Summary of Integrating Knowledge-guided Symbolic Regression and Model-based Design Of Experiments to Automate Process Flow Diagram Development, by Alexander W. Rogers et al.
Integrating knowledge-guided symbolic regression and model-based design of experiments to automate process flow diagram development
by Alexander W. Rogers, Amanda Lane, Cesar Mendoza, Simon Watson, Adam Kowalski, Philip Martin, Dongda Zhang
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed digital framework combines symbolic regression (SR) with model-based design of experiments (MBDoE) to accelerate process flow diagram (PFD) optimisation and knowledge discovery in the formulated product market. The framework integrates SR’s ability to propose Pareto fronts of interpretable mechanistic expressions with MBDoE’s experiment design capabilities, allowing for efficient PFD optimisation while balancing process understanding. To evaluate the framework’s performance, a new process model was constructed to generate in-silico data for different case studies, demonstrating its potential for digital manufacturing and product innovation in the chemical industry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to quickly improve the production process of formulated products, like cosmetics or cleaning supplies. This method uses computers to analyze data and find the best combination of ingredients and processing steps to make a product with specific properties. The team tested their approach using computer-generated data and showed that it can be very effective in finding the right recipe. |
Keywords
» Artificial intelligence » Regression
