Multi-objective optimization studies of microalgae dewatering by utilizing bio-based alkali: a case study of response surface methodology (RSM) and genetic algorithm (GA)

Abstract

In the present study, the flocculating viability of bio-based alkali derived from cocoa pod husk ash was investigated by using a D-optimal design of the response surface methodology (RSM). The interactive effects of biomass concentration and flocculant dose on flocculation efficiency and concentration factors were examined. The generalization ability of modeling and predictive tools such as RSM and artificial neural network (ANN) was also examined. Moreso, the flocculation process was optimized using RSM and response surface methodology coupled with genetic algorithm (RSM-GA). The flocculation process results showed that the bio-based alkali effectively flocculated microalgae with more than 90% flocculation efficiency at the optimum condition predicted by RSM and RSM-GA. Both RSM and ANN have described the flocculation process with high accuracy, based on the values of statistical indices evaluated but ANN has demonstrated a higher generalization potential as compared to RSM. The results of elemental analysis of the bio-based alkali shown that the concentration of K+ (51,489 ppm) was highest, followed by Ca2+ (1450 ppm) and Na+ (210 ppm). This undoubtedly showed the alkaline nature of the bio-based alkali obtained from cocoa pod husk ash that was employed as the flocculant for harvesting microalgae. This study confirms that ash derived alkali can be used to effectively and efficiently harvest microalgae.