Multivariate modeling for microalgae growth in outdoor photobioreactors

An empirical model for prediction of microalgal growth in outdoor photobioreactors cultivation, using Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression method, is implemented. Experimental data of biomass production were collected over 1 year of operation of a bubble column prototype, monitoring light and temperature and changing cultivation's conditions. PCA isolates 2 Principal Components that explain 80% of the variance and are associated with Environmental Conditions and Cultivation Conditions. Moreover, the PLS regression model showed positive results in term of responses (R2 = 0.84) and residuals, following the experimental trends of outputs as specific growth rate (μ(d−1)) and productivity calculated at Cmax (Pmax (g L−1 d−1)), giving also good prediction results in its validation test. This method could be easily used for other purpose, by changing the input values of the specific cultivation used (including CO2 uptake or wastewater dilution ratio in the culture medium), obtaining as outputs the desired variables (lipid production rate, etc.).