AI Flow Chemistry Parameter Optimization for Slug Experiments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for flow chemistry, particularly in slugs, lack automation and optimization, relying on manual work and requiring extensive experimentation to determine optimal parameter sets, which hampers efficiency and precision in chemical manufacturing.
Innovation Solution
A computer-implemented method using machine learning and AI to determine a target parameter set for flow chemistry setups in slugs, involving sensor data analysis, machine learning model training, and optimization algorithms to automate the process, allowing for self-optimization and reduced manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual experimentation and data analysis are used for flow chemistry parameter optimization, then flexibility and adaptability are maintained, but productivity and time consumption are significantly reduced
Solution Approach 1:
The system performs self-optimization by automatically analyzing sensor data from slug flow experiments, training machine learning models, and determining target parameter sets without requiring manual chemist intervention for each experimental cycle
Solution Approach 2:
Manual chemist analysis and decision-making is replaced with automated machine learning algorithms that process sensor data and generate optimization recommendations, transforming a manual intellectual process into an automated computational system
2Manufacturing precision
If extensive experimentation is conducted to screen parameter space, then manufacturing precision and reliability are improved, but loss of time and productivity deteriorate
Solution Approach 1:
The system performs preliminary experiments to collect sensor data that is used to train machine learning models, which then predict optimal parameter sets, eliminating the need for extensive sequential experimentation
Solution Approach 2:
Sensor data from slug flow experiments is fed back into the machine learning model to continuously improve parameter predictions, creating a closed-loop system that rapidly converges on optimal parameters
3Productivity
If flow chemistry is conducted in continuous stream, then productivity is improved, but reliability deteriorates due to clogging and fouling from direct contact between reaction media and channel walls
Solution Approach 1:
The continuous flow is segmented into discrete slugs separated by inert gas bubbles or immiscible liquids, preventing direct contact between reaction media and channel walls and eliminating clogging and fouling issues
Solution Approach 2:
An inert separator phase is introduced as an intermediary between the reaction media and channel walls, preventing harmful interactions while maintaining continuous flow conditions
Data Source
AI summary
A computer implemented method for determining at least one target parameter set for a flow chemistry setup (110) for flow chemistry in slugs is disclosed. The method is a self-learning method. The method comprises the following steps: a) determining at least one process variable by using at least one sensor (122) of a flow chemistry setup (110); b) training of at least one machine-learning model (126) based on the process variable; c) determining the target parameter set by applying an optimizing algorithm in terms of at least one optimization target on the trained machine-learning model (126); d) providing the determined target parameter set and/or considering the determined target parameter set for evaluating a flow chemistry setup (110) and/or for evaluating at least one flow chemistry product.