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  11. Application Of Artificial Intelligence And Image Processing For The Cultivation Of Chlorella Sp. Using Tubular Photobioreactors

Application of Artificial Intelligence and Image Processing for the Cultivation of Chlorella sp. Using Tubular Photobioreactors

Thananop Tummawai1, Thongchai Rohitatisha Srinophakun2, Surapol Padungthon3

  • 1Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand.

ACS Omega
|November 25, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study developed an intelligent microalgae cultivation system using AI and IoT for real-time monitoring. Continuous 24-hour lighting significantly boosted biomass productivity in Chlorella sp. cultivation.

Area of Science:

  • Biotechnology
  • Agricultural Engineering
  • Environmental Science

Background:

  • Microalgae cultivation is crucial for sustainable production of biofuels, food, and environmental remediation.
  • Traditional monitoring methods for microalgae growth are often invasive, labor-intensive, and provide delayed feedback.
  • Integrating advanced technologies can optimize microalgae production systems for enhanced efficiency and sustainability.

Purpose of the Study:

  • To establish an intelligent, closed-loop photobioreactor (PBR) system for real-time, non-invasive monitoring and management of Chlorella sp. growth.
  • To leverage computational fluid dynamics (CFD), Internet of Things (IoT), artificial intelligence (AI), and image processing for optimizing microalgae cultivation.
  • To develop machine learning (ML) models for forecasting and improving algae farming conditions.

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Main Methods:

  • A closed tubular photobioreactor was engineered with seven sensors measuring temperature, pH, light intensity, EC, flow rate, oxygen, and light duration.
  • An ESP8266 microcontroller managed sensor data and system operations, while an ESP32 camera captured images for growth assessment.
  • Machine learning models, including eXtreme Gradient Boosting (XGBoost), were trained on a dataset of 602 samples under varying light cycles.

Main Results:

  • Continuous 24-hour lighting resulted in a 7.19% increase in biomass productivity, compared to a 2.09% increase under a 12-hour cycle.
  • Feature importance analysis identified temperature and light intensity as the most significant growth parameters.
  • The XGBoost model achieved a high accuracy of R² = 0.9997 for predicting microalgae growth.

Conclusions:

  • Intelligent technologies, including AI and IoT, significantly enhance the efficiency and sustainability of microalgae production.
  • Real-time, non-invasive monitoring and predictive modeling are key to optimizing Chlorella sp. cultivation.
  • This advanced system holds potential for applications in renewable energy, food security, and environmental management.