PFAS Modeling via Machine Learning Source Identification
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Solution Overview
Problem
Current methods for understanding and modeling per and polyfluoroalkyl substances (PFAS) compounds in the environment are limited, as they fail to account for the complex behavior of thousands of PFAS compounds, relying on a small subset for predictive modeling and transformation interactions, leading to inaccurate identification of source products and remediation challenges.
Innovation Solution
A computer-aided simulation and machine learning model is developed to create a training dataset for PFAS compounds, allowing for the generation of PFAS patterns and concentration predictions, identifying likely products causing environmental concentrations by considering hundreds to thousands of PFAS compounds and their transformation pathways.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic methods rely on approximately 10 to 20 PFAS compounds for understanding chemical patterns, then the complexity of analysis is reduced, but the accuracy and comprehensiveness of PFAS source identification deteriorates
Solution Approach 1:
The machine learning model is trained on comprehensive environmental data from multiple sources (water, soil, air, biological samples) and thousands of PFAS compounds, enabling it to universally identify sources across diverse environments and compound types. The model handles various data formats and environmental conditions through a unified analytical framework, replacing multiple separate analysis methods with a single multi-functional system.
Solution Approach 2:
The system transforms the analysis from tracking individual compound concentrations to analyzing patterns across thousands of PFAS compounds simultaneously. By changing the parameter from discrete compound monitoring to comprehensive pattern recognition across the entire PFAS chemical space, the system achieves both reduced complexity and improved accuracy.
2Ease of operation
If predictive modeling relies on only 8 to 12 PFAS compounds, then the modeling process becomes manageable, but the ability to account for transformation interactions and transformation pathways is lost
Solution Approach 1:
The system segments the environmental matrix into multiple distinct compartments (water, soil, air, biological tissues) and models PFAS behavior in each separately. This segmentation allows the complex system to be managed through modular analysis while capturing transformation interactions across all compartments, as each segment can be independently characterized and then integrated.
Solution Approach 2:
The machine learning model acts as an intermediary that processes environmental data and translates it into source identification results. The model mediates between the complex transformation interactions of thousands of PFAS compounds and the user's need for manageable analysis, automatically handling the complexity while providing interpretable outputs.
3Device complexity
If basic understanding methods are used for PFAS transport, then the simplicity of the approach is maintained, but the ability to predict future concentrations and account for attenuation over time deteriorates
Solution Approach 1:
The system performs preliminary action by training the machine learning model on historical environmental data and simulated release scenarios before actual source identification is needed. This pre-training establishes the model's predictive capabilities, allowing it to accurately forecast future concentrations and account for attenuation processes without requiring complex real-time calculations during actual investigations.
Data Source
AI summary
An exemplary method includes creating a training dataset of per and polyfluoroalkyl substances (PFAS) compound environmental release over time using a computer aided simulation of environmental release of a set of PFAS containing products and training a machine learning model using the training dataset. The method further includes receiving, at the trained machine learning model, environmental data relating to an environment and PFAS concentration data representing PFAS compound concentrations within the environment and generating, using the trained machine learning model, identification of one or more PFAS containing products likely to have caused the PFAS compound concentrations within the environment.