AI to Pave the Way to Increased Waste-to-Energy Production

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September 28, 2021

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by Lori Friedman (Lehigh University) … “… One is to take it to a landfill, another is burning it to produce electricity, and another is utilizing it as a feedstock for producing biofuels and bioproducts. However, the nature of MSW, a very heterogeneous material, with large variability in its physical, chemical and biological characteristics, poses significant challenges in optimizing MSW conversion processes.”

The Lehigh University Energy Research Center (ERC) has been awarded a new $3.5 million project by the U.S. Department of Energy (DOE) for the development of advanced technology for rapid detection and analysis of MSW streams. This project will be part of a $34 million effort from the Bioenergy Technology Office (BETO) of the Office of Energy Efficiency & Renewable Energy (EERE) to support high-impact research and development to improve and produce biofuels, biopower, and bioproducts. Lehigh will lead a team that includes the Energy Research Company (ERCo), Department of Energy’s (DOE’s) National Energy Technology Laboratory (NETL), ThermoChem Recovery International (TRI), Covanta Energy, the University of Toledo, and SpG Consulting. Lehigh participants include Romero (PI), Zheng Yao, research scientist at Lehigh’s ERC, and Farrah Moazeni, a faculty member in Lehigh’s Department of Civil and Environmental Engineering.

The team will work on streamlining one of the most complex aspects of the waste-to-bioenergy process: analysis of the material. The project will bring together two types of leading-edge spectroscopy, Laser Induced Breakdown Spectroscopy (LIBS) and Raman Spectroscopy, in combination with artificial intelligence (AI).

The technology the team is working on is designed to provide rapid, in-situ characterization of MSW feedstock, providing critical characterization and chemical analysis data in minutes for feed-forward process control of downstream biofuel production processes. The project includes the development of both hardware and software elements that, together, will be capable of improving MSW characterization throughput over baseline methods by at least 25%. The approach could make it possible to process waste material in minutes, instead of hours.

Read the full story in the Lehigh University News Center. READ MORE

AI to Pave the Way to Increased Waste-to-Energy Production (Lehigh University News Center)

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