For an industrial energy-efficiency project, you will naturally need a software platform with advanced features. But, over and above the software’s capabilities, the most important aspect to consider is the use cases. At Blu.e, we present five use cases that apply across all industries, regardless of sector and size.
1- Applying standard operational best practices
It has not yet become standard practice to manage production lines and utilities from an energy efficiency angle. There is a certain resistance on the part of managers – who prefer to think in terms of quality – coupled with a lack of information (and training). Displaying the recommended settings where they can be readily seen by operators will help them manage energy performance and simulate future consumption levels and/or settings with the help of predictive modelling.
| The solution: live performance management
In the control room, display the operating rules applicable to the current operating conditions so that operators can recreate the conditions that yielded good results in the past. This is a powerful energy management tool for operational teams!
2- Monitor application of the operating rules
This is a key step in the PDCA continuous-improvement drive required by energy management standards (ISO 50001). First monitor application of the prescribed rules, then measure any discrepancies and look for the reasons, which may be quite valid (such as production constraints, raw materials quality, etc.). This is an excellent way to forge ties with the teams on the ground and build trust.
| The solution: operating rule reports
Adopt modern reporting practices to coordinate your energy management! For example, one of our customers operates a gas turbine electric generator with cooling pumps. Every Monday, the operating team checks the rule application rate and, if appropriate, launches a graphical analysis. When the rate declines, a discussion is held with the operators to understand why and adjust the settings.
The operating rules report will also help you draw up standards for other similar procedures, deploy best practices across the organisation and see where further training is required.
In energy management, as in other areas, theory and practice sometimes (or even often?) diverge. Why is this compressor consuming more than expected? In what circumstances? Picking up slow drifts away from the norm is even more difficult, but it is vital to eradicate them before they pollute the data by being treated as normal. The answer is to analyse the energy data in order to understand and solve any malfunctions and off-target energy readings.
| The solutions: graphical analysis and the comparator
Data visualisation yields a visual, intuitive analysis of malfunctions, but can only be used once the problem has been at least partially identified. Graphical navigation makes it possible to play with the data by changing the granularity (periods, zooming in from the factory to the equipment) or by placing one layer of data on top of another (comparing it with another site, another item of equipment, another period, etc.).
For more complex cases, artificial intelligence tools can be used to automatically break them down into influence factors and map the scope of the problem.
4- Benchmark and monitor the performance of your production lines and facilities
Whatever the time period (year, month, week, day, hour or real time), it is always useful to track the technical and economic KPIs and the best practice application rates at each site, and especially to compare them with the target values (for example by tracking the budgets). But how are these target values produced? By taking inspiration from comparable sites.
| The solutions: alarms, dashboards, and pivot tables
Identify unexplained variations in consumption, analyse them and take steps to correct them. Monitor the energy efficiency of the utilities and of specific consumptions on the production lines. Set a target for each indicator! Set targets to be achieved in each production scenario and configure context-sensitive alarms in the event of off-target performance.
5- Tweak your operating rules
Significant energy savings can be found in machine and equipment settings (temperature, pressure, flow rate, etc.) and workshop organisation (time taken to start up and shut down equipment, staggered start-up, adjustment to the volume of activity, etc.). Identify and apply best practices after adapting them to the production conditions, and disseminate them to a number of sites.
| The solution: Big Data analysis
Begin by storing all energy consumption data, information about equipment settings and exogenous data (such as weather conditions, energy prices, economic factors, etc.) in a data lake, structured at variable time increments, for a sufficiently long period of time to be sure of their relevance (for seasonal data in particular). Then run Big Data analyses. Look for the most efficient machine settings, produce a visual display and run statistical modelling. You’ll be surprised what you can learn from your data!