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Green Algae

Green Algae

Green algae, also referred to as chlorophytes, are different from red algae in having the chloroplasts containing chlorophylls a and b, which give them their distinct green hue. However, they lack phycobiliproteins, preventing them from developing the red or blue-green pigmentation seen in red algae. In terms of photosynthetic pigment composition, green algae closely resemble plants and share a close evolutionary relationship with them. Taxonomically Green algae belong to Phylum Chlorophyta in...

Overview of Algae

Overview of Algae

The kingdom Archaeplastida encompasses red and green algae, along with land plants. Unlike other protists with chloroplasts that arose through secondary endosymbiosis, only red and green algae originated from primary endosymbiotic events. This diverse group of eukaryotic organisms contains chlorophyll and performs oxygenic photosynthesis.Algae exist in various forms, from large brown kelp in coastal waters to green scum in puddles and stains on rocks or soil. Some species are responsible for...

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Related Experiment Video

Updated: Jun 6, 2025

Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation

Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation

Published on: August 14, 2020

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

Esra Imamoglu¹

¹Department of Bioengineering, Faculty of Engineering, Ege University, Izmir 35100, Turkey.

Bioengineering (Basel, Switzerland)

|November 27, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) enhance microalgae production efficiency and control. While challenges exist, AI/ML offer significant benefits for scalability, cost reduction, and environmental impact in microalgae processes.

Keywords:

artificial intelligence artificial neural networks decision tree deep learning genetic algorithm internet of things machine learning algorithms microalgae random forest support vector machine

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Last Updated: Jun 6, 2025

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Published on: August 14, 2020

Cultivation of Green Microalgae in Bubble Column Photobioreactors and an Assay for Neutral Lipids

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Published on: October 28, 2021

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.