Data governanceFormulation, process, batch, performance and unit standardization
AI Process Optimization
Formulation, Process, and Performance Prediction
Based on machine learning, multi-target optimization and intelligent experimental design, integration of formulation composition, process parameters, performance indicators and experimental feedback, rapid identification of key factors and recommendation of the best and verifiable solutions.
01
Solution Overview
Optimization of materials formulations, response conditions, preparation processes, product performance and manufacturing processes, and organization of historical experiments, computational simulations, production records and representation into modelable data assets. Next round of pilot proposals to reduce error of experience and increase the efficiency of research and development over time, through feature engineering, performance forecasting, identification of key factors and multi-target optimization.
Performance projectionsEstablish regression/classification models to predict target performance ranges
Key factor identificationContribution, sensitivity and binding effects of output variables
Multi-purpose optimizationBalance performance, cost, stability and availability
02
Applications
- Optimization of formulas such as high molecules, coatings, catalysts, battery materials, etc.
- Process windows for temperature, pressure, time, concentration, solvent etc.
- Performance, stability, cost, productivity and safety multi-targeted balance
- Small sample historical experiments need to be converted to reusable AI data asset
- Shorten the R & D cycle by recommending the next set of experiments based on the model
03
Overall process
Documentation and process parameters
Process characteristics
Pareto Sort
Checklist
Credibility analysis
Model Update
04
Technical modules
- Multi-source data integration:Harmonization of experimental, representational, computational and production data and establishment of a retroactive data structure.
- Small sample modelling:Increased prediction stability under limited data in combination with random forest, XGBoost, Gaussian Profession.
- Explanatory analysis:Identification of key factors using SHAP, sensitivity analysis and ranking of variable contributions.
- Multi-purpose decision-making:Pareto Best List of Output Performance, Cost, Stability and Availability.
- Closed anecdotal:Feedback of validation results back into the model and continuous updating of the pilot recommendations.
05
Project value
Improving R & D efficiencyReduce repeat error and prioritize high-value combinations
Enhancing the basis for decision-makingTranslating empirical judgement into interpretable data conclusions
Sediment data assetsDevelop reusable formulations, processes and performance databases
Support for continuous optimizationThe model proposal is updated with the results of new experiments.
AI / Simulation
Characterization / Supplies