Artificial intelligence increasing waste-to-energy production

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Recently 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 municipal solid waste (MSW) streams. 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.

The ERC and ERCo have previously worked on a method of using LIBS and AI to better analyze coal for power generation. Researchers say that with LIBS they were only able to measure the elemental composition of the fuel. But, by using AI neural networks they were able to improve measurement accuracy and correlate elemental composition to other high order parameters such as calorific value and ash fusion temperature.

Waste-to-energy producers need an accurate analysis of the waste material in any given lot. There are standardized procedures for how a representative sample is arrived at and analyzed. The team’s innovative LIBS-Raman Spectroscopy, combined with AI, has the potential to significantly improve the accuracy of the analysis as well as the speed at which it occurs, while facilitating the incorporation of this information into the bioenergy reactor process control.

This project could lead to a process that is both easier and less costly, making waste-to-energy a more attractive alternative to the landfill, and moving the U.S. closer to a waste-processing approach that is sustainable. The proposed project supports BETO’s goal of innovation to accelerate feedstock technologies that would propel a bio-economy, by allowing real-time characterization of MSW feedstock for feed-forward process control of downstream biofuel production processes.

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