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Poster presenting NETL's geologic hydrogen project at the 2024 AGU Annual Meeting.
This Python package implements a high-level interface to access the data published by the Particle Data Group (PDG) in the Review of Particle Physics (the "Review"). The Particle Data Group is an international collaboration led by the PDG group at LBNL. The PDG summarizes the established knowledge in the field of particle physics in a single publication, the Review of Particle Physics. The Review is published and updated online (see https://pdg.lbl.gov) each year, and published in a scientific journal every other year. It is currently licensed under a CC BY-NC 4.0 license. In 2021 PDG was designated by the Office of Science as a SC PuRe Data Resource. The Review is one of the most highly cited publications in the field of particle physics. The PDG Python API is part of PDG's efforts to make all data provided in the Review available in machine-readable format. This data includes the PDG world averages (or best limits) on particle masses, widths or lifetimes, branching fractions, magnetic moments, form factors, coupling constant ratios, and searches, as well as particle quantum numbers. It also includes detailed information on how PDG arrived at its averages, such as e.g. tables of published measurements with comments and footnotes, information on the consistency of published measurements, and detailed fit information.
Recent advances in the chemical process industry have allowed for the intensification of reactors and unit operations, by enhancing heat and mass transfer or combining multiple unit operations. Process intensification can enable chemical plants to be constructed in a more compact, modular fashion, offering improvements over conventional on-site approaches to capital construction, operations and maintenance. This modular chemical process intensification (MCPI) offers several benefits over conventional stick-built (CSB) plant construction in terms of reduced footprint, reduced energy consumption, lower cost, less waste and improved safety and quality control. However, acceptance of MCPI over CSB construction practices within the chemical industry can be impeded by the uncertain risks associated with investing in new technology. Furthermore, the work here documents a case study during the development of a modular chemical plant for capturing distributed energy resources within chemical production. This MCPI approach to plant construction is contrasted with a CSB approach for producing the same chemical product. Data collection tools were developed, based on a literature review, and data was collected from the technology developer to understand the process technology. Sensitivity analysis was then conducted to analyze the business rationale for the application of MCPI over CSB across several market scenarios. It was found that MCPI would be better suited for capacities up to 150 000 metric tons per year, but that improvement in payback period was needed. Additionally, for MCPI approach to achieve acceptable payback periods, efforts are needed to reduce the cost of capital equipment and compress the schedule for ramping up modular production.
Chemical state analysis of a sample surface through fitting bell-shaped curves to x-ray photoelectron spectroscopic polymer data is reviewed using nylon to introduce and discuss aspects of data analysis. Different strategies for modeling chemistry in nylon spectra are presented and in so doing, a case is made to include in published science the design logic and implementation in terms of line shapes and optimization parameter constraints between components in a peak model. Imperfections in line shape relative to the true shape for photoemission lines, when compensated for using constraints to optimization parameters, are shown to provide chemical state information about a sample that justify, for peak models constructed with these limitations, metrics for goodness-of-fit different from those expected for pulse-counted data.
Methane generation has been detected during thermolytic Hydrogen Generation Rate (HGR) testing of SRS tank waste and in simulant samples. Simulant testing has shown methane generation in the presence of trimethylsilanol (TMS) and radioactive sample testing has shown methane generation in presence of methylmercury, both of which are found within SRS waste tanks. The 2019 report Evaluation of the Current State of Knowledge for Thermolysis of Organics within SRS Waste Forming Volatile Organic Compounds (VOCs) reviewed historical data and assessed literature pertaining to thermolytic methane generation. That review recommended undertaking thermolysis studies on simulated waste to detect and quantify VOCs. The purpose of this assessment is to review chemistry and current knowledge of organics in SRS waste to recommend which organics in SRS waste tanks, if any, should be tested for thermolytic methane generation. An evaluation of observed methane generation rates has been performed to assess the implication for current CSTF controls. Based on the inputs used in this report, it was concluded that no methane generation data observed to date has challenged the existing 5% organic contribution to the Composite Lower Flammability Limit (CLFL) assumption in the CSTF tank farm at temperatures below 100 °C. Evidence shows that thermolytic pathways to methane generation become significant at temperatures >100 °C in caustic waste. Currently, the influences of thermolytic methane production on the flammability of vessels at >100 °C (e.g., evaporators) is unclear. It is recommended that testing should be undertaken using the HGR experimental approach for methane generation. The initial simulant testing should obtain quantitative methane generation rates at elevated temperatures for methylated siloxanes/silanes and methylmercury with the goal of determining the current mechanism for thermolytic methane generation seen in SRS tank waste. The results of this testing should then be compiled and analyzed to ascertain the risk of challenging the current projected organic contribution of 5% to the CLFL at elevated temperatures. Following this testing, an evaluation of possible flammable gas generation in the 2H and 3H Evaporators should be performed to assess the risk of methane generation at the high temperatures employed by the evaporators.
This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in an urbanized catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, distributed temperature sensing (DTS), continuous self-potential (SP) monitoring, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Field_Application subfolder contains the ATS XML input scripts, data files, output data for the SP site. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. The flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.m can only be used with COMSOL with MATLAB) is executed using the ATS output data to simulate the potential field. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) DTS Contains collated DTS data including raw Stokes and anti-Stokes measurement (provided as .h5 file). It also includes DTS processing.ipynb, a Jupyter notebook for calibrating the DTS data using dts_calibration Python package. cooler_calibration.csv is the DTS calibration CSV used in the calibration sequence. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion. 6) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 7) SP Contains the SP data collected in field at the SP sites (provided as CSV files). 8) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). Note: Code files (.ipynb, .py, .xml) can be opened in any standard code editor, .exo file can be viewed using Paraview, .h5 files can be opened using HDFView software and h5py Python package, and .resipy file can be opened with the open-source ResIPy software.
This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in a forested catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, ground penetrating radar (GPR), continuous self-potential (SP) monitoring, electromagnetic (EM) imaging, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Contains two subfolders: Synthetic and Field_Application subfolder. Synthetic subfolder contains the ATS XML input script (can be opened using any code editor) for the four synthetic hydrological cases tested (Connected and gaining, Connected and losing, Disconnected and losing, and dry stream). It also includes other experimental cases to test the influence of precipitation and concentration gradient. For each synthetic case, the flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.mph can be opened with the commercial software COMSOL and requires a license) is executed using the ATS output data to simulate the potential field. It also includes the Synthetic_model_plot.ipynb (can be opened using any code editor) to visualize the SP result and generate manuscript figures. The data subfolder contains mesh files to run both the ATS (.exo and .stl files can be viewed using Paraview; .h5 files can be opened using HDFView software and h5py Python package) and COMSOL models. Field_Application subfolder contains two subfolders: ES_MDA_inversion and Final_Model. ES_MDA_inversion contains the Python script (.py can be opened using any code editor) and SP observation data used to run the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) inversion sequence to get the optimal model parameters. The Final_model subfolder contains the ATS XML input scripts, data files, output data for the two SP sites. The same workflow steps outlined for the Synthetic subfolder apply here. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) EM Contains the CSV file of the EM data from the DUALEM-42, including spatial coordinates (x, y, z), apparent conductivity, and in-phase measurements at 2 m coil separations for horizontal coplanar (HCP) and perpendicular (PRP) geometries. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion (.resipy can be opened with the open-source ResIPy software). 6) GPR Includes GPR field datasets collected at 100 MHz and 250 MHz antenna frequencies, along with the processing/interpretation project file (GPR_process.gpz can be viewed using EKKO_Project 6, a commercial software by Sensors & Software that requires a license). 7) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 8) SP Contains the SP data collected in field at the two SP sites (one in the perennial reach and the other in the intermittent reach), provided as DAT files. 9) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). It also includes DTW.ipynb, a Jupyter notebook containing the code for the dynamic time warping (DTW) with sliding window to evaluate SP signal synchronicity.
These data include scanning Doppler lidar measurements from the deployment of the PNNL HALO XR+ (s/n 184) at the A7 site. The measurements include calibrated beam azimuth angles, radial velocity, backscatter, signal to noise ratio per each line of sight, and range-gate. The data are provided in NetCDF.
The wider adoption of hydrogen in multiple sectors of the economy requires that safety and risk issues be rigorously investigated. Quantitative Risk Assessment (QRA) is an important tool for enabling safe deployment of hydrogen fueling stations and is increasingly embedded in the permitting process. QRA requires reliability data, and currently hydrogen QRA is limited by the lack of hydrogen specific reliability data, thereby hindering the development of necessary safety codes and standards [1]. Four tools have been identified that collect hydrogen system safety data: H2Tools Lessons Learned, Hydrogen Incidents and Accidents Database (HIAD), National Renewable Energy Lab's (NREL) Composite Data Products (CDPs), and the Center for Hydrogen Safety (CHS) Equipment and Component Failure Rate Data Submission Form. This work critically reviews and analyzes these tools for their quality and usability in QRA. It is determined that these tools lay a good foundation, however, the data collected by these tools needs improvement for use in QRA. Areas in which these tools can be improved are highlighted, and can be used to develop a path towards adequate reliability data collection for hydrogen systems.
The FORGE team is making these fracture models available to researchers wanting a set of natural fractures in the FORGE reservoir for use in their own modeling work. They have been used to predict stimulation distances during hydraulic stimulation at the open toe section of well 16A(78)-32. These fracture sets are fully stochastic and do not contain the deterministic set that matches the pilot well 58-32 FMI data. Well 58-32 has been completed and 16A(78)-32 is to be drilled as part of Phase 3. The original .fab files are not included due to redundancy. The *.fabgz data for the 800m and 1200m depth areas are in the native FracMan format and have been compressed using Gzip. Filtered data for the 800m depth area includes .csv spreadsheets, native FracMan (.fab), and GOCAD (.ts) files that are in a compressed zip format. The file titled "SGW 2020 Finnila and Podgorney DFN fracture files on GDR.pdf" is a description of the data and should be reviewed prior to data use.
The AQT (Vaisala AQT530) instrument provides observations on meteorological conditions, including particulate matter (PM2.5, PM10), gas species concentrations (NO, NO2, O3, CO), and environment temperature and moisture. These measurements are critical for understanding air quality. These measurements are useful for understanding changes in aerosol properties, air quality research, and comparing to model experiments especially in urban environments. These measurements are collected at the Argonne Testbed for Multiscale Observational Science (ATMOS), a prairie field site at Argonne National Laboratory in Lemont, Illinois. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. Each file contains one day's worth of data (24 hours, starting at 0000 UTC). The data is aggregated into daily frequency to make it easier to process multiple days, and compress the higher-resolution fields. File naming convention includes the project (CROCUS), location (atmos), data level (raw, a1), date (year, month, day), and hour (0000).
Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at Chicago State University (CSU) at a prarie site on campus in Chicago, Illinois. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. The data is aggregated into daily frequency to make it easier to process multiple days, and compress the higher-resolution fields. Each file contains one day's worth of data (24 hours, starting at 0000 UTC). File naming convention includes the project (CROCUS), location (CSU), data level (raw, a1), date (year, month, day), and hour (0000).
Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at the Argonne Testbed for Multiscale Observational Science (ATMOS), a prairie field site at Argonne National Laboratory in Lemont, Illinois. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. The data is aggregated into daily frequency to make it easier to process multiple days, and compress the higher-resolution fields. Each file contains one day's worth of data (24 hours, starting at 0000 UTC). File naming convention includes the project (CROCUS), location (atmos), data level (raw, a1), date (year, month, day), and hour (0000).
Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at the University of Illinois in Chicago, Illinois, on the meteorological tower near the greenhouse on campus. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. File naming convention includes the project (CROCUS), location (UIC), data level (raw, a1), date (year, month, day), and hour (0000).
The AQT (Vaisala AQT530) instrument provides observations on meteorological conditions, including particulate matter (PM2.5, PM10), gas species concentrations (NO, NO2, O3, CO), and environment temperature and moisture. These measurements are critical for understanding air quality. These measurements are useful for understanding changes in aerosol properties, air quality research, and comparing to model experiments especially in urban environments. These measurements are collected at the University of Illinois in Chicago, Illinois, on the meteorological tower near the greenhouse on campus. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. Each file contains one day's worth of data (24 hours, starting at 0000 UTC). File naming convention includes the project (CROCUS), location (UIC), data level (raw, a1), date (year, month, day), and hour (0000).
The objective of this DOE-funded project by the Electric Power Research Institute, Inc. (EPRI), Hamilton Maurer International (HMI) and Sotacarbo S.p.A. (Sotacarbo), is to qualify coal, biomass, and plastic waste blends based on performance testing of selected pellet recipes in a pilot-scale updraft moving-bed gasifier. The testing will provide relevant data to advance the commercial-scale design of the moving-bed gasifier to be able to successfully use these feedstocks to produce hydrogen. In particular, the effects of the waste plastics on feedstock development (i.e., blending and pelletizing) and the resulting products (i.e., syngas compositions, organic condensate production, and ash characteristics) are a focus. The gasifier is Hamilton Mauer International, Inc. (HMI)’s moving-bed gasifier, which has been proven capable of gasifying nearly all coal ranks. It has also shown the ability in prior testing work to gasify wood chips. However, mixtures of these fuels with plastic wastes have not been prepared and gasified together. The feedstocks will be prepared by California Pellet Mill (CPM) under contract to HMI. The technical tasks and current status for this two-year research project are: Feed Procurement and Preparation: Nine different feedstocks were prepared from varying compositions of PRB coal, corn stover biomass, and car fluff waste plastics. Fuel pellets were produced by California Pellet Mill and shipped to Sotacarbo’s test facility in Italy. Test Plan Development: A test plan was created to define the test runs to be performed. The test plan detailed the different tests that were run, instrumentation used, extractive samples taken, and relevant figures of merit. Gasifier Testing: Tests are currently being performed in the pilot-scale gasifier at Sotacarbo using nine different fuel feedstocks generated from varying mixtures of coal, biomass, and plastic wastes. The testing will provide information on gasification reaction efficiency/performance, yielding relevant data for models used to scale up the gasifier design. This task will also include work to reassemble the gasifier at Sotacarbo and perform a baseline 100% coal run. Data Analysis and Reporting: Review of the data, determination of figures of merit, and interpretation of the results to be reported. The results will be used to specify the range of feedstock blends that can be successfully gasified as well as quantify gasifier outputs based on specific blends. This paper will be summarize the pelletizing procedure that insures the viability of the tri-fuel pellets for the gasification runs that are being performed at Sotacarbo’s 30mm up draft moving bed gasifier. Initial gasification tests have been conducted, and all the lab scale tri-fuel pellet gasification runs will be completed by the fall of 2022. Performance data will enable modeling of a full-scale HMI industrial scale gasifier supporting both CHP and Hydrogen production.
The objective of this effort, primarily funded by the United States Department of Energy (DOE), and led by the Electric Power Research Institute, Inc. (EPRI), with support by Hamilton Maurer International (HMI) and Sotacarbo S.p.A. (Sotacarbo), has been to qualify coal, biomass, and plastic waste blends based on performance testing of selected fuel pellet compositions in a pilot-scale updraft moving-bed (UDMB) gasifier. The testing provided relevant data to advance the commercial-scale design of the moving-bed gasifier to be able to successfully use these feedstocks to produce hydrogen. In particular, the effects of waste plastics on feedstock development (i.e., blending and pelletizing) and the resulting products (i.e., syngas compositions, organic condensate production, and ash characteristics) are the focus. The gasifier used for testing is HMI’s moving-bed gasifier, which has been proven capable of gasifying nearly all coal ranks. It has also shown the ability in prior testing work to gasify wood chips (biomass). However, mixtures of these fuels with plastic wastes have not been prepared and gasified together. The three feedstocks were densified and pelletized by California Pellet Mill (CPM) to meet the feedstock size required by Sotacarbo’s 30mm ID UDMB gasifier, under contract to HMI. The technical tasks and results from this two-year research project included: (1) Feed Procurement and Preparation: Nine different tri-fuel pellets were prepared from varying compositions of fresh mined PRB coal, corn stover biomass, and car fluff waste plastics. Tri-fuel pellets were produced by CPM and shipped to Sotacarbo’s test facility in Carbonia, Sardinia, Italy. (2) Test Plan Development: A test plan was created to define the test runs to be performed. The test plan detailed the different UDMB gasification tests to be performed in Sotacarbo’s 12-inch ID pilot scale gasifier, the process monitoring instrumentation used, and the extractive samples recovered for analysis of the total gasification process mass and energy balance. (3) Gasifier Testing: Nine different gasification runs were performed in the pilot-scale gasifier at Sotacarbo using nine different fuel feedstock compositions generated from varying mixtures of PRB coal, biomass, and plastic wastes. The testing generated performance data on gasification reaction efficiency and performance, yielding relevant data for models used to scale up the gasifier design. This task also included work to refurbish and reassemble the pilot gasifier at Sotacarbo and perform a baseline 100% PRB coal run. (4) Data Analysis and Reporting: Review of the data, determination of figures of merit, and interpretation of the results are reported in the project’s final report, published in March 2024. The results show that all tri-fuel pellets gasified well and maintained structural integrity throughout the gasification process. The syngas generated can be shifted to hydrogen by using commercial syngas shifting technologies. (5) High Fidelity computational fluid dynamics (CFD) Simulation: The National Energy Technology Laboratory (NETL) team performed CFD simulations of the UDMB gasifier for two of the tri-fuel pellets gasified in Sotacarbo’s pilot scale gasifier. The kinetic mechanisms for the pyrolysis of each constituent, PRB coal, corn stover biomass, and waste plastics are based on thermogravimetric analysis performed by Sotacarbo. The gasification model was validated by comparing the predicted syngas composition at the exit of the gasifier with the measured syngas composition. In addition, the reactor’s measured internal temperature profile agreed well with the predicted internal reactor temperature profile. These results validate that the model can be used to predict the performance of the updraft moving bed gasifier for different feedstocks and operating conditions. This paper summarizes the results of the completed work in which the pelletizing procedure was validated to ensure the viability of the tri-fuel pellets for the gasification runs performed at Sotacarbo’s 30 mm UDMB gasifier. The gasification performance data from this series of nine runs will enable modeling of a full-scale HMI industrial scale gasifier supporting both combined heat and power, and Hydrogen production from coal (both fresh mined and legacy) combined with various biomass and waste plastics. Additionally, plans and progress on a follow-up project, being executed by the same project team, will be presented. In this project, a total of twenty (20) different feedstocks are being prepared from varying compositions of biomass (both woody biomass and corn stover) with a mixture of legacy coal waste, plastic waste, and refuse-derived fuel (RDF). The testing will provide information on gasification reaction efficiency/performance, yielding relevant data for models used to scale up the gasifier design to 50 megawatt electric (MWe) (equivalent hydrogen production). Tests will also be performed on a bench-scale fluidized-bed gasifier for comparison purposes. The results of this testing will be used to specify the range of feedstock blends that can be successfully gasified as well as quantify gasifier outputs based on specific blends.
The objective of this DOE-funded project by the Electric Power Research Institute, Inc. (EPRI), Hamilton Maurer International (HMI) and Sotacarbo S.p.A. (Sotacarbo), is to qualify coal, biomass, and plastic waste blends based on performance testing of selected pellet recipes in a pilot-scale updraft moving-bed gasifier. The testing provides relevant data to advance the commercial-scale design of the moving-bed gasifier to be able to successfully use these feedstocks to produce hydrogen. In particular, the effects of the waste plastics on feedstock development (i.e., blending and pelletizing) and the resulting products (i.e., syngas compositions, organic condensate production, and ash characteristics) are a focus. The gasifier is Hamilton Mauer International, Inc. (HMI)’s moving-bed gasifier, which has been proven capable of gasifying nearly all coal ranks. It has also shown the ability in prior testing work to gasify wood chips. However, mixtures of these fuels with plastic wastes had not yet been prepared and gasified together. The feedstocks were prepared by California Pellet Mill (CPM) under contract to HMI. The technical tasks for this research project included: • Feed Procurement and Preparation: Nine different feedstocks were prepared from varying compositions of PRB coal, corn stover biomass, and car fluff waste plastics. Fuel pellets were produced by California Pellet Mill (CPM) and shipped to Sotacarbo’s test facility in Italy. • Test Plan Development: A test plan was created to define the test runs to be performed. The test plan detailed the different tests that were run, instrumentation used, extractive samples taken, and relevant figures of merit. • Gasifier Testing: Tests were performed in the pilot-scale gasifier at Sotacarbo using nine different fuel feedstocks generated from varying mixtures of coal, biomass, and plastic wastes. The testing provides information on gasification reaction efficiency/performance, yielding relevant data for models used to scale up the gasifier design. This task will also included work to reassemble the gasifier at Sotacarbo and perform a baseline 100% coal run. • Data Analysis and Reporting: Review of the data, determination of figures of merit, and interpretation of the results was reported. The results can be used to specify the range of feedstock blends that can be successfully gasified as well as quantify gasifier outputs based on specific blends. The tri-fuel pelletizing conducted at CPM and gasification testing results from Sotacarbo’s 30mm up draft moving bed gasifier are significant. Providing data for an established gasifier to help accelerate its updated design to be able to accommodate feedstocks composed of coal, biomass, and plastic waste. This should ultimately lead to development and commercialization of a lower cost, white hydrogen generation system.