Real-time monitoring and automatic density control of large-scale microalgal cultures using near infrared (NIR) optical density sensors

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.

A rapid fluorescence approach on differentiation of typical dinoflagellate of East China Sea

Detecting the marine phytoplankton by the means of absorption or fluorescence spectra were successfully deployed in the past decades, however, the differentiation are mainly limited in levels of class, such as bacillariophytas, dinophytas, raphidophytes, chlorophytes, cyanobacteria, etc. which are characterized by their specific composition of photosynthetic pigments. To further differentiate the typical dinoflagellate Prorocentrum donghaiense, Amphidinium carterae, Scrippsiella trochoidea, Karenia mikimotoi out of the common diatom Skeletonema costatum and haptonema Phaeocystis globosa at East China Sea, a rapid 3D-fluorescence method equipped with CHEMTAX model were conducted. Initial fluorescence excitation spectra of each algal species (under variable environmental conditions) were captured by 3D-fluorometer first. Then fingerprints of each algae were characterized by ten-point discrete excitation spectrum with the excitation wavelengths of 405, 420, 435, 470, 490, 505, 535, 555, 570 and 590 nm, which closely reflecting the difference of photosynthetic pigments. By equipping with CHEMTAX model, the standard spectra and norm spectra were constructed for FS-CHEMTAX (Fluorescence spectra-CHEMTAX) model to further identify the algal species and estimate the cell density. The developed method performed a better way of identifying the toxic species Amphidinium carterae, Phaeocystis globosa, and Karenia mikimotoi out of the non-toxic ones, with the identification accuracy rates of 83.3%, 90% and 100%, in monocultures, and 77.8%, 90% and 100%, in the bi-mixed cultures, respectively. Meanwhile, the detection limits for the three toxic species were found as low as 250, 1,400 and 120 cells/mL. The concentrations estimated are in good agreement with the microscopic cell counts for all the algae groups (correlation coefficients (R²) exceed 0.8). The relative error of predict concentration was lowest for small cells, i.e., Phaeocystis globosa (10.0%) and Amphidinium carterae (21.1%), but the highest for big cells, i.e. Karenia mikimotoi (41.8%) when the target algae become the dominant species. The overall concentration detection error was no more than one order of magnitude, indicating that this method could provide an important technical support for monitoring the related harmful algal blooms.

Rapid in measurements of brown tide algae cell concentrations using fluorescence spectrometry and generalized regression neural network

The frequent occurrence of brown tide pollution in recent years has brought great losses to the economy of coastal areas. Therefore, accurate and efficient detection of the brown tide algae cell concentration is of great significance to the prevention of marine environmental pollution. In this paper, a combination of three-dimensional fluorescence spectroscopy and generalized regression neural network is used to detect the concentration of Aureococcus anophagefferens (A. anophagefferens). Firstly, the fluorescence spectrometer was used to collect spectra of A. anophagefferens with different growth cycles and different concentrations. In order to reduce the complexity of fluorescence spectral data and improve the efficiency of model calculation, the gradient boosting decision tree (GBDT) algorithm is used to rank the importance of spectral features, and select spectral features with great importance as input and concentration of algal cells as output. In view of the nonlinear relationship between input and output, a generalized regression neural network model optimized by the improved sparrow search algorithm (FASSA-GRNN) was established to predict the concentration of algae cells, The model results show that MSE is 0.0046, MAE is 0.0569, and R² is 0.9955. In addition, the FASSA-GRNN model is compared with the prediction results of the SSA-GRNN, GWO-GRNN, and GRNN models. The results show that the prediction accuracy of FASSA-GRNN is better than other models, and the improved sparrow search algorithm (FASSA) can reach the global optimum faster during the training process. This research provides a new method for predicting the concentration of algae cells.

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

Agriculture farms such as crop, aquaculture and livestock have begun the implementation of Internet of Things (IoT) and artificial intelligence (AI) technology in improving their productivity and product quality. However, microalgae farming which requires precise monitoring, controlling and predicting the growth of microalgae biomass has yet to incorporate with IoT and AI technology, as it is still in its infancy phase. Particularly, the cultivation stage of microalgae involves many essential parameters (i.e. biomass concentration, pH, light intensity, temperature and tank level) which require precise monitoring as these parameters are important to ensure an effective biomass productivity in the microalgae farming. Besides, the conventional practices in the current process equipment are still powered by electricity, thus further development by integrating IoT into these processes can ease the production process. Further to that, many researchers has studied the machine learning approach for the identification and classification of microalgae. However, there are still limited studies reported on applying machine learning for the application of microalgae industry such as optimising microalgae cultivation for higher biomass productivity. Therefore, the implementation of IoT and AI in microalgae farming can contribute to the development of the global microalgae industry. The purpose of this current review paper focuses on the overview microalgae biomass production process along with the implementation of IoT toward the future of smart farming. To bridge the gap between the conventional and microalgae smart farming, this paper also highlights the insights on the implementation phases of microalgae smart farming starting from the infant stage that involves the installation and programming of IoT hardware. Then, it is followed by the application of machine learning to predict and auto-optimise the microalgae smart farming process. Furthermore, the process setup and detailed overview of microalgae farming with the integration of IoT have been discussed critically. This review paper would provide a new vision of microalgae farming for microalgae researchers and bio-processing industries into the digitalisation industrial era.

Perspectives of fluorescence spectroscopy for online monitoring in microalgae industry

Microalgae industrial production is viewed as a solution for alternative production of nutraceuticals, cosmetics, biofertilizers, and biopolymers. Throughout the years, several technological advances have been implemented, increasing the competitiveness of microalgae industry. However, online monitoring and real-time process control of a microalgae production factory still require further development. In this mini-review, non-destructive tools for online monitoring of cellular agriculture applications are described. Still, the focus is on the use of fluorescence spectroscopy to monitor several parameters (cell concentration, pigments, and lipids) in the microalgae industry. The development presented makes it the most promising solution for monitoring up-and downstream processes, different biological parameters simultaneously, and different microalgae species. The improvements needed for industrial application of this technology are also discussed.

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.

Application of In-Situ and Soft-Sensors for Estimation of Recombinant P. pastoris GS115 Biomass Concentration: A Case Analysis of HBcAg (Mut+) and HBsAg (MutS) Production Processes under Varying Conditions

Microbial biomass concentration is a key bioprocess parameter, estimated using various labor, operator and process cross-sensitive techniques, analyzed in a broad context and therefore the subject of correct interpretation. In this paper, the authors present the results of P. pastoris cell density estimation based on off-line (optical density, wet/dry cell weight concentration), in-situ (turbidity, permittivity), and soft-sensor (off-gas O₂/CO₂, alkali consumption) techniques. Cultivations were performed in a 5 L oxygen-enriched stirred tank bioreactor. The experimental plan determined varying aeration rates/levels, glycerol or methanol substrates, residual methanol levels, and temperature. In total, results from 13 up to 150 g (dry cell weight)/L cultivation runs were analyzed. Linear and exponential correlation models were identified for the turbidity sensor signal and dry cell weight concentration (DCW). Evaluated linear correlation between permittivity and DCW in the glycerol consumption phase (<60 g/L) and medium (for Mut⁺ strain) to significant (for Mut^S strain) linearity decline for methanol consumption phase. DCW and permittivity-based biomass estimates used for soft-sensor parameters identification. Dataset consisting from 4 Mut⁺ strain cultivation experiments used for estimation quality (expressed in NRMSE) comparison for turbidity-based (8%), permittivity-based (11%), O₂ uptake-based (10%), CO₂ production-based (13%), and alkali consumption-based (8%) biomass estimates. Additionally, the authors present a novel solution (algorithm) for uncommon in-situ turbidity and permittivity sensor signal shift (caused by the intensive stirrer rate change and antifoam agent addition) on-line identification and minimization. The sensor signal filtering method leads to about 5-fold and 2-fold minimized biomass estimate drifts for turbidity- and permittivity-based biomass estimates, respectively.

Prospects and development of algal-bacterial biotechnology in environmental management and protection

The coexistence of algae and bacteria in nature dates back to the very early stages when life came into existence. The interaction between algae and bacteria plays an important role in the planet ecology, cycling nutrients, and feeding higher trophic levels, and have been evolving ever since. The emerging concept of algal-bacterial consortia is gaining attention, much towards environmental management and protection. Studies have shown that algal-bacterial synergy does not only promote carbon capture in wastewater bioremediation but also consequently produces biofuels from algal-bacterial biomass. This review has evaluated the optimistic prospects of algal-bacterial consortia in environmental remediation, biorefinery, carbon sequestration as well as its contribution to the production of high-value compounds. In addition, algal-bacterial consortia offer great potential in bloom control, dye removal, agricultural biofertilizers, and bioplastics production. This work also emphasizes the advancement of algal-bacterial biotechnology in environmental management through the incorporation of Industry Revolution 4.0 technologies. The challenges include its pathway to greener industry, competition with other food additive sources, societal acceptance, cost feasibility, environmental trade-off, safety and compatibility. Thus, there is a need for further in-depth research to ensure the environmental sustainability and feasibility of algal-bacterial consortia to meet numerous current and future needs of society in the long run.

Methods for quantification of growth and productivity in anaerobic microbiology and biotechnology

Anaerobic microorganisms (anaerobes) possess a fascinating metabolic versatility. This characteristic makes anaerobes interesting candidates for physiological studies and utilizable as microbial cell factories. To investigate the physiological characteristics of an anaerobic microbial population, yield, productivity, specific growth rate, biomass production, substrate uptake, and product formation are regarded as essential variables. The determination of those variables in distinct cultivation systems may be achieved by using different techniques for sampling, measuring of growth, substrate uptake, and product formation kinetics. In this review, a comprehensive overview of methods is presented, and the applicability is discussed in the frame of anaerobic microbiology and biotechnology.

The Barnacle Balanus improvisus as a Marine Model - Culturing and Gene Expression

Barnacles are marine crustaceans with a sessile adult and free-swimming, planktonic larvae. The barnacle Balanus (Amphibalanus) improvisus is particularly relevant as a model for the studies of osmoregulatory mechanisms because of its extreme tolerance to low salinity. It is also widely used as a model of settling biology, in particular in relation to antifouling research. However, natural seasonal spawning yields an unpredictable supply of cyprid larvae for studies. A protocol for the all-year-round culturing of B. improvisus has been developed and a detailed description of all steps in the production line is outlined (i.e., the establishment of adult cultures on panels, the collection and rearing of barnacle larvae, and the administration of feed for adults and larvae). The description also provides guidance on troubleshooting and discusses critical parameters (e.g., the removal of contamination, the production of high-quality feed, the manpower needed, and the importance of high-quality seawater). Each batch from the culturing system maximally yields roughly 12,000 nauplii and can deliver four batches in a week, so up to almost 50,000 larvae per week can be produced. The method used to culture B. improvisus is, probably, to a large extent also applicable to other marine invertebrates with free-swimminglarvae. Protocols are presented for the dissection of various tissues from adults as well as the production of high-quality RNA for studies on gene expression. It is also described how cultured adults and reared cyprids can be utilized in a wide array of experimental designs for examining gene expression in relation to external factors. The use of cultured barnacles in gene expression is illustrated with studies of possible osmoregulatory roles of Na⁺/K⁺ ATPase and aquaporins.

Advances in techniques for assessment of microalgal lipids

Microalgae are a varied group of organisms with considerable commercial potential as sources of various biochemicals, storage molecules and metabolites such as lipids, sugars, amino acids, pigments and toxins. Algal lipids can be processed to bio-oils and biodiesel. The conventional method to estimate algal lipids is based on extraction using solvents and quantification by gravimetry or chromatography. Such methods are time consuming, use hazardous chemicals and are labor intensive. For rapid screening of prospective algae or for management decisions (e.g. decision on timing of harvest), a rapid, high throughput, reliable, accurate, cost effective and preferably nondestructive analytical technique is desirable. This manuscript reviews the application of fluorescent lipid soluble dyes (Nile Red and BODIPY 505/515), nuclear magnetic resonance (NMR), Raman, Fourier transform infrared (FTIR) and near infrared (NIR) spectroscopy for the assessment of lipids in microalgae.

Multi-Wavelength Based Optical Density Sensor for Autonomous Monitoring of Microalgae

A multi-wavelength based optical density sensor unit was designed, developed, and evaluated to monitor microalgae growth in real time. The system consisted of five main components including: (1) laser diode modules as light sources; (2) photodiodes as detectors; (3) driver circuit; (4) flow cell; and (5) sensor housing temperature controller. The sensor unit was designed to be integrated into any microalgae culture system for both real time and non-real time optical density measurements and algae growth monitoring applications. It was shown that the sensor unit was capable of monitoring the dynamics and physiological changes of the microalgae culture in real-time. Algae biomass concentration was accurately estimated with optical density measurements at 650, 685 and 780 nm wavelengths used by the sensor unit. The sensor unit was able to monitor cell concentration as high as 1.05 g·L⁻¹ (1.51 × 10⁸ cells·mL⁻¹) during the culture growth without any sample preparation for the measurements. Since high cell concentrations do not need to be diluted using the sensor unit, the system has the potential to be used in industrial microalgae cultivation systems for real time monitoring and control applications that can lead to improved resource use efficiency.

Unlocking nature's treasure-chest: screening for oleaginous algae

Micro-algae synthesize high levels of lipids, carbohydrates and proteins photoautotrophically, thus attracting considerable interest for the biotechnological production of fuels, environmental remediation, functional foods and nutraceuticals. Currently, only a few micro-algae species are grown commercially at large-scale, primarily for “health-foods” and pigments. For a range of potential products (fuel to pharma), high lipid productivity strains are required to mitigate the economic costs of mass culture. Here we present a screen concentrating on marine micro-algal strains, which if suitable for scale-up would minimise competition with agriculture for water. Mass-Spectrophotometric analysis (MS) of nitrogen (N) and carbon (C) was subsequently validated by measurement of total fatty acids (TFA) by Gas-Chromatography (GC). This identified a rapid and accurate screening strategy based on elemental analysis. The screen identified Nannochloropsis oceanica CCAP 849/10 and a marine isolate of Chlorella vulgaris CCAP 211/21A as the best lipid producers. Analysis of C, N, protein, carbohydrate and Fatty Acid (FA) composition identified a suite of strains for further biotechnological applications e.g. Dunaliella polymorpha CCAP 19/14, significantly the most productive for carbohydrates, and Cyclotella cryptica CCAP 1070/2, with utility for EPA production and N-assimilation.

Modelling of Microalgae Culture Systems with Applications to Control and Optimization

Mathematical modeling is becoming ever more important to assess the potential, guide the design, and enable the efficient operation and control of industrial-scale microalgae culture systems (MCS). The development of overall, inherently multiphysics, models involves coupling separate submodels of (i) the intrinsic biological properties, including growth, decay, and biosynthesis as well as the effect of light and temperature on these processes, and (ii) the physical properties, such as the hydrodynamics, light attenuation, and temperature in the culture medium. When considering high-density microalgae culture, in particular, the coupling between biology and physics becomes critical. This chapter reviews existing models, with a particular focus on the Droop model, which is a precursor model, and it highlights the structure common to many microalgae growth models. It summarizes the main developments and difficulties towards multiphysics models of MCS as well as applications of these models for monitoring, control, and optimization purposes.

Monitoring of Microalgal Processes

Process monitoring, which can be defined as the measurement of process variables with the smallest possible delay, is combined with process models to form the basis for successful process control. Minimizing the measurement delay leads inevitably to employing online, in situ sensors where possible, preferably using noninvasive measurement methods with stable, low-cost sensors. Microalgal processes have similarities to traditional bioprocesses but also have unique monitoring requirements. In general, variables to be monitored in microalgal processes can be categorized as physical, chemical, and biological, and they are measured in gaseous, liquid, and solid (biological) phases. Physical and chemical process variables can be usually monitored online using standard industrial sensors. The monitoring of biological process variables, however, relies mostly on sensors developed and validated using laboratory-scale systems or uses offline methods because of difficulties in developing suitable online sensors. Here, we review current technologies for online, in situ monitoring of all types of process parameters of microalgal cultivations, with a focus on monitoring of biological parameters. We discuss newly introduced methods for measuring biological parameters that could be possibly adapted for routine online use, should be preferably noninvasive, and are based on approaches that have been proven in other bioprocesses. New sensor types for measuring physicochemical parameters using optical methods or ion-specific field effect transistor (ISFET) sensors are also discussed. Reviewed methods with online implementation or online potential include measurement of irradiance, biomass concentration by optical density and image analysis, cell count, chlorophyll fluorescence, growth rate, lipid concentration by infrared spectrophotometry, dielectric scattering, and nuclear magnetic resonance. Future perspectives are discussed, especially in the field of image analysis using in situ microscopy, infrared spectrophotometry, and software sensor systems.

Spectroradiometric monitoring for open outdoor culturing of algae and cyanobacteria

We assess the measurement of hyperspectral reflectance for outdoor monitoring of green algae and cyanobacteria cultures with a multichannel, fiber-coupled spectroradiometer. Reflectance data acquired over a 4-week period are interpreted via numerical inversion of a reflectance model, in which the above-water reflectance is expressed as a quadratic function of the single backscattering albedo, which is dependent on the absorption and backscatter coefficients. The absorption coefficient is treated as the sum of component spectra consisting of the cultured species (green algae or cyanobacteria), dissolved organic matter, and water (including the temperature dependence of the water absorption spectrum). The backscatter coefficient is approximated as the scaled Hilbert transform of the culture absorption spectrum with a wavelength-independent vertical offset. Additional terms in the reflectance model account for the pigment fluorescence features and the water-surface reflection of sunlight and skylight. For the green algae and cyanobacteria, the wavelength-independent vertical offset of the backscatter coefficient is found to scale linearly with daily dry weight measurements, providing the capability for a nonsampling measurement of biomass in outdoor ponds. Other fitting parameters in the reflectance model are compared with auxiliary measurements and physics-based calculations. The model-derived magnitudes of sunlight and skylight water-surface reflections compare favorably with Fresnel reflectance calculations, while the model-derived quantum efficiency of Chl-a fluorescence is found to be in agreement with literature values. Finally, the water temperatures derived from the reflectance model exhibit excellent agreement with thermocouple measurements during the morning hours but correspond to significantly elevated temperatures in the afternoon hours.

Effects of light and circadian clock on growth and chlorophyll accumulation of Nannochloropsis gaditana

Circadian clocks synchronize various physiological, metabolic and developmental processes of organisms with specific phases of recurring changes in their environment (e.g. day and night or seasons). Here, we investigated whether the circadian clock plays a role in regulation of growth and chlorophyll (Chl) accumulation in Nannochloropsis gaditana, an oleaginous marine microalga which is considered as a potential feedstock for biofuels and for which a draft genome sequence has been published. Optical density (OD) of N. gaditana culture was monitored at 680 and 735 nm under 12:12 h or 18:6 h light-dark (LD) cycles and after switching to continuous illumination in photobioreactors. In parallel, Chl fluorescence was measured to assess the quantum yield of photosystem II. Furthermore, to test if red- or blue-light photoreceptors are involved in clock entrainment in N. gaditana, some of the experiments were conducted by using only red or blue light. Growth and Chl accumulation were confined to light periods in the LD cycles, increasing more strongly in the first half than in the second half of the light periods. After switching to continuous light, rhythmic oscillations continued (especially for OD680 ) at least in the first 24 h, with a 50% decrease in the capacity to grow and accumulate Chl during the first subjective night. Pronounced free-running oscillations were induced by blue light, but not by red light. In contrast, the photosystem II quantum yield was determined by light conditions. The results indicate interactions between circadian and light regulation of growth and Chl accumulation in N. gaditana.

On-line monitoring of large cultivations of microalgae and cyanobacteria

Large cultivations of microalgae will benefit from on-line monitoring to achieve process control and improved productivity. This monitoring requires reliable sensors for on-line, in situ measurement of both physicochemical and biological process variables. Although standard industrial sensors can be used for many physicochemical variables, monitoring methods for most biological quantities rely on sensors that are currently suitable only for laboratory scale or off-line use. Here, we review these methods and discuss new approaches that could be adapted. We suggest that these new methods should be noninvasive and based on approaches that have already been applied to other bioprocesses; examples discussed here are in situ microscopy, flow cytometry (FC), IR spectroscopy, and software sensors.

An experimental helical-tubular photobioreactor for continuous production of Nannochloropsis sp

An experimental helical-tubular photobioreactor has been designed for controlled, continuous production of Nannochloropsis sp. Its main advantages are: (1) combination of large ratio of culture volume to surface area along with the optimised light penetration depth, (2) easy control of temperature and contaminants, (3) effective spatial distribution of fresh air and CO(2), (4) better CO(2) transfer through extensive interface surface between fresh air and culture-liquid medium and (5) novel automated flow-through sensor providing continuous cell concentration monitoring. Nannochloropsis sp. population density reached maximum value under rather high temperatures and combined natural and artificial light conditions. An average daily increase of 30 x 10(6)cells ml(-1) was obtained at population densities above 350 x 10(6)cells ml(-1) allowing daily harvesting rates of at least 10% the total volume. Measured cellular density productivity data and estimated volumetric productivity range of 1.10-3.03 g l(-1)day(-1), are among the highest Nannochloropsis sp. productivities reported in the literature.