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

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.

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.

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.

Big data and machine learning driven bioprocessing - Recent trends and critical analysis

Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.

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.

Trends in digital image processing of isolated microalgae by incorporating classification algorithm

Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.

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.

Biorefinery and sustainability for the production of biofuels and value-added products: A trends analysis based on network and patent analysis

Biorefineries are modern mechanisms used for producing value-added products and biofuels from different biomass sources. However, a crucial challenge is to achieve a sustainable model for their adequate implementation. Challenges related to technical efficiency and economic feasibility are two of the most relevant problems. Therefore, the present study sought to determine the current trends in basic research and technological development around biorefining and sustainability. We carried out a co-occurrence analysis and a patent analysis using data obtained from the Scopus and Lens databases to provide a general overview of the current state of this area of knowledge. The co-occurrence analysis intends to provide an overview of biorefining and sustainability based on terms associated with these two concepts as a starting point to determine the progress and existing challenges of the field. The results of the patent analysis consisted in identifying the main technological sectors, applicants, and territories where inventions associated with biorefining are registered. The analysis of the information showed that bioeconomy, techno-economic aspects, circular economy, technical issues associated with biomass production, and biofuels represent the focal point of basic research in a wide range of disciplines. Technology development is focused on fermentation, enzymes, and microorganisms, among other areas, which shows the validity of these traditional techniques in addressing the problems faced by the bioeconomy. This scenario shows that developed economies are the driving force behind this area of knowledge and that the PCT system is fundamental for the protection and commercialization of these inventions in places different from where they originated. Furthermore, the challenge lies in learning to work in alternative and complementary technological sectors, beyond microbiology and enzyme applications, in pursuit of the sector's technical and economic feasibility.

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

The microalgae-based bioconversion process is a promising carbon utilization technology because it can upgrade CO2 into valuable substances, but a multiplex monitoring system required for process control to maximize biomass productivity has not been well established. Herein, a 3D printed real-time optical density monitoring device (RTOMD) combined platform was presented. This platform enables precise kinetics analysis by maintaining high accuracy (over 95 %) under raucous outdoor conditions. Through RTOMD-based high-frequency measurements, it was observed that maximum biomass productivity of 4.497 g L-1 d-1 was reached, which greatly exceeds the requirements for a feasible microalgae process. We discovered that the CO2 fixation efficiency could be achieved to 70.75 %, indicating the potential of a bioconversion process to realize a carbon-neutral society. Consequently, the RTOMD system can contribute to promoting microalgae cultivation as an attractive carbon mitigation technology based on an improved understanding of the photosynthetic CO2 fixation kinetics.

Design and characterization of a new pressurized flat panel photobioreactor for microalgae cultivation and CO2 bio-fixation

Microalgae-based biorefinery processes are gaining particular importance as a biotechnological tool for direct carbon dioxide fixation and production of high-quality biomass and energy feedstock for different industrial markets. However, despite the many technological advances in photobioreactor designs and operations, microalgae cultivation is still limited due to the low yields achieved in open systems and to the high investment and operation costs of closed photobioreactors. In this work, a new alveolar flat panel photobioreactor was designed and characterized with the aim of achieving high microalgae productivities and CO₂ bio-fixation rates. Moreover, the energy efficiency of the employed pump-assisted hydraulic circuit was evaluated. The 1.3 cm thick alveolar flat-panels enhance the light utilization, whereas the hydraulic design of the photobioreactor aims to improve the global CO₂ gas-liquid mass transfer coefficient (k_LaCO₂). The mixing time, liquid flow velocity, and k_LaCO₂ as well as the uniformity matrix of the artificial lighting source were experimentally calculated. The performance of the system was tested by cultivating the green microalga Acutodesmus obliquus. A volumetric biomass concentration equal to 1.9 g L^-1 was achieved after 7 days under controlled indoor cultivation conditions with a CO₂ bio-fixation efficiency of 64% of total injected CO₂. The (gross) energy consumption related to substrate handling was estimated to be between 27 and 46 Wh m^-3, without any cost associated to CO₂ injection and O₂ degassing. The data suggest that this pilot-scale cultivation system may constitute a relevant technology in the development of microalgae-based industrial scenario for CO₂ mitigation and biomass production.

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.

Monitoring Microalgal Growth of Chlorella minutissima with a New All Solid-state Contact Nitrate Selective Sensor

As third generation feedstock, microalgae are microorganisms that can grow only in the optimum conditions. There are parameters including the concentration of macro and microelements in nutrient solution, pH, temperature, and light intensity that have significant impact on microalgal growth. In recent years, various sensing devices has been developed for sensitive measurement of these parameters during microalgal growth. In this study, a new potentiometric nitrate selective sensor was developed to indicate the nitrate uptake of microalgae and the effect of nitrate nutrient on microalgal growth, specifically, and this sensor was successfully applied to determine nitrate concentration in medium during microalgal growth. Moreover, the effects of nitrate, carbonate and phosphate concentration in the growth medium on biomass production of Chlorella minutissima were investigated by using Box-Behnken design method, and optimum conditions were determined for the highest biomass production of microalgae. As a result of the experiments, it was seen that the highest C. minutissima production was achieved using the medium consist of 2.63 g/L NaNO₃ , 0.35 g/L Na₂ CO₃ and 0.4 g/L KH₂ PO_4. Statistically, it was observed that there was a proportional relationship between the microalgae production and investigated parameters such as carbon, nitrogen and phosphate amounts of culture mediums. The electrode showed a wide linear range between 1.0×10^-1 and 5.0×10^-5 M with a detection limit of the 5×10^-6 M and the response time was found as 10 s. The results showed that developed nitrate selective sensor could be successfully applied for continuous measurement of nitrate in microalgal productions at reduced cost. This article is protected by copyright. All rights reserved.

Smart sustainable biorefineries for lignocellulosic biomass

Lignocellulosic biomass (LCB) is considered as a sustainable feedstock for a biorefinery to generate biofuels and other bio-chemicals. However, commercialization is one of the challenges that limits cost-effective operation of conventional LCB biorefinery. This article highlights some studies on the sustainability of LCB in terms of cost-competitiveness and environmental impact reduction. In addition, the development of computational intelligence methods such as Artificial Intelligence (AI) as a tool to aid the improvement of LCB biorefinery in terms of optimization, prediction, classification, and decision support systems. Lastly, this review examines the possible research gaps on the production and valorization in a smart sustainable biorefinery towards circular economy.

Carotenoid Production from Microalgae: Biosynthesis, Salinity Responses and Novel Biotechnologies

Microalgae are excellent biological factories for high-value products and contain biofunctional carotenoids. Carotenoids are a group of natural pigments with high value in social production and human health. They have been widely used in food additives, pharmaceutics and cosmetics. Astaxanthin, β-carotene and lutein are currently the three carotenoids with the largest market share. Meanwhile, other less studied pigments, such as fucoxanthin and zeaxanthin, also exist in microalgae and have great biofunctional potentials. Since carotenoid accumulation is related to environments and cultivation of microalgae in seawater is a difficult biotechnological problem, the contributions of salt stress on carotenoid accumulation in microalgae need to be revealed for large-scale production. This review comprehensively summarizes the carotenoid biosynthesis and salinity responses of microalgae. Applications of salt stress to induce carotenoid accumulation, potentials of the Internet of Things in microalgae cultivation and future aspects for seawater cultivation are also discussed. As the global market share of carotenoids is still ascending, large-scale, economical and intelligent biotechnologies for carotenoid production play vital roles in the future microalgal economy.

Application of Internet of Things (IoT) for Optimized Greenhouse Environments

This review presents the state-of-the-art research on IoT systems for optimized greenhouse environments. The data were analyzed using descriptive and statistical methods to infer relationships between the Internet of Things (IoT), emerging technologies, precision agriculture, agriculture 4.0, and improvements in commercial farming. The discussion is situated in the broader context of IoT in mitigating the adverse effects of climate change and global warming in agriculture through the optimization of critical parameters such as temperature and humidity, intelligent data acquisition, rule-based control, and resolving the barriers to the commercial adoption of IoT systems in agriculture. The recent unexpected and severe weather events have contributed to low agricultural yields and losses; this is a challenge that can be resolved through technology-mediated precision agriculture. Advances in technology have over time contributed to the development of sensors for frost prevention, remote crop monitoring, fire hazard prevention, precise control of nutrients in soilless greenhouse cultivation, power autonomy through the use of solar energy, and intelligent feeding, shading, and lighting control to improve yields and reduce operational costs. However, particular challenges abound, including the limited uptake of smart technologies in commercial agriculture, price, and accuracy of the sensors. The barriers and challenges should help guide future Research & Development projects and commercial applications.