Advanced Control Strategies for Sustainable Microalgae Bioprocesses

Added:

Project Setup
Reactor Types
Modeling Methods
Control Strategy
Future Plans

Project Setup

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Playing Section
  • 1

    Introduces research context on water and air pollution challenges.

  • 2

    Presents microalgae as a dual solution for purification and CO2 fixation.

  • 3

    Outlines presentation structure covering methods and objectives.

Fundamentals of microalgae biology, including growth kinetics, photosynthetic efficiency, and key nutrient requirements (nitrogen, phosphorus, light).
Basic control systems engineering concepts, particularly feedback/feedforward loops and the distinction between model-free and model-based control.
The biochemical principles behind biological wastewater treatment and photosynthetic carbon dioxide sequestration.
Introduction to mathematical modeling of bioprocesses, such as ordinary differential equations used to describe cell growth and substrate consumption.
Advanced Model Predictive Control (MPC) design and reinforcement learning applications in real-time bioprocess optimization.
Scale-up engineering challenges of photobioreactors, transitioning from laboratory-scale systems to industrial-scale open ponds or closed systems.
Downstream processing technologies for microalgae, including harvesting, dewatering, and extraction of high-value bioproducts or biofuels.
Techno-economic analysis (TEA) and Life Cycle Assessment (LCA) to evaluate the economic viability and environmental footprint of microalgae biorefineries.
148 views1likes9:38@BiocontrolSeminarsOriginal Release: 2024-11-27

Microalgae bioreactors, particularly open pond raceway systems, offer a sustainable solution for addressing water pollution and CO2 emissions by utilizing microalgae's photosynthetic ability to absorb nitrogen, phosphorus, and CO2 while producing purified water and valuable biomass. Effective control of these systems requires advanced strategies such as Model Predictive Control (MPC) that manage key variables including pH, dissolved oxygen, biomass concentration, and temperature through hierarchical optimization, balancing economic considerations with environmental benefits to maximize biomass production and purification efficiency.