A Critical Review of Existing and Emerging Technologies and Systems to Optimize Solid Waste Management for Feedstocks and Energy Conversion

Khandoker Salem, Kathryn Clayson, Mariangeles Salas, Naimul Haque, Raman Rao, Sachin Agate, Anand Singh, James Levis, Ashutosh Mittal, John Yarbrough, Richard Venditti, Hasan Jameel, Lucian Lucia, Lokendra Pal

Research output: Contribution to journalArticlepeer-review

11 Scopus Citations

Abstract

Solid waste generation and its accumulation is increasing at an alarming pace due to population growth and urbanization posing severe risks to health, safety, and natural ecosystems. This review strategically addresses the challenges and solutions to increasing the sustainability footprint of solid waste management (SWM) systems by revealing multipronged approaches that reduce solid waste and handling costs while generating revenue and reducing greenhouse gas and related emissions. For example, the United States sends ∼150 million tons of waste to landfills, which is composed of over 75% organic and recyclable materials having a potential to be diverted to alternative scenarios. The emergence of an automated upstream and downstream sorting process for solid waste to increase material diversion from landfills is a promising approach for creating sustainable SWM. The utilization of artificial-intelligence-enabled smart and automated systems at the home and industrial scales, comprehensive public re-education including awareness of the adverse effects of landfilled waste on the ecosystem, and more eco-friendly product development are required to significantly reduce landfills and their negative footprint.

Original languageAmerican English
Pages (from-to)3348-3377
Number of pages30
JournalMatter
Volume6
Issue number10
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Inc.

NREL Publication Number

  • NREL/JA-2700-85263

Keywords

  • artificial intelligence
  • climate change
  • landfill diversion
  • smart systems
  • sustainable waste management

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