Active Workflow Learning for Real-Time Laboratory Adjustment
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Solution Overview
Problem
Laboratory workflows, such as polymer synthesis, are time-consuming due to the delay in determining results until the workflow is completed, necessitating a method to improve real-time analysis and adjustment for desired outcomes.
Innovation Solution
An active workflow system that uses machine learning models to analyze samples in real-time, updating models and parameters to adjust workflow parameters dynamically, ensuring predicted outcomes converge with expected results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional workflow analysis is used, then workflow completion is achieved, but real-time optimization is not possible
Solution Approach 1:
The system continuously monitors workflow parameters and sample characteristics in real-time, comparing actual values against predicted values from the machine learning model. When deviations are detected, the system provides feedback to adjust workflow parameters dynamically, enabling real-time optimization rather than waiting for final results.
Solution Approach 2:
The machine learning model makes preliminary predictions about workflow outcomes based on current sample characteristics before the workflow completes. This allows the system to anticipate required adjustments and modify parameters proactively during execution, eliminating the need to wait for final results before optimization.
2Productivity
If real-time sample analysis is implemented, then workflow optimization speed improves, but system complexity increases
Solution Approach 1:
The machine learning model serves as an intermediary between raw sample data and workflow control decisions. It processes sample characteristics and translates them into predicted workflow outcomes and parameter recommendations, simplifying the complex relationship between monitoring and control functions.
Solution Approach 2:
The system uses a single machine learning model that performs multiple functions: predicting workflow outcomes, identifying optimal parameters, and guiding real-time adjustments. This multi-functional approach consolidates what would otherwise require separate systems into one integrated solution.
3Manufacturing precision
If dynamic parameter adjustment is made, then predicted results converge with expected results, but measurement and detection difficulty increases
Solution Approach 1:
The system adjusts workflow parameters dynamically based on real-time sample analysis, making partial corrections along the way rather than waiting for final results. This partial action approach allows continuous refinement of predictions to converge with expected outcomes, improving precision without requiring complete measurement of all parameters until the end.
Data Source
AI summary
A method for adjusting a workflow includes obtaining, at a current time step, one or more samples from the workflow. The method also includes determining a set of sample parameters based on obtaining the one or more samples. Each sample parameter of the set of sample parameters may be associated with a respective sample of the one or more samples. The method further includes generating an updated model by updating an initial model associated with the workflow based on determining the set of sample parameters. The method still further includes adjusting one or more workflow parameters based on the updated model. The one or more workflow parameters may be adjusted such that a currently predicted end result of the workflow converges with an expected end result of the workflow.