Abreast insights of harnessing microalgal lipids for producing biodiesel: A review of improving and advancing the technical aspects of cultivation.

In the pursuit of alternatives for conventional diesel, sourced from non-renewable fossil fuel, biodiesel has gained attentions for its intrinsic benefits. However, the commercial production process for biodiesel is still not sufficiently competitive. This review analyses microalgal lipid, one of the important sources of biodiesel, and its cultivation techniques with recent developments in the technical aspects. In fact, the microalgal lipids are the third generation feedstock, used for biodiesel production after its benefits outweigh that of edible vegetable oils (first generation) and non-edible oils (second generation). The critical factors influencing microalgal growth and its lipid production and accumulation are also discussed. Following that is the internal enhancement for cellular lipid production through genetic engineering. Moreover, the microalgae cultivation data modelling was also rationalized, with a specific focus on growth kinetic models that allow for the prediction and optimization of lipid production. Finally, the machine learning and environmental impact analysis are as well presented as important aspects to consider in fulfilling the prime objective of commercial sustainability to produce microalgal biodiesel.

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.

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

The depletion of fossil fuel sources and increase in energy demands have increased the need for a sustainable alternative energy source. The ability to produce hydrogen from microalgae is generating a lot of attention in both academia and industry. Due to complex production procedures, the commercial production of microalgal biohydrogen is not yet practical. Developing the most optimum microalgal hydrogen production process is also very laborious and expensive as proven from the experimental measurement. Therefore, this research project intended to analyse the random time series dataset collected during microalgal hydrogen productions while using various low thermally pre-treated palm kernel expeller (PKE) waste via machine learning (ML) approach. The analysis of collected dataset allowed the derivation of an enhanced kinetic model based on the Gompertz model amidst the dark fermentative hydrogen production that integrated thermal pre-treatment duration as a function within the model. The optimum microalgal hydrogen production attained with the enhanced kinetic model was 387.1 mL/g microalgae after 6 days with 1 h thermally pre-treated PKE waste at 90 °C. The enhanced model also had better accuracy (R2 = 0.9556) and net energy ratio (NER) value (0.71) than previous studies. Finally, the NER could be further improved to 0.91 when the microalgal culture was reused, heralding the potential application of ML in optimizing the microalgal hydrogen production process.