To ensure robust model validation, the following best practices should be followed:
Use Multi-Level Validation: Validate models at different levels, such as molecular, reactor, and process scales, to ensure consistency across scales. Cross-Validation: Use different subsets of experimental data for training and validation to avoid overfitting. Independent Data Sets: Validate the model using independent data sets not used in model development to ensure generalizability. Collaborative Efforts: Collaborate with experimentalists to obtain high-quality data and validate models under realistic conditions. Continuous Updating: Continuously update the model with new data and insights to improve its accuracy and reliability.
