Predicting microalgal biomass yield in outdoor raceway pond cultivation with the empirical modeling approach

Abstract

Microalgae offer economic prospects in the industrial growth of bioenergetics. Although microalgae cultivation in open raceway ponds is manageable and cost-effective, dynamic environmental parameters can disrupt their growth cycles. Hence, models were developed to predict and optimize large-scale outdoor microalgal production, but many attempts failed to reproduce the predictive ability. This study aims to develop a prediction model for biomass yield in open raceway ponds by integrating two multivariate methods, viz. principal component analysis (PCA) and partial least squares (PLS) regression. The model was developed based on the cultural and environmental findings for cultivating a freshwater chlorophycean microalga Chlorella minutissima in open raceway ponds for two consecutive years. Results obtained from PCA revealed a strong correlation between solar intensity and temperature parameters, accounting for 91% of the variability in the resultant biomass yield. Consequently, PLS regression predictors for model fitting were solar intensity, temperature, relative humidity, and dissolved oxygen concentration. Findings from PLS model demonstrated predicted R2 value of 0.91. Further, the model was validated using the experimental data for the outdoor conditions from available literature and showed significant prediction efficiency up to 97%, with mean absolute errors in the range of 3–21%. Thus, the developed model has the potential to predict the microalgal biomass yield in outdoor raceway ponds accurately.