INDUSTRY & SUSTAINABILITY
Energy consumption optimization: cut energy use without touching output
Energy consumption optimization with TIMi identifies, in an industrial or operational process, the parameters that influence energy use the most, to adjust operations without compromising production, and shows the relative influence of each.
Energy levers
See which parameters move consumption, and by how much.
Extreme conditions
AGC modeled furnace dynamics at 1400 °C, where sensors burn out.
Explained recommendations
Each adjustment comes with the factors that justify it.
HOW IT WORKS
How energy consumption optimization works.
Three steps, in one engine, on a standard server with no cluster.
1. Prepare the data
Anatella gathers the energy consumption data, the operating parameters of the process and the environmental conditions, then structures them into variables.
2. Build the model
Modeler links the operating parameters to the consumption observed, to identify which ones have the most influence on energy efficiency.
3. Recommend and explain
Each recommended adjustment comes with the factors that justify it, and the estimated influence of each parameter.
Anatella
ModelerGETTING IT RIGHT
Choices that shape an energy model.
The goal is lower consumption, with the same output.
Protect quality and pace
The risk is to improve consumption at the expense of production. Quality or yield data can be included as a constraint from the modeling stage, so that recommendations do not trade output for energy.
Start from the measures you have
If consumption is already measured through building management or supervision systems, it can be a sufficient starting point before considering extra sensors.
Extreme conditions are possible
The modeling principle stays the same whatever the operating conditions, as long as operating and consumption data are measured and available. At AGC, TIMi modeled furnace dynamics at 1400 °C, where physical sensors degrade and data is noisy.
EXPLAINABILITY
A recommendation says what to adjust. The factors say what it costs.
A recommendation alone says what to adjust, not why or by how much. A white-box model shows the relative influence of each parameter on consumption, so technical teams can check that a proposed adjustment has no unexpected effect on quality or pace before applying it.
For technical teams
A parameter adjustment can be justified and tested in a targeted way, instead of being applied from an opaque recommendation.
For environmental reporting
Understanding the levers of optimization makes it easier to document the actions taken to reduce the energy footprint of a site or a process.
For model owners
Modeler’s built-in Drift Report shows when a model loses accuracy, so you update it when needed instead of on a fixed calendar.
DATA
What data an energy model uses.
Anatella connects to more than 80 sources, so this data can be gathered without prior custom development.
Energy consumption
Consumption of the process over time.
Operating parameters
Settings and control parameters of the process.
Environmental conditions
Ambient temperature, seasonality.
Production volumes
Volumes or pace for each period measured.
PROOF
Energy optimization in production.
AGC is our reference for energy optimization under extreme conditions.
Fuel consumption down 5% on glass furnaces
TIMi modeled the thermal dynamics of furnaces running at 1400 °C, where other technologies failed because of sensor burnout and noisy data. Savings amount to millions of euros every year.
FAQ
Common questions.
Does it work on processes under extreme conditions?
Yes. The modeling principle is the same whatever the operating conditions, as long as the operating and consumption data are measured and available.
Could optimizing energy hurt production quality?
It is a point of vigilance to integrate from the modeling stage: quality or yield data can be included as a constraint, so that recommendations do not improve consumption at the expense of production.
Do we need dedicated energy sensors?
If consumption measures already exist through building management or supervision systems, they can be a sufficient starting point before considering extra sensors.
Can it feed emissions reporting?
The consumption-reduction levers identified can feed an exercise on the emissions associated with that consumption. The reporting itself is done in your own framework.
SEE FOR YOURSELF
Test it on your own data.
Run TIMi on your own data with a free trial, or talk to us about your case.
