How can energy data analytics improve industrial refrigeration efficiency?

At first stage before identifying and performing data analytics is that of metering and data capturing. Itis very important to install metering where it matters the most. Energy data analytics plays a pivotal role by swiftly uncovering hidden inefficiencies. It helps to pinpoint issues like overworked compressors or misaligned temperature settings. Analytics not only identify wastage but also empowers informed decision making and taking proactive measures such as operation optimization, tuning and implementing targeted maintenance schedules. Then at E

Como suelo decir, casi nada se puede mejorar si no se registran y se procesan los datos. La intuición ayuda, pero no mucho. En el caso de la energía, donde no se puede ver fugas de electrones u otros indicadores, la toma de datos es crucial tanto como su análisis.

Energy wastes can be identified through data analytics. More often than not, energy wastes are "invisible" to the general crowd as it is not apparent. However, when data analytics is used, more parameters could be analysed at once and more insights could be gathered regarding refrigeration systems. This would allow for more insights to be generated regarding energy wastes. For example, a room's door may be left open by accident resulting in high refrigeration load across the whole day. Data analytics may look at the refrigeration usage across a facility and drill down to the particular user to enable better identification and problem solving.

Leveraging energy data analytics unveils a treasure trove of insights. Monitoring equipment run times, compressor cycles, and aligning with manufacturer specifications are crucial. Understanding how ambient temperature and load variations impact energy consumption is key. Any deviations from expectations trigger proactive plans for improvement or maintenance.

Data analytics could be used to schedule the running cycles of assets. Based on refrigeration loads throughout the day as wekk as the available assets, data analytics could be used to to generate the operating strategy with the lowest running cost. This improves productivity as energy managers do not need to manually calculate these parameters and the automated analysis could do most of the heavy lifting for analysis and optimisation.

Maintenance and reliability could be enhanced based on data analytics. Data on the mean time between failures of refrigerant leaks as well as poorer energy performance due to lack of refrigerant may be used to justify a chiller overhaul or to stretch the duration between overhaul to a praticable level. Evaporator and condenser tube conditions could also be monitored using energy parameters and a condition-based maintenance approach may be used to optimise maintenance cost.

El control ayuda a la planificación y la planificación al control. y finalmente todo decanta en la confiabilidad de los datos que se registran o se analizan. A partir de ello, se pueden tomar decisiones para el bien de los negocios o del ahorro energético.