La Farga Rod La Farga Rod

INTELLIGENT MONITORING SYSTEM
EMBEDDED IN COPPER'S
TRANSFORMATION PROCESS
(MONIPRO)
Juan A. ORTEGA, Representing
LA FARGA ROD SL
Present worldwide
We have 3 production plants in Spain (La Farga Lacambra, La Farga Tub and La Farga
Rod), one in China (Ganzhou Jiangwu La Farga), one in the United States (La Farga SDI)
and two copper pipe distribution centres, one in France (La Farga Tub France) and
another in England (La Farga Limited). Through La Farga Intec we have sold our
process technology, which is already present in more than 30 plants worldwide.
La Farga Rod
La Farga Rod, SLU manufactures and sells electrolytic copper rod. Using
cutting-edge technology (2008), which includes the most advanced state-ofthe-art in the industry, a high performance wire is obtained that ensures the
highest levels of quality and productivity to customers..
PRODUCT: Cu-ETP copper rod obtained by
continuous casting for electrical conductor
market
Objectives
 Increase the energy efficiency of the copper process.
 Ensure the quality of the final product in order to avoid economic
losses, the waste of energy associated to the reprocess, and maintain
the optimal tune of the process.
 80% reduction of the waste of energy, which means to save 4.880
MWh annually.
La Farga Rod Plant
Industrial manufacturing plant database
Complete database containing 3 years of operation, more than 5
million of observations and more than 3000 variables that
monitor all the manufacturing processes and the main machines
involved , is available.
Data-Driven Models
• Data-Driven approaches aims at using real CM data acquired
from the process in order to generate a predictive model.
• These methods usually learn from “examples” and try to
capture the subtle functional relation among the data, for
then reproducing the expected behaviour with new inputs.
System functionalities
• Determination of the current state of the production plant: The full data
driven plant model determines the current state and computes the
detuning from the optimal working point.
• Final product quality forecasting: The relationship between the current
state of the production plant and its theoretical working parameters, will be
associated with the final product.
• Identification of the detuning root causes: The distributed architecture
of the data driven model will allow to determine the root cause of the
malfunction.
Block Diagram
Inputs
Manufacturing
Line
Material
Machines
Final
Product
Processes
Intelligent
Monitoring
System
Historical
Data
Performance
Quality

Raw material data

Measured quality historical records

Monitored variable historical records

Production Orders ….
Outputs

Operating status of the production line

Expected quality of the final product
Work Plan
Activity 1. Database compilation and information extraction.
Activity 2. Distributed data-driven models generation.
Activity 3. Intelligent data fusion of distributed models.
Activity 4. Plant Validation of full models for final quality prediction
Activities
2014
M1 M2
A1 Database compilation and information extraction
A2 Distributed data-driven models generation
A3 Intelligent data fusion of distributed models
A4 Plant Validation of full models for final quality prediction
M3 M4 M5 M6
M7
2015
M8 M9
M 10 M 11 M 12 M 1
M2 M3
M4
M5 M6 M7 M8
M 9 M 10 M 11 M 12
Thank you
for your attention
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