Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products

Ensuring carbon neutrality via algae-based wastewater treatment systems: Progress and future perspectives

The emergence of algal biorefineries has garnered considerable attention to researchers owing to their potential to ensure carbon neutrality via mitigation of atmospheric greenhouse gases. Algae-derived biofuels, characterized by their carbon-neutral nature, stand poised to play a pivotal role in advancing sustainable development initiatives aimed at enhancing environmental and societal well-being. In this context, algae-based wastewater treatment systems are greatly appreciated for their efficacy in nutrient removal and simultaneous bioenergy generation. These systems leverage the growth of algae species on wastewater nutrients-including carbon, nitrogen, and phosphorus-alongside carbon dioxide, thus facilitating a multifaceted approach to pollution remediation. This review seeks to delve into the realization of carbon neutrality through algae-mediated wastewater treatment approaches. Through a comprehensive analysis, this review scrutinizes the trajectory of algae-based wastewater treatment via bibliometric analysis. It subsequently examines the case studies and empirical insights pertaining to algae cultivation, treatment performance analysis, cost and life cycle analyses, and the implementation of optimization methodologies rooted in artificial intelligence and machine learning algorithms for algae-based wastewater treatment systems. By synthesizing these diverse perspectives, this study aims to offer valuable insights for the development of future engineering applications predicated on an in-depth understanding of carbon neutrality within the framework of circular economy paradigms.

Advances in microalgae-based carbon sequestration: Current status and future perspectives

The advancement in carbon dioxide (CO2) sequestration technology has received significant attention due to the adverse effects of CO2 on climate. The mitigation of the adverse effects of CO2 can be accomplished through its conversion into useful products or renewable fuels. In this regard, microalgae is a promising candidate due to its high photosynthesis efficiency, sustainability, and eco-friendly nature. Microalgae utilizes CO2 in the process of photosynthesis and generates biomass that can be utilized to produce various valuable products such as supplements, chemicals, cosmetics, biofuels, and other value-added products. However, at present microalgae cultivation is still restricted to producing value-added products due to high cultivation costs and lower CO2 sequestration efficiency of algal strains. Therefore, it is very crucial to develop novel techniques that can be cost-effective and enhance microalgal carbon sequestration efficiency. The main aim of the present manuscript is to explain how to optimize microalgal CO2 sequestration, integrate valuable product generation, and explore novel techniques like genetic manipulations, phytohormones, quantum dots, and AI tools to enhance the efficiency of CO2 sequestration. Additionally, this review provides an overview of the mass flow of different microalgae and their biorefinery, life cycle assessment (LCA) for achieving net-zero CO2 emissions, and the advantages, challenges, and future perspectives of current technologies. All of the reviewed approaches efficiently enhance microalgal CO2 sequestration and integrate value-added compound production, creating a green and economically profitable process.

Waking Up the Sleep Field: An Overview on the Implications of Genetics and Bioinformatics of Sleep

Sleep genetics is an intriguing, as yet less understood, understudied, emerging area of biological and medical discipline. A generalist may not be aware of the current status of the field given the variety of journals that have published studies on the genetics of sleep and the circadian clock over the years. For researchers venturing into this fascinating area, this review thus includes fundamental features of circadian rhythm and genetic variables impacting sleep-wake cycles. Sleep/wake pathway medication exposure and susceptibility are influenced by genetic variations, and the responsiveness of sleep-related medicines is influenced by several functional polymorphisms. This review highlights the features of the circadian timing system and then a genetic perspective on wakefulness and sleep, as well as the relationship between sleep genetics and sleep disorders. Neurotransmission genes, as well as circadian and sleep/wake receptors, exhibit functional variability. Experiments on animals and humans have shown that these genetic variants impact clock systems, signaling pathways, nature, amount, duration, type, intensity, quality, and quantity of sleep. In this regard, the overview covers research on sleep genetics, the genomic properties of several popular model species used in sleep studies, homologs of mammalian genes, sleep disorders, and related genes. In addition, the study includes a brief discussion of sleep, narcolepsy, and restless legs syndrome from the viewpoint of a model organism. It is suggested that the understanding of genetic clues on sleep function and sleep disorders may, in future, result in an evidence-based, personalized treatment of sleep disorders.

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.

Recent advances in microalgal production, harvesting, prediction, optimization, and control strategies

The market value of microalgae has grown exponentially over the past two decades, due to their use in the pharmaceutical, nutraceutical, cosmetic, and aquatic/animal feed industries. In particular, high-value products such as omega-3 fatty acids, proteins, and pigments derived from microalgae have high demand. However, the supply of these high-value microalgal bioproducts is hampered by several critical factors, including low biomass and bioproduct yields, inefficiencies in monitoring microalgal growth, and costly harvesting methods. To overcome these constraints, strategies such as synthetic biology, bubble generation, photobioreactor designs, electro-/magnetic-/bioflocculation, and artificial intelligence integration in microalgal production are being explored. These strategies have significant promise in improving the production of microalgae, which will further boost market availability of algal-derived bioproducts. This review focuses on the recent advances in these technologies. Furthermore, this review aims to provide a critical analysis of the challenges in existing algae bioprocessing methods, and highlights future research directions.

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

Improving microalgae growth modeling of outdoor cultivation with light history data using machine learning models: A comparative study

Accurate prediction of microalgae growth is crucial for understanding the impacts of light dynamics and optimizing production. Although various mathematical models have been proposed, only a few of them have been validated in outdoor cultivation. This study aims to investigate the use of machine learning algorithms in microalgae growth modeling. Outdoor cultivation data of Phaeodactylum tricornutum in flat-panel airlift photobioreactors for 50 days were used to compare the performance of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) with traditional models, namely Monod and Haldane. The results indicate that the machine learning models outperform the traditional models due to their ability to utilize light history as input. Moreover, the LSTM model shows an excellent ability to describe the light acclimation effect. Last, two potential applications of these models are demonstrated: 1) use as a biomass soft sensor and 2) development of an optimal harvest strategy for outdoor cultivation.

Harnessing the potential of microalgae-bacteria interaction for eco-friendly wastewater treatment: A review on new strategies involving machine learning and artificial intelligence

In the pursuit of effective wastewater treatment and biomass generation, the symbiotic relationship between microalgae and bacteria emerges as a promising avenue. This analysis delves into recent advancements concerning the utilization of microalgae-bacteria consortia for wastewater treatment and biomass production. It examines multiple facets of this symbiosis, encompassing the judicious selection of suitable strains, optimal culture conditions, appropriate media, and operational parameters. Moreover, the exploration extends to contrasting closed and open bioreactor systems for fostering microalgae-bacteria consortia, elucidating the inherent merits and constraints of each methodology. Notably, the untapped potential of co-cultivation with diverse microorganisms, including yeast, fungi, and various microalgae species, to augment biomass output. In this context, artificial intelligence (AI) and machine learning (ML) stand out as transformative catalysts. By addressing intricate challenges in wastewater treatment and microalgae-bacteria symbiosis, AI and ML foster innovative technological solutions. These cutting-edge technologies play a pivotal role in optimizing wastewater treatment processes, enhancing biomass yield, and facilitating real-time monitoring. The synergistic integration of AI and ML instills a novel dimension, propelling the fields towards sustainable solutions. As AI and ML become integral tools in wastewater treatment and symbiotic microorganism cultivation, novel strategies emerge that harness their potential to overcome intricate challenges and revolutionize the domain.

Advancements of microalgal upstream technologies: Bioengineering and application aspects in the paradigm of circular bioeconomy

Sustainable energy transition has brought the attention towards microalgae utilization as potential feedstock due to its tremendous capabilities over its predecessors for generating more energy with reduced carbon footprint. However, the commercialization of microalgae feedstock remains debatable due to the various factors and considerations taken into scaling-up the conventional microalgal upstream processes. This review provides a state-of-the-art assessment over the recent developments of available and existing microalgal upstream cultivation systems catered for maximum biomass production. The key growth parameters and main cultivation modes necessary for optimized microalgal growth conditions along with the fundamental aspects were also reviewed and evaluated comprehensively. In addition, the advancements and strategies towards potential scale-up of the microalgal cultivation technologies were highlighted to provide insights for further development into the upstream processes aimed at sustainable circular bioeconomy.

Overview and Challenges of Large-Scale Cultivation of Photosynthetic Microalgae and Cyanobacteria

Microalgae and cyanobacteria are diverse groups of organisms with great potential to benefit societies across the world. These organisms are currently used in food, feed, pharmaceutical and cosmetic industries. In addition, a variety of novel compounds are being isolated. Commercial production of photosynthetic microalgae and cyanobacteria requires cultivation on a large scale with high throughput. However, scaling up production from lab-based systems to large-scale systems is a complex and potentially costly endeavor. In this review, we summarise all aspects of large-scale cultivation, including aims of cultivation, species selection, types of cultivation (ponds, photobioreactors, and biofilms), water and nutrient sources, temperature, light and mixing, monitoring, contamination, harvesting strategies, and potential environmental risks. Importantly, we also present practical recommendations and discuss challenges of profitable large-scale systems associated with economical design, effective operation and maintenance, automation, and shortage of experienced phycologists.

Response Surfaces Method and Artificial Intelligence Approaches for Modeling the Effects of Environmental Factors on Chlorophyll a in Isochrysis galbana

This study reported the condition optimization for chlorophyll a (Chl a) from the microalga Isochrysis galbana. The key parameters affecting the Chl a content of I. galbana were determined by a single-factor optimization experiment. Then the individual and interaction of three factors, including salinity, pH and nitrogen concentration, was optimized by using the method of Box-Benhnken Design. The highest Chl a content (0.51 mg/L) was obtained under the optimum conditions of salinity 30‱ and nitrogen concentration of 72.1 mg/L at pH 8.0. The estimation models of Chl a content based on the response surfaces method (RSM) and three different artificial intelligence models of artificial neural network (ANN), support vector machine (SVM) and radial basis function neural network (RBFNN), were established, respectively. The fitting model was evaluated by using statistical analysis parameters. The high accuracy of prediction was achieved on the ANN, SVM and RBFNN models with correlation coefficients (R2) of 0.9113, 0.9127, and 0.9185, respectively. The performance of these artificial intelligence models depicted better prediction capability than the RSM model for anticipating all the responses. Further experimental results suggested that the proposed SVM and RBFNN model are efficient techniques for accurately fitting the Chl a content of I. galbana and will be helpful in validating future experimental work on the Chl a content by computational intelligence approach.

Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease with high morbidity and mortality, especially in advanced patients. We aimed to develop multi-omics panels of biomarkers for the diagnosis and explore its molecular subtypes.

METHODS

A total of 40 stable patients with advanced COPD and 40 controls were enrolled in the study. Proteomics and metabolomics techniques were applied to identify potential biomarkers. An additional 29 COPD and 31 controls were enrolled for validation of the obtained proteomic signatures. Information on demographic, clinical manifestation, and blood test were collected. The ROC analyses were carried out to evaluate the diagnostic performance, and experimentally validated the final biomarkers on mild-to-moderate COPD. Next, molecular subtyping was performed using proteomics data.

RESULTS

Theophylline, palmitoylethanolamide, hypoxanthine, and cadherin 5 (CDH5) could effectively diagnose advanced COPD with high accuracy (auROC = 0.98, sensitivity of 0.94, and specificity of 0.95). The performance of the diagnostic panel was superior to that of other single/combined results and blood tests. Proteome based stratification of COPD revealed three subtypes (I-III) related to different clinical outcomes and molecular feature: simplex COPD, COPD co-existing with bronchiectasis, and COPD largely co-existing with metabolic syndrome, respectively. Two discriminant models were established using the auROC of 0.96 (Principal Component Analysis, PCA) and 0.95 (the combination of RRM1 + SUPV3L1 + KRT78) in differentiating COPD and COPD with co-morbidities. Theophylline and CDH5 were exclusively elevated in advanced COPD but not in its mild form.

CONCLUSIONS

This integrative multi-omics analysis provides a more comprehensive understanding of the molecular landscape of advanced COPD, which may suggest molecular targets for specialized therapy.

Evaluating the collection and composition of plastic waste in the digital waste bank and the reduction of potential leakage into the ocean

Most ocean plastic pollution results from leakage from waste management activities on land, mainly in coastline communities. In this research, the digitalization of waste management will be evaluated to improve the prevention of leakage. The digitalization means introducing mobile apps into the waste bank that can improve waste management efficiency while providing reliable data. The data on waste management were gained from Griya Luhu App which has been used in 13 villages around Gianyar, while the waste generation was calculated from 97 samples. Then, the villages were categorized by their potential risk of waste leakage based on their distances from the shore. First, the growth of digital waste banks based on the number of units, the number of customers and the amount of waste-managed was analyzed. Second, the composition of waste collected was evaluated. Last, inorganic waste generation (IWG) from digital waste banks was reduced. The results showed that digital waste banks and the customers had grown rapidly in 1 year. The number of waste bank units grew from 0 to 80 with an increase to a total of 5500 customers during the same period with a maximum of 20 tons of waste managed per month. In general, digital waste banks have shown promising performance in preventing waste leakage into the ocean with a 54.04% reduction of IWG. Compared to this reduction percentage, Tulikup as a high-risk village has a considerably low reduction (30.30%) and should be prioritized. Furthermore, the ability to manage a village with a high population/number of customers should be improved.

Microalgae identification: Future of image processing and digital algorithm

The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid, high-accuracy, reliable, low-cost, simple, and state-of-the-art identification methods. Thus, increasing the possibility for the development of potential recognition applications, that could identify toxic-producing and valuable microalgae strains. Recently, deep learning (DL) has brought the study of microalgae species identification to a much higher depth of efficiency and accuracy. In doing so, this review paper emphasizes the significance of microalgae identification, and various forms of machine learning algorithms for image classification, followed by image pre-processing techniques, feature extraction, and selection for further classification accuracy. Future prospects over the challenges and improvements of potential DL classification model development, application in microalgae recognition, and image capturing technologies are discussed accordingly.

Machine learning-guided prediction of potential engineering targets for microbial production of lycopene

The process of designing streamlined workflows for developing microbial strains using classical methods from vast amounts of biological big data has reached its limits. With the continuous increase in the amount of biological big data, data-driven machine learning approaches are being used to overcome the limits of classical approaches for strain development. Here, machine learning-guided engineering of Deinococcus radiodurans R1 for high-yield production of lycopene was demonstrated. The multilayer perceptron models were first trained using the mRNA expression levels of the key genes along with lycopene titers and yields obtained from 17 strains. Then, the potential overexpression targets from 2,047 possible combinations were predicted by the multilayer perceptron combined with a genetic algorithm. Through the machine learning-aided fine-tuning of the predicted genes, the final-engineered LY04 strain resulted in an 8-fold increase in the lycopene production, up to 1.25 g/L from glycerol, and a 6-fold increase in the lycopene yield.

Clustered regularly interspaced short palindromic repeats (CRISPR) technology and genetic engineering strategies for microalgae towards carbon neutrality: A critical review

Carbon dioxide is the major greenhouse gas and regards as the critical issue of global warming and climate changes. The inconspicuous microalgae are responsible for 40% of carbon fixation among all photosynthetic plants along with a higher photosynthetic efficiency and convert the carbon into lipids, protein, pigments, and bioactive compounds. Genetic approach and metabolic engineering are applied to accelerate the growth rate and biomass of microalgae, hence achieve the mission of carbon neutrality. Meanwhile, CRISPR/Cas9 is efficiently to enhance the productivity of high-value compounds in microalgae for it is easier operation, more affordable and is able to regulate multiple genes simultaneously. The genetic engineering strategies provide the multidisciplinary concept to evolute and increase the CO₂ fixation rate through Calvin-Benson-Bassham cycle. Therefore, the technologies, bioinformatics tools, systematic engineering approaches for carbon neutrality and circular economy are summarized and leading one step closer to the decarbonization society in this review.

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

Microalgae biotechnology is a great candidate for carbon neutralization, wastewater treatment and the sustainable production of biofuels and food. Efficient and cost-effective microalgae production depends on highly coordinating the resources used for algal growth. However, dynamic natural disturbances such as culture temperature and sunlight can lead to the poor coordination and waste of resources. Open ponds are the most commonly used commercial microalgal production systems, and enhanced mixing can significantly increase their productivity, but mixing energy can be seriously wasted due to dynamic disturbances, presenting a hindrance to further reducing production costs. Herein, a smart and precise mixing strategy was developed for open ponds in which a paddle wheel's stirring speed for an open pond was smartly and precisely controlled in real time based on dynamic variations in light intensity and culture temperature. The proposed technology achieved the same biomass productivity of Spirulina platensis (8.37 g m^-2 day^-1) as a control with a constant high mixing rate under dynamic disturbances while reducing mixing energy inputs by approximately 30 % compared to the control. This study provides a promising method to address serious resource waste and poor coordination due to dynamic natural disturbances, holding great potential for efficient and cost-effective microalgae production.

Enhanced sustainable integration of CO2 utilization and wastewater treatment using microalgae in circular economy concept

Microalgae have been shown to have a promising potential for CO₂ utilization and wastewater treatment which still faces the challenges of high resource and energy requirements. The implementation of the circular economy concept is able to address the issues that limit the application of microalgae-based technologies. In this review, a comprehensive discussion on microalgae-based CO₂ utilization and wastewater treatment was provided, and the integration of this technology with the circular economy concept, for long-term economic and environmental benefits, was described. Furthermore, technological challenges and feasible strategies towards the improvement of microalgae cultivation were discussed. Finally, necessary regulations and effective policies favoring the implementation of microalgae cultivation into the circular economy were proposed. These are discussed to support sustainable development of microalgae-based bioremediation and bioproduction. This work provides new insights into the implementation of the circular economy concept into microalgae-based CO₂ utilization and wastewater treatment to enhance sustainable production.

Machine Learning Methods Modeling Carbohydrate-Enriched Cyanobacteria Biomass Production in Wastewater Treatment Systems

One-stage production of carbohydrate-enriched microalgae biomass in wastewater is a promising option to obtain biofuels. Understanding the interaction of water quality parameters such as nutrients, carbon, internal carbohydrates, and microbial composition in the culture is crucial for efficient operation and viable large-scale cultivation. Bioprocess models are an essential tool for studying the simultaneous effect of complex factors on carbohydrate accumulation, optimizing the process, and reducing operational costs. In this sense, we use a dataset obtained from an empirical model that analyzed the accumulation of carbohydrates in a single process (simultaneous growth and accumulation) from real wastewater. In this experiment, there were no ideal conditions (limiting nutrient conditions), but rather these limitations are guaranteed by the operating conditions (hydraulic retention times/nutrient or carbon loads). Thus, the model integrates 18 variables that are affected and not only carbohydrates. The effect of these variables directly influences the accumulation of carbohydrates. Therefore, this paper analyzes artificial intelligence (AI) algorithms to develop a model to forecast biomass production in wastewater treatment systems. Carbohydrates were modeled using five artificial intelligence methods: (1) Artificial Neural Networks (ANNs), (2) Convolutional Neural Networks (CNN), (3) Long Short-Term Memory Network (LSTMs), (4) K-Nearest Neighbors (kNN), and (5) Random Forest (RF)). The AI methods allow learning how several components interact and if their combinations work faster than building the physical experiments over the same period of time. After comparing the five learning models, the CNN-1D model obtained the best results with an MSE (Mean Squared Error) = 0.0028. This result shows that the model adequately approximates the system’s dynamics.

Genetic Engineering of Microalgae for Secondary Metabolite Production: Recent Developments, Challenges, and Future Prospects

Microalgae are highly diverse photosynthetic organisms with higher growth rate and simple nutritional requirements. They are evolved with an efficiency to adapt to a wide range of environmental conditions, resulting in a variety of genetic diversity. Algae accounts for nearly half of global photosynthesis, which makes them a crucial player for CO₂ sequestration. In addition, they have metabolic capacities to produce novel secondary metabolites of pharmaceutical, nutraceutical and industrial applications. Studies have explored the inherent metabolic capacities of microalgae with altered growth conditions for the production of primary and secondary metabolites. However, the production of the targeted metabolites at higher rates is not guaranteed just with the inherent genetic potentials. The strain improvement using genetic engineering is possible hope to overcome the conventional methods of culture condition improvements for metabolite synthesis. Although the advanced gene editing tools are available, the gene manipulation of microalgae remains relatively unexplored. Among the performed gene manipulations studies, most of them focus on primary metabolites with limited focus on secondary metabolite production. The targeted genes can be overexpressed to enhance the production of the desired metabolite or redesigning them using the synthetic biology. A mutant (KOR1) rich in carotenoid and lipid content was developed in a recent study employing mutational breeding in microalgae (Kato, Commun. Biol, 2021, 4, 450). There are lot of challenges in genetic engineering associated with large algal diversity but the numerous applications of secondary metabolites make this field of research very vital for the biotech industries. This review, summarise all the genetic engineering studies and their significance with respect to secondary metabolite production from microalgae. Further, current genetic engineering strategies, their limitations and future strategies are also discussed.

Application progress of bioactive compounds in microalgae on pharmaceutical and cosmetics

Microalgae is an autotrophic organism with fast growth, short reproduction cycle, and strong environmental adaptability. In recent years, microalgae and the bioactive ingredients extracted from microalgae are regarded as potential substitutes for raw materials in the pharmaceutical and the cosmetics industry. In this review, the characteristics and efficacy of the high-value components of microalgae are discussed in detail, along with the sources and extraction technologies of algae used to obtain high-value ingredients are reviewed. Moreover, the latest trends in biotherapy based on high-value algae extracts as materials are discussed. The excellent antioxidant properties of microalgae derivatives are regarded as an attractive replacement for safe and environmentally friendly cosmetics formulation and production. Through further studies, the mechanism of microalgae bioactive compounds can be understood better and reasonable clinical trials conducted can safely conclude the compliance of microalgae-derived drugs or cosmetics to be necessary standards to be marketed.

Sequential modelling for carbohydrate and bioethanol production from Chlorella saccharophila CCALA 258: a complementary experimental and theoretical approach for microalgal bioethanol production

Bioethanol production from microalgal biomass is an attractive concept, and theoretical methods by which bioenergy can be produced indicate saving in both time and efficiency. The aim of the present study was to investigate the efficiencies of carbohydrate and bioethanol production by Chlorella saccharophila CCALA 258 using experimental, semiempirical, and theoretical methods, such as response surface methods (RSMs) and an artificial neural network (ANN) through sequential modeling. In addition, the interactive response surface modeling for determining the optimum conditions for the variables was assessed. The results indicated that the maximum bioethanol concentration was 11.20 g/L using the RSM model and 11.17 g/L using the ANN model under optimum conditions of 6% (v/v %) substrate and 4% (v/v %) inoculum at 96-h fermentation, pH 6, and 40 °C. In addition, the value of the experimental data for carbohydrate concentration was 0.2510 g/g biomass at ANN with the maximums of 50% (v/v) wastewater concentration, 4% (m/m) hydrogen peroxide concentration, and 6000 U/mL enzyme activity. Finally, although the RSM model was more effective than the ANN model for predicting bioethanol concentration, the ANN model yielded more precise values than the RSM model for carbohydrate concentration.

Growth kinetics and quantification of carbohydrate, protein, lipids, and chlorophyll of Spirulina platensis under aqueous conditions using different carbon and nitrogen sources

This study evaluated different carbon and nitrogen sources on the growth and production of carbohydrates, protein, lipids, and chlorophyll of Spirulina platensis LEB-52 through an easy successive methodology under aqueous conditions. Spirulina platensis was cultivated at 120 rpm and light intensity of 156 µmol m^-2 s^-1 in a 500 mL Erlenmeyer flask with a working volume of 250 mL, using Zarrouk's medium. The biomass, carbohydrate, and protein production together with the specific growth rate did not show a significant difference between NaHCO₃ and Na₂CO₃. The salts of urea and ammonium are not an alternative nitrogen sources of low cost for Spirulina platensis cultivation. From the experimental results obtained in this study, a successful estimate of carbohydrate, protein, lipids, and chlorophyll content inside Spirulina platensis was achieved without use advanced analytical techniques, allowing saves resources and time. This method can be extrapolated to other microorganisms and cultivation regimens.

Production of biofuels from biomass: Predicting the energy employing artificial intelligence modelling

Bioenergy may be a major replacement of fossil fuels which can make the path easier for sustainable development and decrease the dependency on conventional sources of energy. The main concern with the bioenergy is the availability of feedstock, dealing with its economics as well as its demand and supply chain management. This review deals with the finding of distinct potential of different Artificial Intelligence technologies focusing the challenges in bioenergy production system and its overall improvement in application. The study also highlights the contribution of Artificial Intelligence techniques for the prediction of energy from biomass and evaluates the computing-reasoning techniques for managing bioenergy production, biomass supply chain and optimization of process parameters for efficient bioconversion technologies.

How does the Internet of Things (IoT) help in microalgae biorefinery?

Microalgae biorefinery is a platform for the conversion of microalgal biomass into a variety of value-added products, such as biofuels, bio-based chemicals, biomaterials, and bioactive substances. Commercialization and industrialization of microalgae biorefinery heavily rely on the capability and efficiency of large-scale cultivation of microalgae. Thus, there is an urgent need for novel technologies that can be used to monitor, automatically control, and precisely predict microalgae production. In light of this, innovative applications of the Internet of things (IoT) technologies in microalgae biorefinery have attracted tremendous research efforts. IoT has potential applications in a microalgae biorefinery for the automatic control of microalgae cultivation, monitoring and manipulation of microalgal cultivation parameters, optimization of microalgae productivity, identification of toxic algae species, screening of target microalgae species, classification of microalgae species, and viability detection of microalgal cells. In this critical review, cutting-edge IoT technologies that could be adopted to microalgae biorefinery in the upstream and downstream processing are described comprehensively. The current advances of the integration of IoT with microalgae biorefinery are presented. What this review discussed includes automation, sensors, lab-on-chip, and machine learning, which are the main constituent elements and advanced technologies of IoT. Specifically, future research directions are discussed with special emphasis on the development of sensors, the application of microfluidic technology, robotized microalgae, high-throughput platforms, deep learning, and other innovative techniques. This review could contribute greatly to the novelty and relevance in the field of IoT-based microalgae biorefinery to develop smarter, safer, cleaner, greener, and economically efficient techniques for exhaustive energy recovery during the biorefinery process.

Perspective Design of Algae Photobioreactor for Greenhouses—A Comparative Study

The continued growth and evolving lifestyles of the human population require the urgent development of sustainable production in all its aspects. Microalgae have the potential of the sustainable production of various commodities; however, the energetic requirements of algae cultivation still largely contribute to the overall negative balance of many operation plants. Here, we evaluate energetic efficiency of biomass and lipids production by Chlorella pyrenoidosa in multi-tubular, helical-tubular, and flat-panel airlift pilot scale photobioreactors, placed in an indoor environment of greenhouse laboratory in Central Europe. Our results show that the main energy consumption was related to the maintenance of constant light intensity in the flat-panel photobioreactor and the culture circulation in the helical-tubular photobioreactor. The specific power input ranged between 0.79 W L−1 in the multi-tubular photobioreactor and 6.8 W L−1 in the flat-panel photobioreactor. The construction of multi-tubular photobioreactor allowed for the lowest energy requirements but also predetermined the highest temperature sensitivity and led to a significant reduction of Chlorella productivity in extraordinary warm summers 2018 and 2019. To meet the requirements of sustainable yearlong microalgal production in the context of global change, further development towards hybrid microalgal cultivation systems, combining the advantages of open and closed systems, can be expected.

High-throughput proteomics and metabolomic studies guide re-engineering of metabolic pathways in eukaryotic microalgae: A review

Eukaryotic microalgae are a rich source of commercially important metabolites including lipids, pigments, sugars, amino acids and enzymes. However, their inherent genetic potential is usually not enough to support high level production of metabolites of interest. In order to move on from the traditional approach of improving product yields by modification of the cultivation conditions, understanding the metabolic pathways leading to the synthesis of the bioproducts of interest is crucial. Identification of new targets for strain engineering has been greatly facilitated by the rapid development of high-throughput sequencing and spectroscopic techniques discussed in this review. Despite the availability of high throughput analytical tools, examples of gathering and application of proteomic and metabolomic data for metabolic engineering of microalgae are few and mainly limited to lipid production. The present review highlights the application of contemporary proteomic and metabolomic techniques in eukaryotic microalgae for redesigning pathways for enhanced production of algal metabolites.