Optimization of spray drying process parameters for the food bioactive ingredients

Area of Science:

• Food Science and Technology
• Chemical Engineering
• Process Optimization

Background:

• Spray drying (SD) is a critical thermal process for producing powders and encapsulated materials.
• Quality degradation can occur during SD, impacting nutritional and sensory attributes.
• Optimization is crucial for maximizing product quality and economic viability.

Purpose of the Study:

• To review optimization techniques for spray drying (SD) processes.
• To highlight the role of mathematical modeling in predicting and enhancing product quality.
• To discuss the application of various optimization methods in SD.

Main Methods:

• Response Surface Methodology (RSM)
• Factorial design
• Taguchi methods
• Artificial Intelligence (AI)-based methods: Artificial Neural Networks (ANN), Genetic Algorithms (GA), Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System (ANFIS)
• Probabilistic methods: Monte Carlo simulation

Main Results:

• Optimization methods, particularly RSM, are widely applied to SD processes.
• Mathematical models coupled with optimization techniques can predict and improve product quality.
• AI-based and probabilistic methods offer advanced approaches for SD process enhancement.
• Case studies demonstrate the successful implementation of these methods.

Conclusions:

• Optimization strategies are essential for improving the quality and economics of spray drying.
• A combination of mathematical modeling and advanced optimization techniques offers a promising approach for SD.
• Further research and application of these methods can lead to superior final products.