A smart and precise mixing strategy for efficient and cost-effective microalgae production in open ponds

Advancements of astaxanthin production in Haematococcus pluvialis: Update insight and way forward

The global market demand for natural astaxanthin (AXT) is growing rapidly owing to its potential human health benefits and diverse industry applications, driven by its safety, unique structure, and special function. Currently, the alga Haematococcus pluvialis (alternative name H. lacustris) has been considered as one of the best large-scale producers of natural AXT. However, the industry's further development faces two main challenges: the limited cultivation areas due to light-dependent AXT accumulation and the low AXT yield coupled with high production costs resulting from complex, time-consuming upstream biomass culture and downstream AXT extraction processes. Therefore, it is urgently to develop novel strategies to improve the AXT production in H. pluvialis to meet industrial demands, which makes its commercialization cost-effective. Although several strategies related to screening excellent target strains, optimizing culture condition for high biomass yield, elucidating the AXT biosynthetic pathway, and exploiting effective inducers for high AXT content have been applied to enhance the AXT production in H. pluvialis, there are still some unsolved and easily ignored perspectives. In this review, firstly, we summarize the structure and function of natural AXT focus on those from the algal H. pluvialis. Secondly, the latest findings regarding the AXT biosynthetic pathway including spatiotemporal specificity, transport, esterification, and storage are updated. Thirdly, we systematically assess enhancement strategies on AXT yield. Fourthly, the regulation mechanisms of AXT accumulation under various stresses are discussed. Finally, the integrated and systematic solutions for improving AXT production are proposed. This review not only fills the existing gap about the AXT accumulation, but also points the way forward for AXT production in H. pluvialis.

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.

A precise microalgae farming for CO2 sequestration: A critical review and perspectives

Microalgae are great candidates for CO2 sequestration and sustainable production of food, feed, fuels and biochemicals. Light intensity, temperature, carbon supply, and cell physiological state are key factors of photosynthesis, and efficient phototrophic production of microalgal biomass occurs only when all these factors are in their optimal range simultaneously. However, this synergistic state is often not achievable due to the ever-changing environmental factors such as sunlight and temperature, which results in serious waste of sunlight energy and other resources, ultimately leading to high production costs. Most control strategies developed thus far in the bioengineering field actually aim to improve heterotrophic processes, but phototrophic processes face a completely different problem. Hence, an alternative control strategy needs to be developed, and precise microalgal cultivation is a promising strategy in which the production resources are precisely supplied according to the dynamic changes in key factors such as sunlight and temperature. In this work, the development and recent progress of precise microalgal phototrophic cultivation are reviewed. The key environmental and cultivation factors and their dynamic effects on microalgal cultivation are analyzed, including microalgal growth, cultivation costs and energy inputs. Future research for the development of more precise microalgae farming is discussed. This study provides new insight into developing cost-effective and efficient microalgae farming for CO2 sequestration.

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.

Biomass production of Chlorella pyrenoidosa by filled sphere carrier reactor: Performance and mechanism

Microalgae-based Carbon Capture, Utilization and Storage is vital for mitigating global climate change. A filled sphere carrier reactor was developed to achieve high biomass production and carbon sequestration rate of Chlorella pyrenoidosa. By introducing air (0.04% CO₂) into the reactor, the dry biomass production achieved 8.26 g/L with the optimized parameters of polyester carrier, 80% packing density, 5-fold concentrated nutrient combining 0.2 mol/L phosphate buffer. At simulated flue gas CO₂ concentration of 7%, the dry biomass yield and carbon sequestration rate reached up to 9.98 g/L and 18.32 g/L/d in one day, which were as high as 249.5 and 79.65 times comparing with those of suspension culture at day 1, respectively. The mechanism was mainly attributed to the obvious intensification of electron transfer rate and remarkable increase of RuBisCO enzyme activity in the photosynthetic chloroplast matrix. This work provided a novel approach for potential microalgae-based carbon capture and storage.

Utilization of dark fermentation effluent for algal cultivation in a modified airlift photobioreactor for biomass and biocrude production

Developing an efficient photobioreactor (PBR) and reducing freshwater dependence are among the significant challenges for generating 3rd generation biomass feedstock. Addressing these, the present study focused on developing a modified airlift (MoAL) PBR. Its performance was further evaluated and compared with the traditional airlift PBR by cultivating microalgae in dark fermentation spent wash. Lower mixing time and higher interfacial mass transfer coefficient was observed in the MoAL PBR having a perforated draft tube. Experimentally, the MoAL exhibited the maximum biomass concentration of 3.18 g L^-1, which was 30% higher than that of the conventional airlift PBR. The semi-continuous operation of the MoAL (with water recycling) achieved the maximum biomass productivity of 0.83 g L^-1 d^-1, two folds superior to that of batch culture. The comprehensive biomass characterization (proximate, ultimate, and thermochemical) further confirmed its potential for bioenergy application. Considering that, hydrothermal liquefaction of the biomass resulted in a maximum biocrude yield of 31% w/w with a higher heating value (HHV) of 36.6 MJ kg^-1. In addition, the biocrude comprised 66.6% w/w lighter fraction (<343 °C), including 21.5% w/w of heavy naphtha, 20.5% w/w of kerosene, and 24.6% w/w of diesel. The results can help develop sustainable technology for simultaneous wastewater remediation and biocrude production.

Photobiological effects of converting biomass into hydrogen - Challenges and prospects

In comparison to other methods of producing hydrogen, the production of biohydrogen is significantly less harmful to the surrounding ecosystem when it was produced from the biological origin such as microalgae. It could take the place of conventional fossil fuels while avoiding the emission of greenhouse gases. The substrates such as food, agricultural waste, and industrial waste can be readily utilized after the necessary pretreatment, led to an increase in the yield of hydrogen. Improving the production of biofuels at each stage can have a significant impact on the final results, making this method a potentially useful instrument. As a consequence of this, numerous approaches to pretreat the algal biomass, numerous types of enzymes and catalyst that play a crucial role for hydrogen production, the variables that influence the production of hydrogen, and the potential applications of genetic engineering have all been comprehensively covered in this study.