Microalgae for Biostimulants: Valorization of Wastewater as the Nutrient Source and Enzymatic Hydrolysis of Algae Biomass

This study investigates the cultivation of Chlorella vulgaris microalgae in agricultural wastewaters from a fertilizer production facility, with a focus on potential biostimulants production. Investigating microalgal growth performance across varying wastewater concentrations, the research aims to achieve biostimulant production, high-value byproducts, wastewater treatment, and economic benefits. Two distinct wastewaters, denoted as wastewater 1 and wastewater 2, are utilized in the study. The study involves a comprehensive analysis of cell growth and nutrient utilization. Additionally, the biomass produced, rich in protein content, undergoes enzymatic hydrolysis for potential protein extraction. The enzymes Alcalase® and Viscozyme® are employed individually and in combination to evaluate their efficacy in protein extraction. The research also addresses the potential influence of experimental conditions, including pH and temperature, on enzyme performance and protein denaturation. The results indicate successful microalgal cultivation in 5% and 10% concentrations of wastewaters, with optimal growth observed at the 5% concentration. The study covers complexities in nutrient utilization, highlighting the need for further optimization in the microalgal cultivation process. It also underscores the significance of ammonium content in the medium for microalgae growth. furthermore, the nitrogen and phosphorus successfully removed from the wastewater 1, achieving the goal of wastewater treatment. In enzymatic hydrolysis, Alcalase® demonstrates a positive effect in protein extraction, while Viscozyme® exhibits no effectiveness. The enzymatic hydrolysis process requires optimization of both experimental conditions and enzyme concentration. The findings contribute valuable insights into sustainable biostimulant and byproduct production from agricultural wastewaters, paving the way for future research and process optimization.