Henna plant biomass enhanced azo dye removal: Operating performance, microbial community and machine learning modeling

Abstract

The bio-reduction of azo dyes is significantly dependent on the availability of electron donors and external redox mediators. In this study, the natural henna plant biomass was supplemented to promote the biological reduction of an azo dye of Acid Orange 7 (AO7). Besides, the machine learning (ML) approach was applied to decipher the intricate process of henna-assisted azo dye removal. The experimental results indicated that the hydrolysis and fermentation of henna plant biomass provided both electron donors such as volatile fatty acid (VFA) and redox mediator of lawsone to drive the bio-reduction of AO7 to sulfanilic acid (SA). The high henna dosage selectively enriched certain bacteria, such as Firmicutes phylum, Levilinea and Paludibacter genera, functioning in both the henna fermentation and AO7 reduction processes simultaneously. Among the three tested ML algorithms, eXtreme Gradient Boosting (XGBoost) presented exceptional accuracy and generalization ability in predicting the effluent AO7 concentrations with pH, oxidation-reduction potential (ORP), soluble chemical oxygen demand (SCOD), VFA, lawsone, henna dosage, and cumulative henna as input variables. The validating experiments with tailored optimal operating conditions and henna dosage (pH 7.5, henna dosage of 2 g/L, and cumulative henna of 14 g/L) confirmed that XGBoost was an effective ML model to predict the efficient AO7 removal (91.6%), with a negligible calculating error of 3.95%. Overall, henna plant biomass addition was a cost-effective and robust method to improve the bio-reduction of AO7, which had been demonstrated by long-term operation, ML modeling, and experimental validation.