Capability Evaluation and Statistical Control of Electrostatic Separation Processes

The variability of the outcome is often deplored by the users of electrostatic separation equipment. A slight modification of the composition of the processed material, a change in the environmental conditions, or an alteration of the electrode configuration may cause objectionable fluctuation of product quality. Thus, the purity of the copper product recovered from electric cable wastes can easily diminish from 98%, which is fully acceptable for recycling purposes, to a value below the standard level of 97%. This paper is written with the aim of showing how statistical process control methods could contribute to the improvement of users´ overall satisfaction. In order to validate a procedure for evaluating the short- and long-term capabilities of the electrostatic separation process, the authors carried out a series of experiments on subgroups of chopped electric cable wastes, which are similar to those currently processed in the recycling industry (95% polyvinyl chloride; 5% copper). The first set of experimental data showed that an ldquoin-controlrdquo electrostatic separation process can easily satisfy the level of performance required by the customers (i.e., a capability index of > 1.33). The same set of data enabled the computation of the upper and lower limits of the R- and X -bar control charts. The second set of experiments was designed to evaluate the ability of these charts to detect the following typical situations of abnormal operation of the electrostatic separation equipment: 1) a change in the composition of the processed material; 2) a failed connection of the static electrode to the high-voltage supply; and 3) the accumulation of dust on the surface of the corona electrode. Based on the authors´ experience as consultants for the recycling industry, the following several recommendations have been formulated: 1) Evaluate the capability of the electrostatic separation process by performing no more than ten observations and making u- se of the confidence indexes; 2) use moving R- and X-bar control charts to obviate the difficulty of sampling; and 3) prepare a ldquoCorrective Actions Guiderdquo for the operators using control charts for monitoring.