MACHINE LEARNING ENHANCED LASER INDUCED BREAKDOWN SPECTROSCOPY (LIBS) FOR AUTOMATIC CONTROL OF HYDROGEN PRODUCING GASIFIERS. THE PROJECT’S OVERALL OBJECTIVE IS TO DEMONSTRATE THE FEASIBILITY OF 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. TO ACHIEVE THIS GOAL, THE RESEARCH TEAM WILL FOCUS ON THE FOLLOWING SPECIFIC OBJECTIVES AND CRITICAL FACTORS: (1) ASSEMBLING A MATERIAL INVENTORY AND DEVELOPING SAMPLING PROCEDURE; (2) DESIGNING AND ASSEMBLING LIBS SYSTEM FOR STATIC AND DYNAMIC CONDITIONS; (3) DEVELOPING AND VALIDATING ML ALGORITHMS FOR LIBS DATA PROCESSING; (4) INSTALLING LIBS+ML SYSTEMS ON ONE ENTRAINED-FLOW AND ONE FLUIDIZED BED GASIFIER; (5) INTEGRATING LIBS+ML OUTPUTS INTO GASIFIER CONTROL SYSTEMS; AND (6) PERFORMING A TECHNO-ECONOMIC ANALYSIS (TEA).