Robust optimization of microalgae cultivation using a dual-uncertainty quantified random forest-based EGO framework

Key Points

• The aim is to optimize microalgae cultivation for stable biomass yields amidst industrial variability.
• Utilized a dual-uncertainty quantified random forest framework.
• Applied robust optimization techniques.
• Addressed noise factors present in industrial settings.
• Achieved stable and reproducible biomass yields.
• Validated the optimization framework under various uncertainties.