Kinetic analysis of microalgae cultivation utilizing 3D-printed real-time monitoring system reveals potential of biological CO2 conversion

A comprehensive review on the heterotrophic production of bioactive compounds by microalgae

Bioactive compounds derived from microalgae have garnered considerable attention as valuable resources for drugs, functional foods, and cosmetics. Among these compounds, photosynthetic pigments and polyunsaturated fatty acids (PUFAs) have gained increasing interest due to their numerous beneficial properties, including anti-oxidant, anti-viral, anti-bacterial, anti-fungal, anti-inflammatory, and anti-tumor effects. Several microalgae species have been identified as rich sources of bioactive compounds, including the Chlorophyceae Dunaliella and Haematococcus, the Bacillariophyta Phaeodactylum and Nitzschia, and the dinoflagellate Crypthecodinium cohnii. However, most of the reported microalgae species primarily grow through autotrophic mechanisms, resulting in low yields and high production costs of bioactive compounds. Consequently, the utilization of heterotrophic microalgae, such as Chromochloris zofingiensis and Nitzschia laevis, has shown significant advantages in the production of astaxanthin and eicosapentaenoic acid (EPA), respectively. These heterotrophic microalgae exhibit superior capabilities in synthesizing target compounds. This comprehensive review provides a thorough examination of the heterotrophic production of bioactive compounds by microalgae. It covers key aspects, including the metabolic pathways involved, the impact of cultivation conditions, and the practical applications of these compounds. The review discusses how heterotrophic cultivation strategies can be optimized to enhance bioactive compound yields, shedding light on the potential of microalgae as a valuable resource for high-value product development.

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.

Impact of 3D printing materials on mircoalga Chlorella vulgaris

3D printing represents a key enabling technology in designing photobioreactors. It allows rapid prototyping of complex geometries at an affordable price. Yet, no study dealt with the biocompatibility of 3D printing material with microalgae. Thus microalga Chlorella vulgaris was cultivated in contact with different 3D printing materials (Acrylonitrile Butadiene Styren - ABS, PolyCarbonate Blend - PC-Blend, PolyLactic acid - PLA, and acrylate methacrylate resin). Cell status was analyzed using flow cytometry, fluorometry, and pigment profiling. Results revealed that acrylate methacrylate resin material inhibits growth, a constant rise in intracellular reactive oxygen species, and a decrease in photosynthetic apparatus functioning. On the contrary, ABS, PC-Blend, and PLA led to nominal perfromances. Nevertheless, PLA was the only material that did not induce an early onset of intracellular reactive oxygen species. Therefore, resin can be ruled out as photobioreactor material, ABS and PC-Blend could be used after a curation period, and PLA induces no detectable perturbations by the means used in this study.

Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review

The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.

Enhancement of microalgal biomass productivity through mixotrophic culture process utilizing waste soy sauce and industrial flue gas

Wastewater treatment plants are indispensable facilities, which emit a massive amount of greenhouse gases. To boost CO₂ mitigation and wastewater treatment performance, mixotrophic microalgae cultivation using wastewater has recently been proposed. In this study, food industry wastewater (waste soy sauce) was applied to Chlorella sorokiniana UTEX 2714 cultivation. By using a medium with 20% (v/v) of 10-fold diluted soy sauce, the biomass and fatty acid methyl ester (FAME) productivity enhanced by 1.93 and 1.76 times, respectively. Biomass productivity increased up to 5.2 times when using medium with high soy sauce content under high-intensity light that inhibits cell growth in photoautotrophic environments. Furthermore, industrial flue gas treatment with wastewater was demonstrated by outdoor semi-continuous cultivation with 42% improved biomass production. Consequently, these results suggest that mixotrophic microalgal cultivation has great potential to address both climate change and water pollution while producing valuable products and can contribute to building a sustainable society.