A holistic approach of integrating AI-based optimization technique for improved productivity of phyco-myco co-cultivation

Abstract

The scientific community encounters two primary challenges in manufacturing biodiesel from algae. One of them relates to the economical cultivation, while the other problem points to productivity improvement. Co-cultivating microalgae and fungi hold significant potential for biomass and lipid production, offering both environmental and economic advantages. In view of these aspects, the present research has attempted to utilize kitchen effluents, including milk-whey (MW) and soya-chunk boiled water (SCBW), as media supplements in co-cultivation. To obtain highest possible production, the process parameters were optimized using response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA) methods. The ANN-GA resulted 2.5 and 4.2-fold increase in biomass and lipid respectively compared to the axenic algae culture. Thus, co-cultivating Chlorella vulgaris and Aspergillus awamori on medium enriched with mixed effluents provide cleaner water with COD removal efficiency of 75–80 % and valuable biomass for lipid production suitable for biodiesel application. Serial scale-up studies have been adopted from a 3 L (1.77 ± 0.02 g/L biomass, 0.4 ± 0.03 g/L lipid) flask to 10 L (1.87 ± 0.05 g/L biomass, 0.48 ± 0.02 g/L lipid) bubble column reactor. The bioremediation treatment has resulted in a 70 % increase in germination efficiency in C. arietinum and a 15 % increase in V. radiata compared to the germination efficiency of the untreated media. The FAMEs were examined using GC–MS to assess their appropriateness for biodiesel application. This work is unique in achieving improved biomass utilizing cost-effective waste effluents as supplements in an open condition with a reduced time. The produced biodiesel is at par with the standard requirement.