Biotech Microalgae Laboratory
Microalgae Growth Optimization Model
Timeline
Data Collection
Chemical concentrations and growth results structured.
Model Training
ML model trained to predict development outcomes.
Feature Analysis
Key growth-driving elements identified.
Reverse Insights
Environmental impact interpreted.
Optimization
Future substrate conditions estimated.
Context
The laboratory conducted experiments using different substrate compositions to analyze microalgae development under varying chemical environments. Each experiment produced: - Chemical concentration data - Sample imagery - Final observed growth levels
Challenge
Understanding which chemical elements truly influenced algae growth required extensive manual analysis. The number of variables made it difficult to: - Identify growth-driving factors - Compare experimental outcomes - Predict optimal future environments
Solution
We developed a machine learning model trained on: - Chemical concentration data - Substrate imagery - Measured algae development outcomes The model predicts expected growth levels and enables reverse analysis to identify which elements most contributed to successful development.
Impact
- ✔Prediction of microalgae growth outcomes
- ✔Identification of key growth-driving elements
- ✔Reduction of trial-and-error experimentation
- ✔Data-driven substrate design
Outcome
The lab gained the ability to estimate which future chemical environments are most likely to produce optimal microalgae development — turning experimentation into a guided optimization process.