Predictive Growth Model of Indigenous Green Microalgae (Scenedesmus sp. UKM9) in Palm Oil Mill Effluent (POME)

Abstract

Microalgae cultivation in POME treatment to removed environmental pollutants synchronized with biomass production. Biomass production depends on the proliferation rate of microalgae in wastewater, which is rarely addressed. Thus, growth rate assessment was performed on a local strain of Scenedesmus sp. UKM9 by using four different mathematical models of logistic, modified logistic, modified Gompertz, and Baranyi-Roberts. Statistical analyses, including root-mean-square error (RMSE), R2, adjusted R2, bias factor (BF), accuracy factor (AF), and standard error prediction (%SEP) were applied to verify the accuracy of each model. The findings illustrated that the Baranyi-Roberts model had the highest AF of 1.000, the lowest RMSE and %SEP values of 0.1300 and 5.40%, respectively, and the predicted BF value was 1.0001. These results revealed that the model was relevant to complex environmental conditions, and its parameter was biologically interpretable. The Baranyi-Roberts model regards a suitable model for the growth of indigenous microalgae of Scenedesmus sp. UKM9 in POME.