Journal Article

Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses

Imamoglu, Esra

2024

Overview

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.

• Publisher: MDPI AG,MDPI
• Subject: Adaptive learning / Adaptive systems / Algae / Algorithms / Aquatic microorganisms / Artificial intelligence / Artificial neural networks / Barriers / Biodiesel fuels / Biofuels / Biological monitoring / Biological products / Biomass / Carbohydrates / Carbon dioxide / Carotenoids / Costs / Cybersecurity / Data mining / decision tree / Decision trees / Deep learning / Dietary supplements / Downtime / Efficiency / Energy consumption / Environmental impact / Flocculation / genetic algorithm / Genetic algorithms / Growth conditions / Harvesting / Learning algorithms / Lipids / Machine learning / machine learning algorithms / Modular systems / Neural networks / Ponds / Process controls / Productivity / random forest / Real time / Review / support vector machine / Support vector machines / Sustainability