AndreaBozzo/ceres-open-data-index huggingface dataset:
The Carbon Storage Technical Viability Approach (CS TVA)

 The Carbon Storage Technical Viability Approach (CS TVA) StoryMap provides an in-depth overview of the products created during the CS TVA research effort. In detail, the StoryMap addresses the CS TVA Matrix, Database Version 2.0, Database Catalog, Workflow, and Data Availability Result Database, discussing how each was developed and implemented. Information on how the matrix, database, and database catalog are interconnected, and their usage is also explained. The workflow section provides information on the CS TVA product development from the data-gathering stage to the final data availability results. A section on an expansion of the CS TVA workflow that utilizes Natural Language processing (NLP) section was included. Finally, the Data Availability Results Database is discussed. These results provide data science-informed insights into potential data gaps when assessing the viability of carbon storage in a given area or region. 

Tags: CS TVA, Carbon Storage, Data Availability, Data Science, EDX4CCS, United States
PARETO  UI 1.0.0 Release

PARETO is an open-source Python-based software package for oilfield produced water management and beneficiary reuse optimization.
PARETO supports produced water industry by providing cost-effective water management solutions.
This version introduced a new user interface (UI) which makes it easier to navigate for industry users.
This User Interface manages PARETO optimization engine (codebase version) so that no code is visible to the end users.

New Features :
- Updated user Dashboard with better summary of KPI
- Improved work processes based on the user feedback
- Added latest input tabs from PARETO codebase
- expanded optimization settings
- upgraded to Pandas v2
- license and copyright added
- Improved numerical rounding in results overview
- Migrated utility function from UI repo to PARETO repo
- Added support function through UI

Tags: Oilfield water, PARETO, PARETO-UI, PSE, Process Systems Engineering, Produced water
US Electric System Operating Data Bulk Data v2024-03-12

This dataset is an archive of the U.S. Electric System Operating Data (EBA) bulk data from EIA.gov (http://api.eia.gov/bulk/EBA.zip) retrieved on March 12, 2024, which includes data from 2015-07-01 through 2024-03-12 and is used in the Python package, ElectricityLCI (https://github.com/USEPA/ElectricityLCI), for modeling the 2016 and 2018 electricity baselines. Data prior to 2019 are no longer hosted on EIA.gov but may be found in the National Archives under the 'Hourly and Daily Balancing Authority Operations Report Files' (https://catalog.archives.gov/id/290085174). This ZIP file is formatted to work with ElectricityLCI. The ZIP file should be copied to the user's local data store (returned by the `get_datastore_dir` method in globals.py) under the subfolder, 'bulk_data'. The model configuration file should have the following two settings in order to read data from the ZIP file: 'use_eia_bulk_zip: true' and 'bypass_bulk_vintage: true'. Source: U.S. Energy Information Administration (March 2024).

Tags: LCA, data inventory, electricity
WaterTAP 0.9 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems.

An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

Tags: IDAES, Process Systems Engineering, Technoeconomic Assessment, Water Treatment, WaterTAP
FECM/NETL Offshore CO2 Saline Storage Cost Model

The FECM/NETL Offshore CO2 Saline Storage Cost Model (CO2_S_COM_Offshore) estimates costs for a CO2 storage project in an offshore saline formation or reservoir. It is applicable for storage projects located on the Outer Continental Shelf of the Gulf of America. The purpose is to model the costs associated with a project, using simplified geo-engineering equations to calculate reservoir values needed to determine costs (such as CO2 plume area and number of injection wells). To use the model, change any of the inputs, which are always in orange cells, to the values you desire. Although there are numerous values that can be changed, the values expected to be of most interest have input cells on the 'Key_Inputs' sheet.

Last update: 5/2/2025; Version 1.1 corrects bug in reservoir thickness calculations. 

Tags: Carbon storage, Saline, cost model, offshore, technoeconomic
Basin-Scale Structural Features Database

The Basin-Scale Structural Features database provides spatial datasets of faults, fractures, folds, and earthquakes compiled from public, authoritative sources (e.g., U.S. Geological Survey and State Geological Surveys) and aggregated into derivative forms to support subsurface assessments. Recognizing that characterizing basin-scale structural features requires interpreting data that are often ambiguous or lack key information, the source data were evaluated using a knowledge-data framework and geospatial fuzzy logic method (Justman et al., 2020) to represent both measured (observed) and predicted (inferred or potential) structural features as derivative datasets. This workflow employs conceptual models for known structural features and predicted structural features, incorporating geospatial data to estimate potential, even with limited data. The aim is to aid and support an understanding of basin-scale features and identify potential gaps in data and knowledge.

As of 4/30/2025, the database includes resources for nine sedimentary basins: Appalachian, Denver, U.S. Gulf Coast, Illinois, Michigan, Permian, Sacramento, San Joquin and Williston. The database is organized by basin and then data category: 1) Faults, fractures, folds, 2) Earthquakes, 3) Topographic, 4) Structural contours and isopachs, 5) Geophysical, and 6) Structural feature density assessment maps.


Tags: basin scale, earthquakes, faults, folds, fractures, structural complexity, structural features
WaterTAP 1.1 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems.

An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

Tags: Desalination, IDAES, Process Systems Engineering, Technoeconomic Assessment, Water Treatment, WaterTAP, idaes-pse
Carbon Storage Technical Viability Approach (CS TVA) Matrix

The Carbon Storage Technical Viability Approach (CS TVA) Matrix is a knowledge framework developed to outline the information needed for geologic carbon storage. The CS TVA Matrix contains 5 categories, 14 sub-categories, and 47 components. This framework can be leveraged to assess the availability of data and information needed for a carbon storage project. The information categories of the matrix are tied to a list of required data using weighted mapping, published herein. 

Tags: CS TVA, EDX4CCS, United States, carbon storage, community metrics, data science, geologic carbon storage, infrastructure, subsurface
IDAES-PSE 2.7.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.

IDAES-PSE 2.7.0 Release Highlights

New features:

AutoScaler and CustomScalerBase classes: Such tools are the core of the new scaling framework being implemented in IDAES. Wider adoption of scaling tools among users will result in quicker and more robust model solutions.

Scaler for equilibrium reactor and saponification properties: These scaler models are examples to follow for how to use the new scaling tools.

ONNX Surrogate support from Optimization & Machine Learning Toolkit (OMLT): ONNX is an open standard format to save and load ML/AI models that is widely supported by all major frameworks. This capability makes it easier for IDAES users to create surrogate models and use them without having to support each framework individually.

1D Membrane Model for CO2 Capture and Utilization: Supports ongoing efforts for modeling and optimizing polymer membrane processes for CO2 capture and conversion into formic acid. 

StreamScaler unit model: Unrelated to the CustomScalerBase, this unit model allows a stream’s extensive variables to be scaled by a fixed factor. This allows streams being processed by multiple units in parallel to be scaled down to unit scale and scaled back up to process scale.

Bug fixes or improvements:

Scaling, EoS, Diagnostics tool, Modular Properties, tests & documentation

Deprecations:

Old Cubic EoS

Tags: IDAES, Process Systems Engineering, idaes-examples, idaes-pse
IDAES-PSE 2.8.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.

Tags: IDAES, Process Systems Engineering, idaes-examples, idaes-pse
IDAES-PSE 2.5.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.

IDAES-PSE 2.5.0 Release Highlights

Upcoming Changes

IDAES will be switching to the new Pyomo solver interface in the next release. Whilst this will hopefully be a smooth transition for most users, there are a few important changes to be aware of.
The new solver interface uses a different version of the IPOPT writer (“ipopt_v2”) and thus any custom configuration options you might have set for IPOPT will not carry over and will need to be reset.
By default, the new Pyomo linear presolver will be activated with ipopt_v2. Whilst are working to identify any bugs in the presolver, it is possible that some edge cases will remain.
IDAES will begin deploying a new set of scaling tools and APIs over the next few releases that make use of the new solver writers.
The old scaling tools and APIs will remain for backward compatibility but will begin to be deprecated.

New Models, Tools and Features

New diagnostics check for near-parallel variables and constraints.
New diagnostics tools for identifying causes of infeasibility in models.
New example for creating a custom model of a liquid-liquid extractor unit operation.

Bug Fixes

Fixed bug in Gibbs reactor that caused it to appear to have additional spurious degrees of freedom.
Fixed bug in the Modular Property Framework that would cause errors when trying to use phase-based material balances with phase equilibria.
Fixed bug in Modular Properties Framework that caused errors when initializing models with non-vapor-liquid phase equilibria.

Testing and Robustness

Deployed the IDAES Diagnostics Toolbox to confirm that there are no structural or numerical issues in the core model libraries.
Additional robustness tests for core model, and some associated improvements in the converge tester class.
Fixed a number of issues that were causing unexpected warnings to be emitted during testing.

Deprecations and Removals

Removed examples for RIPE tool which has not been supported for a number of releases.

Tags: IDAES, PSE, Process Systems Engineering, idaes-examples, idaes-pse, idaes-ui
WaterTAP 1.2 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems.

An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

Tags: Desalination, IDAES, Process Systems Engineering, Technoeconomic Assessment, Water Treatment, WaterTAP, idaes-pse
Electricity Baseline 2022

The Electricity Baseline (2022) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2022" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569193. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).

Tags: Electricity, LCA, data inventory
NEWTS Economic Data Dashboard: Critical Materials for Energy

The NEWTS Economic Data Dashboard: Critical Materials for Energy is an economic screening tool for assessing the concentration and potential value of the 18 critical materials for energy in fossil energy-related wastewater across the United States. Datasets used to develop the dashboard and complete economic calculations are available as supplementary downloads. These resources were developed primarily using geochemical composition and volume data from the NEWTS Integrated dataset (version 1.0). Energy-related wastewater types presented in the dashboard include produced water (PW), brackish groundwater (BW), acid mine drainage (AMD), coal combustion residual leachate (CCRL), power plant flue gas desulfurization wastewater (FGD), and geothermal fluids. The concentrations of the following critical minerals were included in the analysis, when available: Al, Co, Cu, Dy, F, Ga, Ge, C, Ir, Li, Mg, Mn, Nd, Ni, Pt, Pr, Si, Tb. This economic screening tool was built to support identification of promising critical mineral feedstocks and economic research targets. 

Tags: Critical Minerals, Critical Minerals and Materials, Geochemistry, Produced water, acid mine drainage, dashboard, economic geology, fossil energy, wastewater
Electricity Baseline 2020

The Electricity Baseline (2020) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2020" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569605. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).

Tags: Electricity, LCA, LCI, data inventory
Electricity Baseline 2020 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2020 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data.

ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store).

This submission includes the background data used to generate the 2020 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files.

To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'.

The log file generated by the 2020 model run is also included, which contains the statements at the DEBUG level and above.

Tags: Electricity, LCA, LCI, Life Cycle, data inventory
Electricity Baseline 2021

The Electricity Baseline (2021) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2021" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569576. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).

Tags: Electricity, LCA, LCI, Life Cycle
Electricity Baseline 2021 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2021 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data.

ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store).

This submission includes the background data used to generate the 2021 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files.

To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'.

The log file generated by the 2021 model run is also included, which contains the statements at the DEBUG level and above.

Tags: Electricity, LCA, LCI, Life Cycle, data inventory
WaterTAP 1.0 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems.

An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

Tags: Desalination, IDAES, Process Systems Engineering, Technoeconomic Assessment, Water Treatment, WaterTAP, idaes-pse
useeio-infra-app

This GitHub repository hosts the source code to an RShiny web application that implements the U.S. Environmentally-Extended Input-Output (USEEIO) model in order to conduct a screening-level life cycle analysis. The USEEIO model is an open-source model developed and maintained by the Environmental Protection Agency (EPA), along with colleagues and contractors, and is available on GitHub (https://github.com/USEPA/USEEIO). The USEEIO model incorporates environmental data into pre-existing Economic Input-Output Models, which look at the interdependence of different economic industries. In this web application, the focus is on energy infrastructure applications; however, it can be used to implement USEEIO in any area for which this model is applicable.

Tags: Infrastructure, LCA, Life Cycle, web application
PARETO UI 1.1.0 Release

PARETO is an open-source Python-based software package for oilfield produced water management and beneficiary reuse optimization.
PARETO supports produced water industry by providing cost-effective water management solutions.
This version introduced an updated User Interface (UI) which makes it easier to navigate and understand the solution for industry users.

New Features:
- Map files are added for visualization
- Added output export function button
- Water residual view added
- Workflow was streamlined
- File extension was expanded
- Minor bugfix

Tags: PARETO, PARETO-UI, PSE, Process Systems Engineering, Produced water, oilfield water