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AI centric solutions are needed for advanced sensors, controls, materials, and computing, as well as enabling real-time control, smart diagnostics, and accelerated materials development.  The ERC is applying machine learning technologies to form a holistic ecosystem to advance energy conversion, storage, and data-driven innovation in energy systems.

Related Journal Articles

  • Combustion optimization of a coal-fired power plant boiler using artificial intelligence neural networks

  • Analysis of LIBS Data from Coal and Biomass Using Artificial Intelligence Techniques

  • Online LIBS–ML Framework for Dynamic Characterization of Heterogeneous Waste-Derived Gasification Feedstocks

  • Machine Learning in the Context of Laser-Induced Breakdown Spectroscopy

  • Artificial Intelligence Techniques in LIBS Data Analysis and Interpretation

  • Rapid characterization of MSW and RDF feedstocks for waste-to-energy process using LIBS and ML techniques

  • Analysis of LIBS data from coal and biomass using artificial intelligence techniques

  • A globally enhanced general regression neural network for on-line multiple emissions prediction of utility boiler

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  • Home (current)
  • Vision (current)
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    • Conventional Power Generation
    • Critical Materials
    • Cross-cutting Technologies
    • Water-Energy Nexus
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    • Environmental Compliance
    • Data Center Cooling
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    • Laboratories
    • Equipment
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    • Collaborate with ERC

Energy Research Center

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    Lehigh University Energy Research Center
    117 ATLSS Drive
    Bethlehem, PA 18015
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