Smart microalgae farming with internet-of-things for sustainable agriculture

Integrating bicarbonate-based microalgal production with alkaline sewage for ocean negative carbon emissions

Using sewage (wastewater) for ocean alkalinity enhancement (OAE) has been considered as one promising ocean negative carbon emissions (ONCE) approach due to its high carbon sequestration efficiency and low environmental risk. To make this process more profitable and sustainable, this perspective proposes to integrate bicarbonate-based microalgal production and sewage alkalinity enhancement for ONCE. In this concept, the spent aqueous alkaline bicarbonate-based microalgal medium is cheap or even free for OAE, while the produced microalgae with high value-added compositions make this process more profitable. To make the proposed idea more efficient and sustainable, the prospects for its future development are also discussed in this opinion article. This perspective provides a novel and practical idea for achieving efficient carbon neutralization and high economic value simultaneously.

Algal-based bioplastics: global trends in applied research, technologies, and commercialization

The excessive global demand for plastic materials has resulted in severe plastic waste pollution. Conventional plastics derived from non-renewable fossil fuels are non-biodegradable, leading to significant environmental problems. Algal-based bioplastics represent a more viable, renewable, and sustainable alternative to conventional plastics. They have identical properties and characteristics as conventional plastics while being naturally biodegradable. The potential of the algal biomass value chain has already been well-established by researchers. Here, we review the novel insights on research, technology, and commercialization trends of algal-based bioplastics, encompassing macroalgae and green microalgae/cyanobacteria. Data showed that within the last decade, there has been substantial interest in utilizing microalgae for biopolymer production, with more focus on using cyanobacterial species compared to green algae. Moreover, most of the research conducted has largely focused on the production of PHA or its co-polymers. Since 2011, there have been a total of 55 patents published related to algal-based bioplastics production. To date, ~ 81 entities worldwide (commercial and private businesses) produce bioplastics from algae. Overall results of this study emphasized that even with the economic and social challenges, algae possess a substantial potential for the sustainable development of bioplastics while also addressing the UN's SDGs.

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.

Non-destructive monitoring of microalgae biofilms

Biofilm-based cultivation systems are emerging as a promising technology for microalgae production. However, efficient and non-invasive monitoring routines are still lacking. Here, a protocol to monitor microalgae biofilms based on reflectance indices (RIs) is proposed. This framework was developed using a rotating biofilm system for astaxanthin production by cultivating Haematococcus pluvialis on cotton carriers. Biofilm traits such as biomass, astaxanthin, and chlorophyll were characterized under different light and nutrient regimes. Reflectance spectra were collected to identify the spectral bands and the RIs that correlated the most with those biofilm traits. Robust linear models built on more than 170 spectra were selected and validated on an independent dataset. Astaxanthin content could be precisely predicted over a dynamic range from 0 to 4% of dry weight, regardless of the cultivation conditions. This study demonstrates the strength of reflectance spectroscopy as a non-invasive tool to improve the operational efficiency of microalgae biofilm-based technology.

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.

Cultivation of microalgae-bacteria consortium by waste gas-waste water to achieve CO2 fixation, wastewater purification and bioproducts production

The cultivation of microalgae and microalgae-bacteria consortia provide a potential efficient strategy to fix CO2 from waste gas, treat wastewater and produce value-added products subsequently. This paper reviews recent developments in CO2 fixation and wastewater treatment by single microalgae, mixed microalgae and microalgae-bacteria consortia, as well as compares and summarizes the differences in utilizing different microorganisms from different aspects. Compared to monoculture of microalgae, a mixed microalgae and microalgae-bacteria consortium may mitigate environmental risk, obtain high biomass, and improve the efficiency of nutrient removal. The applied microalgae include Chlorella sp., Scenedesmus sp., Pediastrum sp., and Phormidium sp. among others, and most strains belong to Chlorophyta and Cyanophyta. The bacteria in microalgae-bacteria consortia are mainly from activated sludge and specific sewage sources. Bioengineer in CBB cycle in microalgae cells provide effective strategy to achieve improvement of CO2 fixation or a high yield of high-value products. The mechanisms of CO2 fixation and nutrient removal by different microbial systems are also explored and concluded, the importance of microalgae in the technology is proven. After cultivation, microalgae biomass can be harvested through physical, chemical, biological and magnetic separation methods and used to produce high-value by-products, such as biofuel, feed, food, biochar, fertilizer, and pharmaceutical bio-compounds. Although this technology has brought many benefits, some challenging obstacles and limitation remain for industrialization and commercializing.

Spirulina Cultivation Under Different light-emitting Diodes for Boosting Biomass and Protein Production

Microalgae biomass and pigments have a high economic value due to their many biological and commercial applications. In this sense, Spirulina platensis was grown under different (LEDs) light-emitting diodes. The current examination aims to increase the biomass production of S. platensis by formulating an optimal growth condition under different LED lights. Light-emitting diodes have a precise wavelength that has an encouraging effect on microalgae biomass production. For this purpose, the light intensity of 3000 lx was used to illuminate the culture medium, resulting in enhanced S. platensis biomass production. The highest optical density of 0.576 and dry cell weight of 0.343 g/L was recorded for the white light-emitting diode, and the red light-emitting diode, the optical density of 0.479 and dry cell weight of 0.321 g/L was recorded. The highest protein content of 66.10 ± 0.44% was registered with a blue light-emitting diode, followed by a white light-emitting diode with a protein content of 60.86 ± 0.39%. This research is an essential step in defining the light condition that might be useful to increase the biomass production of S. platensis. The study's findings demonstrated that exposure to various light-emitting diode colors could enhance both the quality and quantity of biomass produced in S. platensis cultures and encourage the use of light-emitting diodes as a light source for S. platensis farming without any undesirable effects on growth.

Deciphering Microalgal Diversity of Peculiar Lentic Ecosystem in Chennai, South India: A Way Towards Sustainability

Microalgae are quickly gaining attention among the researchers in various aspects such as biofuel applications, biogas, biomass production, waste water treatment, carbon fixation, animal feed ingredients, pigment production, and pharmaceutical products. One of the approaches to choose microalgae for biotechnological applications is to investigate their diversity and abundance in all possible wet environments. Samples were collected from three sampling sites for the period of 1 year (October 2021-September 2022) in Vadapalani temple tank at Chennai. Physicochemical parameters in current investigation were estimated according to APHA, 2017. Qualitative and quantitative analyses of phytoplankton were done throughout the study period. One-way ANOVA (Analyses of Variance) and Karl Pearson's correlation coefficient were estimated using SPSS (V.26.0). A total of 11 diversity indices were estimated using PAST (V 4.0). A total of 52 algal species were identified, prevailed over by Chlorophyceae (15 species), followed by Zygnematophyceae, Bacillariophyceae, Cyanophyceae, and Trebouxiophyceae. Chlorophyceae quantitatively structured the major category. The maximum and minimum values of density were observed during the season of summer (287 Cells/L) and monsoon (80 Cells/L), respectively. Chlorophyceae showed dominance with a density of 168 cells/L. The maximum and minimum densities of Chlorophyceae were recorded in the summer season (55 cells/L) and monsoon season (24 cells/L), respectively. Shannon's index (H') attained its zenith in February and April month of 2022 (3.60). This study further ignites the researchers to phycoprospect various temple water to address the nature of microalgae occurrence and for biotechnological purposes.

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.

Study on the influence mechanism of adoption of smart agriculture technology behavior

Smart agricultural (SA) technology has become a technological support for modern agriculture. By exploring the decision-making process and psychological motivation of farmers in adopting SA technology, it is conducive to achieving the popularisation of SA technology and promoting the modernisation of agriculture. Based on microscopic research data, a Structural Equation Model (SEM) is used to analyse the influencing factors and extent of cotton farmers' adoption of SA technologies, using Deconstructive Theory of Planned Behavior (DTPB) as the analytical framework. This was combined with in-depth interviews to further reveal the motivations and influencing mechanisms of cotton farmers' adoption of SA technologies. The results show that under the behavioural belief dimension, cotton farmers value the positive effect of perceived usefulness even though the risk of the technology itself has a dampening effect on adoption intentions. Under the normative belief dimension, superior influence influenced the willingness to adopt SA technologies to a greater extent than peer influence. Under the control belief dimension, factors such as self-efficacy and information channels influence willingness to adopt technology and behaviour. In addition, behavioural attitudes, subjective norms, and perceived behavioural control all contribute to cotton farmers' willingness to adopt SA technologies, and can also influence behaviour directly or indirectly through willingness to adopt. Policy and technology satisfaction positively moderate the transition from willingness to behaviour. Therefore, preferential policies are proposed to reduce the cost of adopting SA technologies; to continuously improve the level of SA technologies; to establish SA technology test plots to provide a reference base; and to increase knowledge training on SA and expand access to information.

Recent advances in CO2 fixation by microalgae and its potential contribution to carbon neutrality

Many countries and regions have set their schedules to achieve the carbon neutrality between 2030 and 2070. Microalgae are capable of efficiently fixing CO₂ and simultaneously producing biomass for multiple applications, which is considered one of the most promising pathways for carbon capture and utilization. This work reviews the current research on microalgae CO₂ fixation technologies and the challenges faced by the related industries and government agencies. The technoeconomic analysis indicates that cultivation is the major cost factor. Use of waste resources such as wastewater and flue gas can significantly reduce the costs and carbon footprints. The life cycle assessment has identified fossil-based electricity use as the major contributor to the global warming potential of microalgae-based CO₂ fixation approach. Substantial efforts and investments are needed to identify and bridge the gaps among the microalgae strain development, cultivation conditions and systems, and use of renewable resources and energy.

Smart systems in producing algae-based protein to improve functional food ingredients industries

Production and extraction systems of algal protein and handling process of functional food ingredients need to control several parameters such as temperature, pH, intensity, and turbidity. Many researchers have investigated the Internet of Things (IoT) approach for enhancing the yield of microalgae biomass and machine learning for identifying and classifying microalgae. However, there have been few specific studies on using IoT and artificial intelligence (AI) for production and extraction of algal protein as well as functional food ingredients processing. In order to improve the production of algal protein and functional food ingredients, the implementation of smart system is a must to have real-time monitoring, remote control system, quick response to sudden events, prediction and characterisation. Techniques of IoT and AI are expected to help functional food industries to have a big breakthrough in the future. Manufacturing and implementation of beneficial smart systems are important to provide convenience and to increase the efficiency of work by using the interconnectivity of IoT devices to have good capturing, processing, archiving, analyzing, and automation. This review investigates the possibilities of implementation of IoT and AI in production and extraction of algal protein and processing of functional food ingredients.

Analysis of Microalgal Density Estimation by Using LASSO and Image Texture Features

Monitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively.

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.

Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities

Among the most relevant themes of modernity, using renewable resources to produce biofuels attracts several countries' attention, constituting a vital part of the global geopolitical chessboard since humanity's energy needs will grow faster and faster. Fortunately, advances in personal computing associated with free and open-source software production facilitate this work of prospecting and understanding complex scenarios. Thus, for the development of this work, the keywords "biofuel" and "nanocatalyst" were delivered to the Scopus database, which returned 1071 scientific articles. The titles and abstracts of these papers were saved in Research Information Systems (RIS) format and submitted to automatic analysis via the Visualization of Similarities Method implemented in VOSviewer 1.6.18 software. Then, the data extracted from the VOSviewer were processed by software written in Python, which allowed the use of the network data generated by the Visualization of Similarities Method. Thus, it was possible to establish the relationships for the pair between the nodes of all clusters classified by Link Strength Between Items or Terms (LSBI) or by year. Indeed, other associations should arouse particular interest in the readers. However, here, the option was for a numerical criterion. However, all data are freely available, and stakeholders can infer other specific connections directly. Therefore, this innovative approach allowed inferring that the most recent pairs of terms associate the need to produce biofuels from microorganisms' oils besides cerium oxide nanoparticles to improve the performance of fuel mixtures by reducing the emission of hydrocarbons (HC) and oxides of nitrogen (NOx).

Data-driven analysis on immobilized microalgae system: New upgrading trends for microalgal wastewater treatment

Microalgal immobilization is receiving increasing attention as one of the most viable alternatives for upgrading conventional wastewater treatment. However, an in-depth discussion of the state-of-the-art and limitations of available technologies is currently lacking. More importantly, the reason for the hesitant development of immobilized microalgae for wastewater treatment remains unclear, which hinders its practical application. Thus, comprehensively understanding and evaluating details on immobilized microalgae is urgently needed, especially for the current advances of immobilization of microalgae in wastewater treatment over the last few decades. In this review, scientometric approach is used to explore research hotspots and visualize emerging trends. Data-driven analysis is used to scientifically and methodically determine hotspots in the current research on immobilized microalgal wastewater treatment, along with that the implicit inner connection underlying the frequent co-occurring terms was explored in depth. Four hotspots focusing on immobilized microalgae for wastewater treatment were identified, mainly demonstrating: (1) main factors including light, temperature and immobilization methods would majorly affect the treatment performance of immobilized microalgae; (2) immobilized microalgae membrane bioreactor, immobilized microalgae-based microbial fuel cell and immobilized microalgae-based bed reactor are three dominant treatment systems; (3) immobilized microalgae have a higher robustness and tolerance for treating various types of wastewater; and (4) a complete sustainable circle from wastewater treatment to resource conversion via the immobilized microalgae can be achieved. Finally, several new directions and new perspectives that expose the necessity for fulfilling further research and fundamental gaps are pointed out. Taken together, this review provides helpful information to facilitate the development of innovative and feasible immobilized microalgal technologies thus increasing their viability and sustainability.

Microalgal cultivation for the upgraded biogas by removing CO2, coupled with the treatment of slurry from anaerobic digestion: A review

Biogas is the gaseous by product generated from anaerobic digestion (AD), which is mainly composed of methane and CO2. Numerous independent studies have suggested that microalgae cultivation could achieve high efficiency for nutrient uptake or CO2 capture from AD, respectively. However, there is no comprehensive review on the purifying slurry from AD and simultaneously upgrading biogas via microalgal cultivation technology. This paper aims to fill this gap by presenting and discussing an information integration system based on microalgal technology. Furthermore, the review elaborates the mechanisms, configurations, and influencing factors of integrated system and analyzes the possible challenges for practical engineering applications and provides some feasibility suggestions eventually. There is hope that this review will offer a worthwhile and practical guideline to researchers, authorities and potential stakeholders, to promote this industry for sustainable development.

A low-cost system for monitoring pH, dissolved oxygen and algal density in continuous culture of microalgae

In a continuous and closed system of culturing microalgae, constantly monitoring and controlling pH, dissolved oxygen (DO) and microalgal density in the cultivation environment are paramount, which ultimately influence on the growth rate and quality of the microalgae products. Apart from the pH and DO parameters, the density of microalgae can be used to contemplate what light condition in the culture chamber is or when nutrients should be supplemented, which both also decide productivity of the cultivation. Moreover, the microalgal density is considered as an indicator indicating when the microalgae can be harvested. Therefore, this work proposes a low-cost monitoring equipment that can be employed to observe pH, DO and microalgal density over time in a culture environment. The measurements obtained by the proposed monitoring device can be utilized for not only real-time observations but also controlling other sub-systems in a continuous culture model including stirring, ventilating, nutrient supplying and harvesting, which leads to more efficiency in the microalgal production. More importantly, it is proposed to utilize the off-the-shelf materials to fabricate the equipment with a total cost of about 513 EUR, which makes it practical as well as widespread. The proposed monitoring apparatus was validated in a real-world closed system of cultivating a microalgae strain of Chlorella vulgaris. The obtained results indicate that the measurement accuracies are 0.3%, 3.8% and 8.6% for pH, DO and microalgae density quantities, respectively.

Unsupervised feature selection based on incremental forward iterative Laplacian score

Feature selection facilitates intelligent information processing, and the unsupervised learning of feature selection has become important. In terms of unsupervised feature selection, the Laplacian score (LS) provides a powerful measurement and optimization method, and good performance has been achieved using the recent forward iterative Laplacian score (FILS) algorithm. However, there is still room for advancement. The aim of this paper is to improve the FILS algorithm, and thus, feature significance (SIG) is mainly introduced to develop a high-quality selection method, i.e., the incremental forward iterative Laplacian score (IFILS) algorithm. Based on the modified LS, the metric difference in the incremental feature process motivates SIG. Therefore, SIG offers a dynamic characterization by considering initial and terminal states, and it promotes the current FILS measurement on only the terminal state. Then, both the modified LS and integrated SIG acquire granulation nonmonotonicity and uncertainty, especially on incremental feature chains, and the corresponding verification is achieved by completing examples and experiments. Furthermore, a SIG-based incremental criterion of minimum selection is designed to choose optimization features, and thus, the IFILS algorithm is naturally formulated to implement unsupervised feature selection. Finally, an in-depth comparison of the IFILS algorithm with the FILS algorithm is achieved using data experiments on multiple datasets, including a nominal dataset of COVID-19 surveillance. As validated by the experimental results, the IFILS algorithm outperforms the FILS algorithm and achieves better classification performance.

Global market and economic analysis of microalgae technology: Status and perspectives

Microalgae have been a promising alternative source of high-value compounds to replace the non-sustainable fossil fuels resource. The recent research development of algae-based bioproducts has remarkable impact various industries section for its renewability, efficiency, and environmentally friendly crops over those synthetic-made product. However, by utilizing microalgae biomass toward their full potential is still limited due to lack of research funding, social acceptability and challenges in policy implementation. This present review highlights the various microalgae biotechnology with consideration of economical aspect for the global potential of algae market, comparison between the microalgae market in Malaysia and international countries. In addition, the cultivation technologies and feasibility of microalgae biomass production globally, followed by insightful challenges and future development of microalgae industry are mentioned. The current study will contribute to the understanding of upstream and downstream of microalgae processing along with technical economical understandings for the successful commercialisation of microalgae products.