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Related Concept Videos

Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

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Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
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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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Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
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The group Stramenopiles include some phototrophic microorganisms. Members of this group possess flagella covered in numerous short, hairlike extensions, a feature that inspired the group's name, derived from the Latin words for "straw" and "hair." Some of the main categories of Stramenopiles include diatoms, golden algae, and brown algae.Diatoms are unicellular, photosynthetic eukaryotes, with over 200 known genera. They play a key role in the planktonic communities of both marine and...
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Real-time ensemble microalgae growth forecasting with data assimilation.

Hongxiang Yan1, Mark S Wigmosta1,2, Ning Sun1

  • 1Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.

Biotechnology and Bioengineering
|January 5, 2021
PubMed
Summary
This summary is machine-generated.

Accurate 7-day microalgae growth forecasts are now possible using a new system combining biomass growth and hydrodynamic models with data assimilation. This approach significantly improves forecasting accuracy for microalgae production and harvesting operations.

Keywords:
biomass forecastingbiomass growth modelensemble data assimilationharvest planningparticle filteringpond operation

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Area of Science:

  • * Algology and applied microbiology.
  • * Aquatic ecosystem modeling.
  • * Biotechnology and bioprocess engineering.

Background:

  • * Precise, short-term microalgae growth prediction is crucial for optimizing cultivation and harvesting processes.
  • * Existing models often lack the accuracy needed for real-time operational decisions in commercial microalgae production.

Purpose of the Study:

  • * To develop and validate an operational microalgae growth forecasting system.
  • * To enhance short-range (7-day) biomass forecasting accuracy using ensemble data assimilation.
  • * To assess the system's utility for informing real-time operational decisions in microalgae cultivation.

Main Methods:

  • * Integration of the Huesemann Algae Biomass Growth Model (BGM) with the Modular Aquatic Simulation System in Two Dimensions (MASS2) hydrodynamic model.
  • * Implementation of ensemble data assimilation (DA) to sequentially update the BGM's initial conditions.
  • * Assimilation of measured biomass optical density data to refine model predictions.

Main Results:

  • * The developed forecasting system demonstrated a significant improvement in 7-day microalgae growth prediction accuracy.
  • * An average improvement of approximately 85% in forecasting skill was observed compared to forecasts without data assimilation.
  • * The system was successfully run in pseudo-real-time and validated against Monoraphidium minutum growth in outdoor pond cultures.

Conclusions:

  • * The ensemble data assimilation approach substantially enhances the accuracy of short-range microalgae biomass forecasts.
  • * The forecasting system's accuracy is sufficient to support operational decisions in commercial microalgae production.
  • * This advancement holds potential for improving the efficiency and economic viability of microalgae cultivation.