Improving Assumptions One approach to mitigating model limitations is to refine the assumptions and approximations used. This can involve incorporating more detailed descriptions of physical and chemical phenomena or using more sophisticated computational methods.
Multi-Scale Modeling Multi-scale modeling, which involves integrating models at different scales (e.g., molecular, mesoscale, and macroscopic), can help bridge the gap between laboratory and industrial scales, providing more accurate predictions across different conditions.
Parameter Sensitivity Analysis Conducting a sensitivity analysis can help identify which parameters have the most significant impact on model predictions. This can guide efforts to improve the accuracy of those parameters through targeted experiments or more detailed modeling.
Validation and Calibration Validation against experimental data is crucial for assessing the accuracy of catalytic models. Calibration, which involves adjusting model parameters to better fit experimental results, can also improve model reliability.
