A review on machine learning approaches for microalgae cultivation systems.

Area of Science:

• Biotechnology
• Bioprocess Engineering
• Computational Biology

Background:

• Microalgae are vital for biomass production, offering sustainable solutions for global food and fuel demands.
• Optimizing microalgae cultivation requires advanced screening, monitoring, and predictive modeling systems.
• Current methods for modeling microalgae processes include mechanistic and machine learning-based approaches.

Purpose of the Study:

• To review and analyze existing techniques for modeling, predicting, and monitoring microalgal biomass.
• To investigate the potential of hybrid modeling approaches in microalgae cultivation and processing.
• To identify challenges and propose solutions for the practical implementation of hybrid techniques.

Main Methods:

• Comprehensive literature review of mechanistic and machine learning models for microalgae.
• Analysis of methods applied to bioenergy, pharmaceutical, and food industry applications.
• Exploration of emerging hybrid modeling methodologies and their limitations.

Main Results:

• Diverse modeling techniques exist, each with specific strengths and weaknesses for microalgae applications.
• Hybrid approaches show promise for enhanced accuracy and efficiency in microalgae process optimization.
• Practical implementation of hybrid models faces challenges, but solutions are emerging from related fields.

Conclusions:

• Advanced modeling is essential for unlocking the full potential of microalgae in various industries.
• Hybrid modeling techniques represent a promising frontier for optimizing microalgae value chains.
• Overcoming implementation challenges will facilitate the wider adoption of sophisticated microalgae management systems.