Statistical entropy analysis as tool for circular economy: Proof of concept by optimizing a lithium-ion battery waste sieving system

Abstract With the concept of circular economy gaining strength as an alternative for the sustainable production of raw materials, there is an inherent need to develop methods capable of quantifying the efficiency of recycling systems, provide guidelines for optimization of existing technologies, and support the design of new products based on sound, scientific and engineering principles. The work hereby presented proposes the use of statistical entropy coupled with material flow analysis as a basis for the optimization of separation and purification processes. Unlike other efficiency parameters, this approach provides an analysis of component concentration or dilution from a systemic perspective, taking into consideration products, by-products and waste streams. As a proof-of-concept, a sieving process for waste lithium-ion batteries (LIB) was chosen. It is demonstrated that using this approach it is possible to determine the stages that do not contribute to the concentration of components thus offering guidelines for process optimization. In the present case, the total number of sieving stages can be decreased with a minimum impact on the concentration of the products. In comparison, it is also shown that the widely accepted exergy analysis is not able to identify the opportunities for optimization due to the particular characteristics of this exemplary system, i.e., negligible change in energy consumption as a function of sieving stages and absence of chemical changes. Finally, the experimental results suggest that Al and Cu can be concentrated using a simple sieving pre-processing step, perhaps in preparation for a subsequent refining stage.