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DOE awards $9.3M to 6 projects to advance hydrogen technology that converts waste to clean energy

The US Department of Energy’s (DOE’s) Office of Fossil Energy and Carbon Management (FECM) announced six projects selected to receive approximately $9.3 million in federal funding to develop technology solutions to make clean hydrogen a more available and affordable fuel for electricity generation, industrial decarbonization, and transportation.

The projects will focus on advancing hydrogen systems that convert varied waste feedstock materials into clean energy with superior environmental performance. Selected projects will help communities by decreasing the volume of wastes sent to landfills and creating local economic opportunities by locating new waste-to-energy plants in these communities.

  • Demonstration of Biomass and Waste Controlled Feed System for Entrained Flow Gasification in the Production of Net-Zero HydrogenGTI Energy plans to develop and conduct a pilot demonstration of a novel “feedforward” gasifier feed control system that will maximize the effective syngas yield and ultimately the clean hydrogen yield from the conversion of biomass and waste feedstocks. The “feedforward” gasifier feed control system will measure and analyze the feedstock organic and inorganic compositions and optimize gasifier operating parameters such as the biomass/waste feed rate via a model-based controller to achieve a target operating temperature and target effective syngas yield. The project will also execute a full techno-economic analysis and life-cycle assessment study for a 500-tonne-per-day hydrogen production plant from biomass/waste mixtures, based on the proposed technology.

    DOE Funding: $3,984,275; Non-DOE Funding: $1,000,334; Total Value: $4,984,609

  • Machine Learning Enhanced LIBS for Automatic Control of Hydrogen Producing GasifiersLehigh University, in a collaborative effort with Energy Research Company, University of Utah, Idaho National Laboratory, and SpG Consulting, intends to demonstrate the feasibility of laser induced breakdown spectroscopy (LIBS) integrated with machine learning (ML) designed to provide information on the characteristics of feedstock streams into entrained-flow and fluidized bed gasifier systems that produce clean hydrogen with biomass, waste plastics and legacy coal waste as the feedstocks. The project will develop a LIBS system to detect and quantify feedstock materials under static and dynamic conditions coupled with ML algorithms to optimize the fluidized bed gasifier for optimum syngas production. The project will also perform a techno-economic analysis of the proposed technology integrated with hydrogen-producing gasifiers.

    DOE Funding: $2,954,021; Non-DOE Funding: $738,502; Total Value: $3,692,523

  • Development of Wireless Artificial Intelligence-Powered Multi-functional Fiber Optic Sensors for Hydrogen Production in Gasification-Based SystemsStevens Institute of Technology plans to develop and test the performance of a wireless artificial intelligence (AI)-powered multi-functional fiber optic sensor system for gasification-based systems. The sensor system will be designed to sense high temperature, pressure, strain, hydrogen concentration, carbon monoxide concentration, carbon dioxide concentration and fouling severity, simultaneously, in what is known as a multi-functional sensor system. The sensor signals are transmitted to a remote station wirelessly and analyzed to determine the condition of a gasification-based system in real time. The analysis of sensor data will be performed with AI methods based on deep learning techniques. A deep learning model will be trained to process and interpret sensing signals, continuously outputting the condition of the monitored gasification-based system, automatically and efficiently to enhance the competitiveness of gasification-based systems utilizing challenging mixed solid feedstocks to produce hydrogen.

    DOE Funding: $500,000; Non-DOE Funding: $126,366; Total Value: $626,366

  • Bimodular Nanoarray Sensors for Surface Fouling Monitoring in Gasifier Internal ComponentsThe University of Connecticut intends to develop a new class of nanostructured array (nanoarray) monolithic sensors for in-situ and real-time monitoring of surface particulate fouling on internal components of solid waste/biomass feedstock-based gasifiers such as heat exchanger tubes. The advantages of the nanoarray structured monoliths include robust sensor performance with low-pressure drops, easy customization, superior heat transfer, and easy tunability. Scale up of these nanoarray monolith sensors is readily achievable because of the University of Connecticut’s well-defined modular structure and scalable manufacturing techniques. Apart from functional testing, sensor manufacturability will be validated considering deployment design constraints, cost pressures, and energy efficiency requirements. An ultra-low-power wireless mesh communication technology will also be employed to interconnect the proposed surface fouling monitoring sensors and evaluated to justify the competitiveness of using such nanostructured monolith sensors for cost-effective and robust in-situ and real-time measurement of the surface fouling status.

    DOE Funding: $499,998; Non-DOE Funding: $125,000; Total Value: $624,998

  • Demonstration of an Integrated, Grid-Flexible, Hydrogen-Blended Turbine System With Innovative Point Source Carbon CaptureBaker Hughes plans to develop technology to integrate several novel and commercial state-of-the-art technologies to demonstrate a grid-flexible hydrogen-blended natural gas turbine system with innovative point source carbon capture at the lab-scale. The objectives of the project are to validate digital twin architecture for blue hydrogen with carbon capture from natural gas turbines, with variable renewable energy loads as input, and to demonstrate lab-scale operation of the proposed digital twin while achieving the Department of Energy target of a 95% carbon dioxide capture rate.

    DOE Funding: $691,457; Non-DOE Funding: $196,250; Total Value: $887,707

  • Deployment and Test Facility Data Implementation of Physics-Based Digital Twin and Surrogate-Model of a Hydrogen-Blend Combined Cycle Gas Turbine with Post-Combustion Carbon CaptureElectric Power Research Institute intends to create a cohesive Hydrogen Fuel Blend, Gas Turbine Combined Cycle with Carbon Capture (H2-GTCC-CC) digital twin model for use by the energy community to assess the net-zero capability and performance of new and existing gas turbine combined cycle plants when operated with hydrogen fuel blending and carbon capture under flexible operations. The project aims to enhance and integrate existing digital twins for gas turbines, Rankine cycles, and carbon capture to create the H2-GTCC-CC system model. The project will result in a web-based tool, which will allow both experts and non-experts to create their own H2-GTCC-CC digital twins and evaluate the ability of specific sites to enhance operational efficiency, optimize flexible operations, and support the transition to clean energy to meet net-zero goals using hydrogen blend fuels and carbon capture technology.

    DOE Funding: $700,000; Non-DOE Funding: $175,000; Total Value: $875,000

DOE’s National Energy Technology Laboratory (NETL), under the purview of FECM, will manage the selected projects.

p>Since January 2021, FECM has committed an estimated $138 million in projects that explore new, clean methods to produce hydrogen and to improve the performance of hydrogen-fueled turbines.


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