Abstract

An evolved Optibar inter-cell bar for copper EW 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 Neuro-Fuzzy Inference System networks to calculate the current flowing through non-sensored connecting Optibar segments using virtual sensors. This way, process cathode currents are available on-line enabling a myriad of key process computations; i.e. cathode harvest time, weight at harvest, current dispersion among cathodes, energy consumption and process efficiency, and on-line setting of optimum process current level based on the ability to detect and locate metallurgical short-circuits events. In this work, 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 currents 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.