BIOFLOCCULANT PRODUCTION FROM MICROBIAL CONSORTIUM AND EVALUATION OF FLOCCULATING ACTIVITY USING ARTIFICIAL INTELLIGENCE

Abstract

Water pollution from industrial sources has increased, and it has become a serious environmental problem. Improperly treated wastewater, including chemical and microbial waste, pollutes water bodies and poses risks to ecosystems and human health. Inorganic flocculation has been used as a viable method of wastewater treatment but poses potential health risks. Therefore, we need to find safe alternatives to inorganic flocculant. This project aims to generate bioflocculants from a microbial consortium and evaluate their flocculating activity using artificial intelligence. This study focuses on isolating bacteria from the Henge River from and testing their ability to produce bioflocculants. Twelve bacterial were isolated and group into three i.e., GROUP A (1= Bacillus sp, 2= Pseudomonas sp, 3= Pseudomonas sp, 4= Bacillus cereus) GROUP B (1= Streptococcus sp, 2= Serratia sp, 3= Klebsiella 0sp, 4= Staphylococcus sp) and GROUP C (1= Staphylococcus sp, 2= Corynebacterium sp, 3= micrococcus sp, 4= Acetobacter sp) at double, triple, and quadruple combination. The statistical variance was also calculated using ANOVA and post HOC test. Group A showed a range of flocculating activity of 85%, to 34% which were produced by consortium A1, 3, 4 and A1, 2, 3 and 4. The range of flocculating activity for Group B was 93%, to 64% which were produced by consortium B2, 4 and B1, 3. For consortium C, the flocculating activity range was 88% to 52% which were produced by consortium C1, 3 and C3, 4. The consortium of the isolate showed significant flocculating activity suggesting that combination of organism could produce bioflocculant with better activity. The compilation of dataset for the artificial intelligence in evaluating activity will minimize human errors and enhance precision.