A MONTE-CARLO METHOD FOR EVALUATING THE ECONOMIC PERFORMANCE OF PLASTICS RECYCLING SYSTEMS USING HISTORICAL PRICING

The management of plastic waste is a considerable emerging global concern. Conventional plastics recycling rates range from 5-10% in the US, which results in considerable material being sent to landfill. New sorting technologies using artificial intelligence have the potential to dramatically increase the ability of waste management companies to sort out valuable fractions of plastic waste and create high-purity streams for secondary markets. However, several fractions of the plastics recycling stream, i.e., low-density polyethylene, polypropylene, and polystyrene, do not have well-developed markets. These materials may be sent to pyrolysis facilities for thermal recycling, but the economics of the pyrolysis industry are currently uncertain. This work aims to identify the breakeven price of the plastic fractions that would be sent to pyrolysis in order to determine the economic viability of the sorting facility. The work will use Monte Carlo analysis to evaluate several scenarios and understand how compositional variation, price variability, and facility attributes affect the breakeven price. The presorting conditions in Materials Recovery Facilities strongly affects the profitability of the sorting facility. High residual high density polyethylene (HDPE) and polyethylene terephthalate (PET) content in presorted bales are important revenue drivers for the facility, even in presorted 3-7 bales. Key variables that lead to profitability include a high sale price of polyethylene terephthalate, a high sale price of high-density polyethylene, and if polypropylene can be sold to recycling markets (as opposed to pyrolysis facilities).