Microalgae-derived hydrogen production towards low carbon emissions via large-scale outdoor systems

Enhanced biomethane production from Scenedesmus sp. using polymer harvesting and expired COVID-19 disinfectant for pretreatment

This study evaluates the repurposing of expired isopropanol (IPA) COVID-19 disinfectant (64% w/w) to pretreat algal biomass for enhancing methane (CH4) yield. The impact of harvesting methods (centrifugation and polymer flocculation) and microwave pretreatment on CH4 production from Scenedesmus sp. microalgal biomass were also investigated. Results show minimal impact of harvesting methods on the CH4 yield, with wet centrifuged and polymer-harvested biomass exhibiting comparable and low CH4 production at 66 and 74 L/kgvolatile solid, respectively. However, microalgae drying significantly increased CH4 yield compared to wet biomass, attributed to cell shrinkage and enhanced digestibility. Consequently, microwave and IPA pretreatment significantly enhanced CH4 production when applied to dried microalgae, yielding a 135% and 212% increase, respectively, compared to non-pretreated wet biomass. These findings underscore the advantage of using dried Scenedesmus sp. over wet biomass and highlight the synergistic effect of combining oven drying with IPA treatment to boost CH4 production whilst reducing COVID-19 waste.

Microalgal biorefineries: Advancement in machine learning tools for sustainable biofuel production and value-added products recovery

The microalgae can be converted into biofuels, biochemicals, and bioactive compounds in a biorefinery. Recently, designing and executing more viable and sustainable biofuel production from microalgal biomass is one of the vital challenges in the development of biorefinery. Scalable cultivation of microalgae is mandatory for commercializing and industrializing the biorefinery. The intrinsic complication in cultivation of microalgae is the physiological and operational factors that renders challenging impact to enable a smooth and profitable operation. However, this aim can only be successful via a simulation prospect. Machine learning tools provides advanced approaches for evaluating, predicting, and controlling uncertainties in microalgal biorefinery for sustainable biofuel production. The present review provides a critical evaluation of the most progressing machine learning tools that validate a potential to be employed in microalgal biorefinery. These tools are highly potential for their extensive evaluation on microalgal screening and classification. However, the application of these tools for optimization of microalgal biomass cultivation in industries in order to increase the biomass production, is still in its initial stages. Integrated hybrid machine learning tools can aid the industries to function efficiently with least resources. Some of the challenges, and perspectives of machine learning tools are discussed. Besides, future prospects are also emphasized. Though, most of the research reports on machine learning tools are not appropriate to gather generalized information, standard protocols and strategies must be developed to design generalized machine learning tools. On a whole, this review offers a perspective information about digitalized microalgal exploitation in a microalgal biorefinery.