Multi-objective optimization of some process parameters of a lab-scale thickener using grey relational analysis

This paper presents a novel effective method for the optimization of some operating parameters of a laboratory scale thickener on the dewatering performance with multiple performance characteristics based on the grey relational analysis (GRA). Lab-scale thickener operation parameters including feed flowrate, solid percent, flocculant dosage and feedwell height were optimized based on multiple performance characteristics. Sixteen experiments were conducted using GRA to optimize the parameters for lab-scale thickener parameters to generate two quality characteristics (the underflow solid percent and bed height of thickener). Analysis of the grey relational grade indicates that parameter significance and the optimal parameter combination for the lab-scale thickener are identified. The analytical results from two confirmation experiments using the optimal process parameters confirm that the above performance characteristics in dewatering of tailing can be improved effectively through this approach.

► The multi-objective optimization of a lab-scale thickener process was accomplished.
► The analytical data was generated by using Taguchi array design with four parameters.
► Optimal levels of process parameters were identified using grey relational analysis.
► The contribution of each parameter was weighted according to its importance.