Project MONIPRO

Intelligent monitoring system embedded in copper’s transformation process (MONIPRO)

 

Details of co-funding

Ref. EEA GRANTS CALL: IDI-20140898

CDTI Funding: 253,741.50 €

Funding of donor countries (Liechtenstein, Iceland and Norway): 63,435.38 €

 

Summary

La Farga Rod process consumes a large amount of energy in a combination of fuels and electricity. In the continuous process of manufacturing wire (electrolytic copper), a tiny mismatch in the process parameters can lead to a reduction in product quality. This waste could be reprocessed relatively easily; however an additional high amount of energy is consumed, which would not have been necessary if these losses had not occurred. 

The main objective of the present project is to increase the energy efficiency of La Farga Rod electrolytic copper wire process and thereby reduce CO2 emissions to the atmosphere. To this end, the development of a new software system is planned, capable of ensuring in advance the quality of the final product in order to avoid as many losses as possible, and the waste of energy associated with the reprocessing of the refused products, and the production processes not being tuned optimally.

The planned approach is the development of a set of data based models, taking into account the interrelationships between the process variables, in order to build up a comprehensive model of the manufacturing plant. Using specific techniques, based on Artificial Intelligence, for managing and analyzing information, correlations can be detected between all the variables currently monitored and the associated parameters, thereby providing advanced knowledge of the quality before it is achieved.

Numerically speaking, La Farga Rod consumes the energy equivalent of 15,400 homes annually. The ultimate goal established in this project is therefore to reduce the product losses associated with reprocessing to 1%, which would mean an 80% reduction of the energy consumption associated with reprocessing, and which in absolute terms would mean a saving the equivalent to the total energy consumed by more than 600 homes in one year.



    • Segmentation of the database. The segmentation results will be used for the determination of critical information, and the training of different models involved in the monitoring system.
    • Connectors between monitoring system and database. Define the connectors between the various databases involved, and implementing monitoring and train developed algorithms.
    • Implementation of algorithms for intelligent supervision. Develop an application that works with their own integrated management systems and on real-time analysis. The proposed algorithm will analyses the process status and will determine the expected quality for the product being processed.
    • Extraction and selection of information from the database. Based on the experience and knowledge of the process and by suitable algorithm based on artificial intelligence, will determine which one are the critical variables that contain the highest percentage of the requested information (hidden patterns) to correlate it with the quality of the final product.
    • Proposal of numerical indicators for characterization and modeling of the plant. Development of algorithms for calculating numerical indicators that characterize and model each of the threads and the plant as a whole. This requires the calculation of statistical parameters in the time domain.
    • Proposal dimensionality reduction algorithms space indicators. The calculated numerical indicators describe a complete and accurate threads, probably to much information to introduce in the iterations. For this will be necessary to analyse the redundant information from them and get a set of equivalent numerical indicators.
    • Proposed algorithms modeling and characterization for determining the state line. Modeling and characterization of the process is made taking as input the reduced set of indicators and determined by a classification process, which is the state of the plant and the expected quality of the final product.
    • New process able to predict the quality product before it is obtained by analysing the process indicators in real time.
    • Increase performance and energy efficiency by reducing wastage and optimal adjustment process.
  • 8th October 2015
    Follow-up meeting of project MONIPRO

    The doctors responsible for La Farga’s R&D project and personnel attend the meeting. The results of the project are presented, and the final logic that will be added in the deliverable, the software module for monitoring quality in real time, is defined.

    We move forward as scheduled.

    Slide1    Slide2

    20151008 105617 copia    20151008 105636 copia





    May 2015
    Definition of the mathematical model.

    15maigMonipro    15maigMonipro1




    16th January 2015
    MoNiPro Project Follow-Up Meeting

    The technical works for the execution of the MoNiPro project are progressing according to schedule, as stated in the follow-up meeting held on Friday 16th January at the facilities of the GAIA building of the UPC, in which representatives of La Farga, CTM and MCIA-UPC took part.

    Apart from the review of the schedule, it was also decided to present the initial scientific results of the project at the IEEE International Conference on Industrial Technology (ICIT 2015), which will be held in Seville from 17th to 19th March 2015. The conference will bring together prestigious international specialists and researchers in the fields of industrial monitoring and smart control systems, industrial communications and flexible manufacturing, among others.

    DSC08706



    1st October 2014
    First follow-up meeting in La Farga, Les Masies of Voltregà.

    20141001 123152




    15th september 2014
    Installing the official badge of the project in the control room

    PC110605      PC110606




    1st September 2014

    MONIPRO startup project is formalized in a meeting in Madrid.



Legal terms | +34 938 504 100 | Colonia Lacambra, s/n  08508 Les Masies de Voltregà (Barcelona) Spain | © 2018 La Farga.