Kinetic model derived from machine learning for accurate prediction of microalgal hydrogen production via conversion from low thermally pre-treated palm kernel expeller waste

Modelling photoperiod in enhancing hydrogen production from Chlorella vulgaris sp. while bioremediating ammonium and organic pollutants in municipal wastewater

Municipal wastewater is ubiquitously laden with myriad pollutants discharged primarily from a combination of domestic and industrial activities. These heterogeneous pollutants are threating the natural environments when the traditional activated sludge system fails sporadically to reduce the pollutants' toxicities. Besides, the activated sludge system is very energy intensive, bringing conundrums for decarbonization. This research endeavoured to employ Chlorella vulgaris sp. In converting pollutants from municipal wastewater into hydrogen via alternate light and dark fermentative process. The microalgae in attached form onto 1 cm3 of polyurethane foam cubes were adopted in optimizing light intensity and photoperiod during the light exposure duration. The highest hydrogen production was recorded at 52 mL amidst the synergistic light intensity and photoperiod of 200 μmolm-2s-1 and 12:12 h (light:dark h), respectively. At this lighting condition, the removals of chemical oxygen demand (COD) and ammoniacal nitrogen were both achieved at about 80%. The sustainability of microalgal fermentative performances was verified in recyclability study using similar immobilization support material. There were negligible diminishments of hydrogen production as well as both COD and ammoniacal nitrogen removals after five cycles, heralding inconsequential microalgal cells' washout from the polyurethane support when replacing the municipal wastewater medium at each cycle. The collected dataset was finally modelled into enhanced Monod equation aided by Python software tool of machine learning. The derived model was capable to predict the performances of microalgae to execute the fermentative process in producing hydrogen while subsisting municipal wastewater at arbitrary photoperiod. The enhanced model had a best fitting of R2 of 0.9857 as validated using an independent dataset. Concisely, the outcomes had contributed towards the advancement of municipal wastewater treatment via microalgal fermentative process in producing green hydrogen as a clean energy source to decarbonize the wastewater treatment facilities.

Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization

Preservatives are essential components in cosmetic products, but their safety issues have attracted widespread attention. There is an urgent need for safe and effective alternatives. Antimicrobial peptides (AMPs) are part of the innate immune system and have potent antimicrobial properties. Using machine learning-assisted rational design, we obtained a novel antibacterial peptide, IK-16-1, with significant antibacterial activity and maintaining safety based on β-defensins. IK-16-1 has broad-spectrum antimicrobial properties against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans, and has no haemolytic activity. The use of IK-16-1 holds promise in the cosmetics industry, since it can serve as a preservative synergist to reduce the amount of other preservatives in cosmetics. This study verified the feasibility of combining computational design with artificial intelligence prediction to design AMPs, achieving rapid screening and reducing development costs.