Electrointensive Industries

Minimize energy consumption with the same facilities

Closed-loop control aimed at reducing the consumption profile and improving the degree of simultaneity, based on the energy footprint and Machine Learning.



    Optimized energy use targets 5-15% long-term cost savings

    • Continuous e ciency improvements aim for 10-20% reductions in energy-related expenses.
    • Data-driven strategies may lead to 5-10% long-term savings.
    • Edge computing o ers substantial potential for 15-25% long-term cost reductions in precision.
  • Adaptability

    Seamless integration enhances operational efficiency without disrupting ongoing processes

    • Adapts to process changes for cost savings, evolving without significant new investments.
    • In-house implementation allows ongoing improvements, ensuring competitiveness and agility.
    • Ensures both current competitiveness and future resilience against industry changes.


AI and data acquisition technologies implemented at the edges

Enable microsecond-level processing for real-time AI-based anomaly detection

Real-time signal analysis optimizes energy management

Swiftly identifying anomalies for immediate e ciency improvements and cost savings through AI-based processing.

Focusing on distributed energy measurement up to 8 kHz

This expertise provides valuable insights into energy consumption and system performance.


Author: M. Bermeo-Ayerbe, Carlos Ocampo-Martinez, and J. Diaz-Rozo

Title: Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems

Publication: Journal of Energy, vol. 238, p. 121691

Date: January 2022

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Author: M. Bermeo-Ayerbe, Carlos Ocampo-Martinez, and J. Diaz-Rozo

Title: Adaptive predictive control for peripheral equipment management to enhance energy efficiency in smart manufacturing systems

Publication: Journal of Cleaner Production, vol. 291, p. 125556

Date: April 2021

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Author: J. L. Diaz, M. Bermeo, J. Diaz-Rozo, y C. Ocampo-Martinez

Title: An optimization-based control strategy for energy efficiency of discrete manufacturing systems

Publication: ISA Transactions, vol. 93, pp. 399-409

Date: October 2019

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