Abstract
The quality of prebaked carbon anodes, consumed in electrolysis during the primary aluminum production, has an important impact on the cell performance. The anode quality depends on the raw material quality and operating conditions in the anode plant. Development of simple, quick, and inexpensive techniques and tools for anode quality control will help industry identify the source of problems and take the necessary corrective actions rapidly. In this article, different quality control tools developed to find the wettability of coke by pitch, effect of mixing on coke particle size distribution, metallic impurity content, optimum vibration time, pitch content in green anode, and the measurement of green and baked anode electrical resistivities are presented. In parallel, data analysis using the artificial neural network (ANN), a powerful statistical tool for such applications, provides complementary information on quality and process. This article also presents the potential utilization of ANN in quality control.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
