Carbon Dioxide Removal

AI/ML Approaches for Research and Development Application

Approaches
Use machine learning approaches based on known labels/expectations, usually for
  • Classification (e.g., Logistic, Support Vector Machine)
  • Regression (e.g., Linear, Kernel Ridge)
  • Leverage data to better understand and predict conditions, limitation and process requirements of materials for lower-partial-pressure DAC systems.
  • Predict the optimum sorbent, solvent or other material and air contractor design.
  • Predict potential impacts of precipitation, air pollutants, etc.
  • Forecast material life and wear
  • Setting acceptable operating ranges for each system and under which conditions.
  • Predict system needs for sites and overall best sites.
  • Digitize and characterize legacy data for NLP models using OCR.
  • Predict the impacts of various feedstock mixes/biomass types on pollution control equipment.
  • Predict chemical, biological and geological system impacts over time.
  • Structured numeric data, text, image, maps, sequences, time-series data
  • Predictive and prescriptive DAC methods for operation and management
  • Potential impacts of weather.
  • Best site locations
  • Predict geological, chemical and biological systems over time.
  • Real-time prediction and monitoring
  • Site management and planning
Approaches
Use machine learning approaches without known labels/expectations, usually for
  • Clustering
  • Dimension reduction
  • Select the most promising materials for testing.
  • Select key factors in minerals.
  • Identify the most valuable data
  • Determine site-specific co-benefits at potential deployment locations.
  • Help locate minerals of interest.
  • Structured or unstructured data
  • Identify key factors to optimize critical properties at scale for local conditions.
  • Identify the most valuable new types of data that should be tracked.
  • Locate minerals of interest.
Approaches
Use complex neural network architecture for
  • Image and spatial classification
  • Time series regression
  • Natural language processing
  • Adjust the technology to variable boundary conditions.
  • Accelerate wholistic simulations for different priority rankings, summarize new outcomes and assist in weighing the impacts.
  • Detailed model to generate rapid edge processing to predict DAC load following at the local level.
  • Predict impacts of weather variations on DAC systems based on existing trends.
  • Suggest strategies to increase the rate/capacity of carbon uptake by the mineral types present.
  • Scan data sources to identify patterns to explore the complex relationships for Ocean CDR
  • Image based advanced quantification and detection.
  • Structured numeric data, text, image, maps, sequences, time-series data
  • Predict impacts of weather variations
  • Predictive and prescriptive operation optimization
  • Develop strategies to increase rate/capacity of carbon uptake
  • Image based quantification and detection
  • Multi-scale and resolution prediction
Approaches
Combine more than one machine learning models to handle the tasks
  • Random Forest
  • XGBoost
  • Committee Machine
  • Identify the predictable effects and setting acceptable range of operating parameters to optimize overall operations.
  • Determine most cost effective technology for a specific site to include multiple considerations (geological, capital, prediction, land, water, social)
  • Prioritize resources and methods to clarify the impacts of temperature, rainfall, humidity, plant-mineral interactions and similar.
  • Predict and quantify social and economic impacts & spatio-temporal questions.
  • Multi-dimension scale predictions.
  • Structured numeric data, text, image, maps, sequences, time-series data
  • Predictive and prescriptive operation optimization
  • Determine cost effective technology
  • Predictive decision support
Approaches
Integrate scientific equations and knowledge into machine learning algorithms (usually using Deep Learning), such as Physics-Informed Machine Learning
  • Aid in scaling DAC material technology that performs well in the lab to variable conditions in the real world.
  • Create digital twin to mimic testing facilities’ environment to enable virtual/hybrid experiments.
  • Improve designs for gas-liquid contactors, electrode materials and membrane contactors for large-scale applications.
  • Evaluate and predict complex in situ mineral stability in the presence of water & natural geochemical processes.
  • Structured numeric data, text, image, maps, sequences, time-series data
  • Scientific equations, field equations, partial differential equations
  • Predictive and prescriptive operation optimization
  • Multi-scale and resolution prediction
  • Predictive decision support