful performance indicator that solves the problem of conventional KPIs, highly dependent on operating conditions. 1.2.2 Sub-effi ciencies Sub-effi ciencies for the components explain every change in the total SEI so the root cause can be pinpointed. The key sub-effi ciencies are: ■ Compressor isentropic effi ciency – Isentropic effi ciency is well-established and a part of any thermodynamic training, but it has rarely been used for benchmarking or troubleshooting in the fi eld.
■ Condenser Effi ciency – Condenser effi ciency identifi es how the condenser operates versus an ideal-indefi nite condenser and indicates, e.g. fouling, issues with the fl ow on the refrigerant/air/liquid side, refrigerant charge or none condensable.
■ Evaporator Effi ciency – Evaporator effi ciency identifi es how the evaporator operates versus an ideal-indefi nite evaporator and indicates, e.g., fouling, issues with the fl ow on the air/liquid/refrigerant side, expansion device, or low refrigerant charge.
■ Cycle Effi ciency – The losses in the refrigerant cycle are dependent on, e.g., subcooling and superheating but also indicate when the cycle is improved with, for example, an economiser. Figure 3 shows an example of a dashboard visualising low SEI caused by low sub-effi ciency in the evaporator caused by high dT/low fl ow. Figure 4 is a dashboard showing the same system
operating with nearly the same supply temperatures, but SEI has improved from 25.6% (COP 3.76) to 42.4% (COP 5.02) by correcting evaporator fl ows. The system operates with a similar cooling capacity but consumes 66.4 kW instead of 101 kW.
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Figure 4, Dashboard showing the same system operating with nearly same supply temperatures, but System effi ciency has improved from 25.6% (COP 3.76) to 42.4% (COP 5.02) after correction of evaporator fl ows. The system is operating with the same cooling capacity but consume 66.4 kW instead of 101 kW.
Predictive maintenance with AFDD The requirement to reduce energy consumption, refrigerant
leaks, failures and downtime push for predictive maintenance (PdM). Most failures and downtime are caused by a lengthy period of poor operation detectable by data-driven PdM long before it causes high energy bills and failures. 2.1 Explainable AI means Digital Twins that we understand. As the operation continuously changes and systems become more complex, manual confi guration of thresholds for early warning is challenging and time-consuming, and it requires competence as design and operating conditions vary. Conventional AI based on large amounts of data could identify deviation if an enormous amount of representative data, including identifi ed faults, is available. However, as there are virtually no identical systems, accumulating
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Figure 3, Example of dashboard visualising low SEI caused by low sub-effi ciency for evaporator caused by high dT/low fl ow in evaporator.
Figure 2
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