Unlocking advanced waste management models: Machine learning integration of emerging technologies into regional systems

Resources Conservation & Recycling Advances, vol. 26, pp. 200253

Abstract

• Gasification, pyrolysis, and dry reforming integrated into a waste management model. • Machine learning replaced rigorous simulation in modeling waste management systems. • Three waste technology scenarios were evaluated using the integrated model. • New technologies improve waste management from an environmental perspective. • Unit capacity is critical to reducing landfill rates and environmental impacts. The waste management sector requires specialized systems analysis tools to facilitate decision-making and make waste management sustainable and efficient. While integrated systemic approaches exist for assessing conventional waste management systems, the integration of emerging technologies such as gasification, pyrolysis, and methane dry reforming remains largely overlooked. In this work, these three technologies have been integrated into a conventional regional waste management model by abstracting rigorous simulation models into machine-learning surrogate models. The resulting technology-rich waste management model incorporates material flow analysis and life-cycle assessment as tools for supporting policy and decision-making. The model was tested by assessing the environmental impacts and landfill rates for three technology implementation scenarios. Overall, the inclusion of these emerging technologies led to an environmental performance improvement compared to a reference system. For example, a 116.5 % reduction of the carbon footprint in the most optimistic scenario. Nevertheless, the mere addition of these technologies was not enough to achieve landfill rates below 10 %, reaching 37.6 % in the most optimistic scenario. Therefore, properly sizing capacity was found to be a key factor in minimizing both environmental impact and landfill rate.

Authors 4

1. Nicolás Martínez-Ramón

   Universidad Rey Juan Carlos · IMDEA Energy Institute

   Affiliation as printed

   IMDEA Energy, Systems Analysis Unit, Móstoles 28935, Spain

   Rey Juan Carlos University, Chemical and Environmental Engineering Group, Móstoles 28933, Spain

2. Ioan-Robert Istrate Aachen Institute of Environmental Sciences (CML)

   Leiden University

   Affiliation as printed

   Leiden University, Institute of Environmental Sciences (CML), Leiden, CC 2333, The Netherlands

3. Diego Iribarren corresponding

   IMDEA Energy Institute

   Affiliation as printed

   IMDEA Energy, Systems Analysis Unit, Móstoles 28935, Spain

4. Javier Dufour

   Universidad Rey Juan Carlos · IMDEA Energy Institute

   Affiliation as printed

   IMDEA Energy, Systems Analysis Unit, Móstoles 28935, Spain

   Rey Juan Carlos University, Chemical and Environmental Engineering Group, Móstoles 28933, Spain