Designing a continuous quality improvement framework for improving electrowinning current efficiency.

Abstract

In general, the electrowinning process consumes substantial electrical energy. Considering the ever-increasing unit cost of electrical power there is a need to improve current efficiency so that electrical energy is utilised efficiently. In light of the research/knowledge gap identified, this research aims to design a continuous quality improvement framework for improving electrowinning current efficiency. The objectives of this research are as follow: (i) To explore factors that influence current efficiency; (ii) To evaluate the factor that has the most significant effect on current efficiency, by applying statistical process control; and finally (iii) To design a continuous quality improvement framework for improving electrowinning current efficiency, by applying statistical process control. The scope of work for this research focused on applying statistical process control on an online industrial copper electrowinning process instead of doing laboratory experiments. In this case, a sequential mixed research methodology was applied and Minitab statistical software package was utilized for analysing data by creating control charts. The factors that influence current efficiency were explored and the main factors are as follow: metallurgical short-circuits, impurities, electrode condition, electrode alignment, contacts condition, electrolyte temperature, reagent addition, electrolyte acid concentration, current density, rectifier current, electrode insulators, cathode nodules, electrolyte copper content, and electrolyte flow rate. After analysing constructed control charts and implementing an out of control action plan, it was concluded that metallurgical short-circuits (hotspots) have the most significant effect on current efficiency than all the other factors. Bringing hotspots under statistical control resulted in improved current efficiency by 5.40 % which is equivalent to approximately 74 MT of 99.999 % pure grade A copper cathode production over a period of 1.5 months. Finally, a continuous quality improvement framework for improving electrowinning current efficiency was designed. This was done by considering the following: Anderson Darlington normality test, non-normal data transformation (using Johnson and Box-Cox transformation), constructing control charts, and then analysing control charts which include Pearson correlation analysis, out of control points alignment analysis, root cause analysis, process capability analysis, and implementing an out of control action plan.