NC State research could improve predictions for solid waste management

Researchers from the North Carolina State University have developed a new approach for predicting the contents of municipal solid waste that can help improve the efficiency of recycling and landfill operations.

The two-phase strategy predicts the waste composition, then predicts the total quantity, and the two predictions are combined to give a comprehensive waste estimate. The research is outlined in a new paper, “Predicting the Composition of Solid Waste at the County Scale,” published in the journal Waste Management. The paper was co-authored by Rajesh Buch of Arizona State University

“The effect of our new approach is that solid waste managers can forecast a detailed breakdown of the different materials that will make up the waste stream in addition to the overall tonnage of waste they might expect in the coming year,” says Adolfo Escobedo, co-author of a paper on the work and an associate professor in North Carolina State University’s Edward P. Fitts Department of Industrial and Systems Engineering.

RELATED: NC State awarded $2.25M grant to develop sustainable energy products from waste streams

The new method applies a conventional approach to forecasting how many total tons of solid waste will be generated at the county level and incorporates a separate, complementary model that predicts the makeup of the waste with an unprecedented level of detail.

The result is capable of estimating municipal solid waste composition across 43 comprehensive material categories, ranging from aluminum cans to food waste. Users can then couple the outputs from the waste composition model with predictions of total solid waste from well-established techniques.

“For example, if the model for waste tonnage predicts there will be 1,000 tons of solid waste, and the composition model predicts that 25 percent of the waste will be food waste, you end up with a prediction of 250 tons of food waste,” says Joshua Grassel, corresponding author of the paper and a Ph.D. student in the operations research graduate program at North Carolina State.

This novel approach overcomes limitation of existing methods that rely on material-specific quantity data, facilitating the prediction of dozens of waste material streams, the researchers say. Existing methods typically classify MSW into no more than 10 categories and often reduce it to a single aggregate total.

To implement the strategy, the proposed study uses publicly available data encompassing demographic, economic and spatial predictors in conjunction with waste sampling reports. In addition, it develops a Least Absolute Shrinkage and Selection Operator regression model to estimate the MSW composition across 43 comprehensive material categories.