Machine learning models for estimating contamination across different curbside collection strategies.

Contaminated recyclables, which are frequently discarded as waste, pose a significant challenge to the implementation of a circular economy. These contaminated recyclables impede the circulation of resources, resulting in higher processing costs at material recovery facilities (MRFs). Over the past few decades, machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and random forest (RF) have evolved to provide new methods for predicting inbound contamination rates in addition to traditional statistical models. In this study, we applied ML models to predict inbound contamination rates using demographic features from 15 counties in the U.S. with different curbside collection strategies. In general, we found that ML models outperformed linear mixed models. Specifically, SVM models had the highest performance (R2 = 0.75; mean absolute error (MAE) = 0.06), which may be due to their ability to model nonlinear relationships between features and inbound contamination rates. The key predictor was population, with poverty rate being positively correlated and median age negatively correlated with inbound contamination rates. To improve the management of contamination and enhance the implementation of a circular economy, better models are needed to understand and estimate inbound contamination rates as well as identify critical factors in the present and future.

Recovering value from single stream material recovery facilities - An outbound contamination analysis in Florida.

The single stream recycling (SSR) program is a process in which all recyclable materials are deposited into a single collection bin. SSR has gained popularity in the U.S. due to its inherent abilities in waste collection, and specifically, in Florida, more than twenty counties have recently switched their recycling program from dual stream recycling (DSR) to SSR. Despite a more efficient collection process, mixing all recyclable materials into a single bin can lead to cross contamination even before reaching material recovery facilities (MRFs). This study aims to provide a better understanding of the sorting process and equipment in MRFs, and the impact of the SSR program on contamination rates in outbound materials that were processed through Florida's recycling systems. First, we investigate the audit data obtained from a currently operating MRF in Florida using mass flow analysis to identify the most problematic recyclable streams and the processes with low efficiency and high false separation rates. According to our results, the sorting rates of mixed paper, glass and plastics are under the industry standards. Moreover, we investigate the outbound contamination rates of 35 old corrugated cardboard (OCC) and 266 old newsprints (ONP) samples obtained from four currently operating MRFs in Florida. Based on the results, only 31.4% of OCC samples and none of the ONP samples were within the accepted mills' standards for contamination rates. This study provides valuable insights for lowering contamination and raising the end-product quality by identifying the problematic contaminants and processes in sorting and separation in MRFs.