Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses.

Area of Science:

• Biotechnology
• Process Engineering
• Computational Science

Background:

• Microalgae cultivation is crucial for biofuels, food, and pharmaceuticals.
• Traditional methods face limitations in efficiency, control, and scalability.
• Emerging AI and ML technologies offer potential solutions to these challenges.

Purpose of the Study:

• To review the application of AI/ML in microalgae processes.
• To analyze the benefits and challenges of AI/ML implementation.
• To identify future research directions for AI/ML in this field.

Main Methods:

• Literature review of AI/ML applications in microalgae cultivation.
• Analysis of commonly used ML algorithms (SVM, GA, DT, RF, ANN, DL).
• Examination of challenges and proposed solutions for AI/ML integration.

Main Results:

• AI/ML significantly improve real-time monitoring, species identification, growth optimization, harvesting, and purification.
• Algorithms like SVM, GA, ANN, and DL show promise but face issues like computational cost and transparency.
• Demonstrated improvements in system performance, scalability, resource efficiency, cost reduction, and environmental impact.

Conclusions:

• AI/ML integration in microalgae processes offers substantial advantages.
• Overcoming data availability, model complexity, and regulatory hurdles is key for broader adoption.
• Future work should focus on simulation-based data, modular designs, and adaptive learning for robust AI/ML systems.