Quantitative Methods to Support Data Acquisition Modernization within Copper Smelters

Sensors and process control systems are essential for process automation and optimization. Many sectors have adapted to the Industry 4.0 paradigm, but copper smelters remain hesitant to implement these technologies without appropriate justification, as many critical functions remain subject to groun...

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Bibliographic Details
Published in:Processes 2020-11, Vol.8 (11), p.1478
Main Authors: Navarra, Alessandro, Wilson, Ryan, Parra, Roberto, Toro, Norman, Ross, Andrés, Nave, Jean-Christophe, Mackey, Phillip J.
Format: Article
Language:English
Subjects:
Automation
Batch processes
Competition
Control systems
Copper
Data acquisition
Discrete event systems
Distributed control systems
Expert systems
Industrial applications
Industrial Revolution
Metallurgy
Modernization
Nickel
Optimization
Optoelectronics
Process controls
Production capacity
Quantitative analysis
Reengineering
Sensors
Smelters
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Summary:Sensors and process control systems are essential for process automation and optimization. Many sectors have adapted to the Industry 4.0 paradigm, but copper smelters remain hesitant to implement these technologies without appropriate justification, as many critical functions remain subject to ground operator experience. Recent experiments and industrial trials using radiometric optoelectronic data acquisition, coupled with advanced quantitative methods and expert systems, have successfully distinguished between mineral species in reactive vessels with high classification rates. These experiments demonstrate the increasing potential for the online monitoring of the state of a charge in pyrometallurgical furnaces, allowing data-driven adjustments to critical operational parameters. However, the justification to implement an innovative control system requires a quantitative framework that is conducive to multiphase engineering projects. This paper presents a unified quantitative framework for copper and nickel-copper smelters, which integrates thermochemical modeling into discrete event simulation and is, indeed, able to simulate smelters, with and without a proposed set of sensors, thus quantifying the benefit of these sensors. Sample computations are presented, which are based on the authors’ experiences in smelter reengineering projects.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr8111478