Abstract

An evolved Optibar intercell bar for copper electrowinning processes with current sensing capabilities is presented. This technology upgrades the advantages of the conventional Optibar by providing a complete measuring system using magnetic sensors inside the capping board. To enhance reliability and simplicity, only half of the intercell currents are physically measured. This is accomplished using adaptive neurofuzzy inference system networks to calculate the current flowing through nonsensored connecting Optibar segments using virtual sensors. This way, process cathode currents are available online enabling myriad of key process computations; i.e., cathode harvest time, weight at harvest, current dispersion among cathodes, energy consumption and process efficiency, and online setting of optimum process current level based on the ability to detect and locate metallurgical short-circuits events. In this paper, this task is accomplished using a short-circuit diagnosis algorithm based on the recognition of current distribution patterns that characterizes the phenomena using artificial neural networks. The technology employed to implement the system is completely embedded in the bar to ensure compatibility with the process environment. From the outside, Optibar add-ons are hidden and do not disrupt the operation. On site industrial data proved that cathode current measurements exhibit an average absolute error lower than 2% with a dispersion lower than 1.6%. Finally, the algorithm developed for short-circuit diagnosis exhibits a success rate of 98% or better. © 2009 IEEE.