Study of energy efficiency in the consumption pattern of the chocolate coating line

Aingura insights

Electrointensive Industries

Consumption Efficiency

Potential Improvement in energy efficiency between 7% and 10% depending on equipment.
Potential improvement in energy consumption by 15% approx. in support systems such as cooling.

Pattern analysis, prediction and intelligent classification

To explore production system consumption pattern to detect hidden electric energy consumers that are large enough to impact in the overall energy consumption.
To provide an advanced level of energy consumption analysis after applying traditional efficiency techniques.
To detect outlier situations where energy consumption is deviated from normal consumption to include them in the next version of company’s environmental and energy consumption policies.

Dataset size: 500 MB.
Number of variables: 7.
Sampling time: 1s.
Exploratory clustering algorithms (Gaussian mixtures, k- means, agglomerates) have been applied to search for atypical consumers or energy consumption deviations.
Multidimensional scaling techniques have been used to visually analyze these patterns.

GMM clustering anakysis

Figure 17. Consumption pattern of the food coating line.

A temporal analysis has also been carried out on the consumption pattern. A deviation has been detected related to a fire in the capacitor bank a month later. This is shown in Figure 18.

Figure 18. Temporal analysis of the consumption pattern.

Cluster 0 and 1 show the change in the behavior of the system from the energy point of view at the beginning of September. Cluster 1 contains all the data from that change until the fire in late October.
Cluster 2 also shows other random consumption patterns that imply an increase in the average consumption of the line, which should be studied.