Biodegradability Modeling for Faster Formulation Assessment
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Solution Overview
Problem
Current methods for determining biodegradability of formulations are time-consuming and resource-intensive, limiting the development process and requiring significant investment in testing and certification, while non-degradable waste contributes to environmental contamination.
Innovation Solution
A computer-implemented method using a data-driven biodegradation model that is trained for specific habitats, allowing for rapid and accurate determination of biodegradability by providing a digital representation of the formulation and habitat descriptors, reducing the need for extensive training data and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional test methods are used to determine biodegradability, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the formulation and tests this copy in silico rather than physically testing the actual formulation. The digital representation includes molecular structure, composition, and physical-chemical properties that replicate the real formulation's biodegradation behavior, allowing rapid prediction without physical testing time delays
Solution Approach 2:
The patent replaces the physical/mechanical testing system with a computational model. Instead of conducting actual biodegradation experiments in labs (which take months to years), the system uses computer-based molecular dynamics simulations and machine learning models to predict biodegradability, substituting physical processes with digital calculations
2Measurement precision
If comprehensive testing is conducted to ensure accurate biodegradability determination, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models and pre-calculating biodegradation pathways for common chemical structures before actual assessment is needed. This allows rapid prediction of new formulations by leveraging pre-computed data and models, reducing the computational energy required for each individual assessment
Solution Approach 2:
The patent applies partial action by focusing computational resources only on the specific molecular structures and chemical components that are most relevant to biodegradability prediction, rather than performing comprehensive analysis of all possible properties. The model selectively processes only the necessary molecular features and environmental parameters
3Measurement precision
If a biodegradation model is trained with extensive data to improve accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by training the machine learning model specifically on local characteristics relevant to biodegradability, such as molecular functional groups, chemical bonds, and environmental conditions, rather than requiring comprehensive data across all possible formulation properties. This focused approach reduces the overall data requirements and model complexity while maintaining prediction accuracy for the specific task
Data Source
AI summary
The disclosure relates to a method for determining the biodegradability of a given formulation in a given biodegradation habitat.