On-line monitoring of large cultivations of microalgae and cyanobacteria

Development and Application of an Automated Raman Sensor for Bioprocess Monitoring: From the Laboratory to an Algae Production Platform

Microalgae provide valuable bio-components with economic and environmental benefits. The monitoring of microalgal production is mostly performed using different sensors and analytical methods that, although very powerful, are limited to qualified users. This study proposes an automated Raman spectroscopy-based sensor for the online monitoring of microalgal production. For this purpose, an in situ system with a sampling station was made of a light-tight optical chamber connected to a Raman probe. Microalgal cultures were routed to this chamber by pipes connected to pumps and valves controlled and programmed by a computer. The developed approach was evaluated on Parachlorella kessleri under different culture conditions at a laboratory and an industrial algal platform. As a result, more than 4000 Raman spectra were generated and analysed by statistical methods. These spectra reflected the physiological state of the cells and demonstrate the ability of the developed sensor to monitor the physiology of microalgal cells and their intracellular molecules of interest in a complex production environment.

Photosynthetic performance of Chlamydopodium (Chlorophyta) cultures grown in outdoor bioreactors

The microalga Chlamydopodium fusiforme MACC-430 was cultured in two types of outdoor pilot cultivation units-a thin-layer cascade (TLC) and a raceway pond (RWP) placed in a greenhouse. This case study aimed to test their potential suitability for cultivation scale-up to produce biomass for agriculture purposes (e.g., as biofertilizer or biostimulant). The culture response to the alteration of environmental conditions was evaluated in "exemplary" situations of good and bad weather conditions using several photosynthesis measuring techniques, namely oxygen production, and chlorophyll (Chl) fluorescence. Validation of their suitability for online monitoring in large-scale plants has been one of the objectives of the trials. Both techniques were found fast and robust reliable to monitor microalgae activity in large-scale cultivation units. In both bioreactors, Chlamydopodium cultures grew well in the semi-continuous regime using daily dilution (0.20-0.25 day^-1). The biomass productivity calculated per volume was significantly (about 5 times) higher in the RWPs compared to the TLCs. The measured photosynthesis variables showed that the build-up of dissolved oxygen concentration in the TLC was higher, up to 125-150% of saturation (%sat) as compared to the RWP (102-104%sat). As only ambient CO₂ was available, its shortage was indicated by a pH increase due to photosynthetic activity in the thin-layer bioreactor at higher irradiance intensities. In this setup, the RWP was considered more suitable for scale-up due to higher areal productivity, lower construction and maintenance costs, the smaller land area required to maintain large culture volumes, as well as lower carbon depletion and dissolved oxygen build-up. KEY POINTS: • Chlamydopodium was grown in both raceways and thin-layer cascades in pilot-scale. • Various photosynthesis techniques were validated for growth monitoring. • In general, raceway ponds were evaluated as more suitable for cultivation scale-up.

Cryopreservation of the Microalgae Scenedesmus sp

Each phytoplankton species presents a different behavior and tolerance to the cryopreservation process. Therefore, in a species-specific protocol, it is essential to ensure both growth and post-thawing cell viability. In this study, we explored the effect of cryopreservation of Scenedesmus sp. with two cryoprotectants, dimethyl sulfoxide (DMSO) and methanol (MET), at 5% and 10% inclusion for each. In the control treatment, the microalgae were not exposed to cryoprotective agents (Control). Three post-thawing cell viability criteria were used: no cell damage (NCD), cell damage (CD), and marked lesions (LM), and mitochondrial and cell membrane damage was evaluated by flow cytometry. The study was a 2 × 2 factorial design, with five replications by treatments, population growth, and cell damage evaluated from the fifth day after thawing. On the fifth day, the highest percentage of NCD was observed when the microalgae were cryopreserved with DMSO 5% (50%); Regarding the control group, it showed 0% NCD. Flow cytometry analysis reveals minor damage at the membrane and mitochondria (9-10.7%) when DMSO is used at both inclusion percentages (5-10%) after thawing. In the exponential phase, the highest growth rates, doubling time, and yield was observed in cryopreserved cells with MET 5%. The results suggest that DMSO 5% is an ideal treatment for cryopreserving microalgae Scenedesmus sp.

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.

Monitoring PHB production in Synechocystis sp. with hyperspectral images

Microalgae wastewater treatment systems have the potential for producing added-value products. More specifically, cyanobacteria are able to accumulate polyhydroxybutyrates (PHBs), which can be extracted and used for bioplastics production. Nonetheless, PHB production requires proper culture conditions and continue monitoring, challenging the state-of-the-art technologies. The aim of this study was to investigate the application of hyperspectral technologies to monitor cyanobacteria population growth and PHB production. We have established a ground-breaking measurement method able to discern spectral reflectance changes from light emitted to cyanobacteria in different phases. All in all, enabling to distinguish between cyanobacteria growth phase and PHB accumulation phase. Furthermore, first tests of classification algorithms used for machine learning and image recognition technologies had been applied to automatically recognize the different cyanobacteria species from a complex microbial community containing cyanobacteria and microalgae cultivated in pilot-scale photobioreactors (PBRs). We have defined three main indicators for monitoring PHB production: (i) cyanobacteria specific-strain density, (ii) differentiate between growth and PHB-accumulation and (iii) chlorosis progression. The results presented in this study represent an interesting alternative for traditional measurements in cyanobacteria PHB production and its application in pilot-scale PBRs. Although not directly determining the amount of PHB production, they would give insights on the undergoing processes.

Chlorophyll fluorescence as a valuable multitool for microalgal biotechnology

Variable fluorescence of chlorophyll (CF) of the photosynthetic apparatus is an ample source of valuable information on physiological condition of photosynthetic organisms. Currently, the most widespread CF-based technique is represented by recording pulse-amplitude modulated (PAM) induction of CF by saturating light. The CF-based monitoring techniques are increasingly employed for characterization of performance and stress resilience of microalgae in microalgal biotechnology. Analysis of CF induction curves reveals the fate of light energy absorbed by photosynthetic apparatus, the proportions of the energy that have been utilized for photochemistry (culture growth), and heat dissipated by photoprotective mechanisms. Hence CF and its derived parameters are an accurate proxy of the metabolic activity of the photosynthetic cell and the engagement of photoprotective mechanisms. This information is a solid foundation for making decisions on the microalgal culture management during the lab-scale and industrial-scale cultivation. Applications of CF and PAM include the monitoring of stressor (high light, nutrient deprivation, extreme temperatures, etc.) effects for assessment of the culture robustness. It also serves as a non-invasive express test for gauging the effect of assorted toxicants in microalgae. This approach is becoming widespread in ecological toxicology and environmental biotechnology, particularly for bioprospecting strains capable of the destruction of dangerous pollutants such as pharmaceuticals. In the review, we discuss the advantages and drawbacks of using CF-based methods for assessment of the culture conditions. Special attention is paid to the potential caveats and applicability of different variations of CF and PAM measurements for solving problems of microalgal biotechnology.

Advances in automated real-time flow cytometry for monitoring of bioreactor processes

Flow cytometry and its technological possibilities have greatly advanced in the past decade as analysis tool for single cell properties and population distributions of different cell types in bioreactors. Along the way, some solutions for automated real-time flow cytometry (ART-FCM) were developed for monitoring of bioreactor processes without operator interference over extended periods with variable sampling frequency. However, there is still great potential for ART-FCM to evolve and possibly become a standard application in bioprocess monitoring and process control. This review first addresses different components of an ART-FCM, including the sampling device, the sample-processing unit, the unit for sample delivery to the flow cytometer and the settings for measurement of pre-processed samples. Also, available algorithms are presented for automated data analysis of multi-parameter fluorescence datasets derived from ART-FCM experiments. Furthermore, challenges are discussed for integration of fluorescence-activated cell sorting into an ART-FCM setup for isolation and separation of interesting subpopulations that can be further characterized by for instance omics-methods. As the application of ART-FCM is especially of interest for bioreactor process monitoring, including investigation of population heterogeneity and automated process control, a summary of already existing setups for these purposes is given. Additionally, the general future potential of ART-FCM is addressed.

Sustainable smart photobioreactor for continuous cultivation of microalgae embedded with Internet of Things

This research work aims to fabricate an optimized up-scaled photobioreactor and extraction tank which incorporates the Internet of Things (IoT) for remote monitoring of selected parameters without being present in the lab as the industry is gradually moving towards the direction of remote operation. Several design factors were considered where modelling using ANSYS was carried out before the finalised design is drawn using AutoCAD. To monitor critical parameters that include liquid level, temperature, and pH condition during the operation of the tanks, water-proof sensors are implemented with the aid of Arduino NodeMCU board and the sensors are linked with Blynk, a smartphone application that allows remote monitoring via Wi-Fi connection. The sensors' results obtained using the Blynk application show high accuracy as compared with manual data except for photobioreactor liquid level. This shows that IoT and remote monitoring can be integrated successfully.

Real-Time Monitoring of Microalgal Biomass in Pilot-Scale Photobioreactors Using Nephelometry

The increasing cultivation of microalgae in photobioreactors warrants efficient and non-invasive methods to quantify biomass density in real time. Nephelometric turbidity assessment, a method that measures light scatter by particles in suspension, was introduced already several decades ago but was only recently validated as a high-throughput tool to monitor microalgae biomass. The light scatter depends on the density of the suspended particles as well as on their physical properties, but so far there are hardly any accounts on how nephelometric assessment relates to classic methods such as dry weight and spectrophotometric measurement across a broad biomass density range for different microalgae species. Here, we monitored biomass density online and in real time during the semi-continuous cultivation of three commercial microalgae species Chloromonas typhlos, Microchloropsis gaditana and Porphyridium purpureum in pilot-scale photobioreactors, and relate nephelometric turbidity to dry weight and optical density. The results confirm a relatively strong (R2 = 0.87–0.93) and nonlinear relationship between turbidity and biomass density that differs among the three species. Overall, we demonstrate how nephelometry can be used to monitor microalgal biomass in photobioreactors, and provide the necessary means to estimate the biomass density of the studied species from turbidity data to facilitate automated biomass monitoring.

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.

Simulating chlorophyll-a fluorescence changing rate and phycocyanin fluorescence by using a multi-sensor system in Lake Taihu, China

Algal pollution in water sources has posed a serious problem. Estimating algal concentration in advance saves time for drinking water plants to take measures and helps us to understand causal chains of algal dynamics. This paper explores the possibility of building a short-term algal early warning model with online monitoring systems. In this study, we collected high-frequency data for water quality and weather conditions in shallow and eutrophic Lake Taihu by an in situ multi-sensor system (BIOLIFT) combined with a weather station. Extracted chlorophyll-a from water samples and chlorophyll-a fluorescence differentiated according to different algal classeses verified that chlorophyll-a fluorescence continuously measured by BIOLIFT only represent chlorophyll-a of green algae and diatoms. Stepwise linear regression was used to simulate the chlorophyll-a fluorescence changing rate of green algae and diatoms together (ΔChl_a-f%) and phycocyanin fluorescence concentration (blue-green algae) on the water surface layer (CyanoS). The results show that nutrients (total N, NO₃-N, NH₄-N, total P) were not necessary parameters for short-term algal models. ΔChl_a-f % is greatly influenced by the seasons, so seasonal partition of data before modeling is highly recommended. CyanoS_max and ΔChl_a-f% were simulated by only using multi-sensor and meteorological data (R² = 0.73; 0.75). All the independent variables (wave, water temperature, relative humidity, depth, cloud cover) used in the model were measured online and predictable. Wave height is the most important independent variable in the shallow lake. This paper offers a new approach to simulate and predict the algal dynamics, which also can be applied in other surface water.

Optimal influent N-to-P ratio for stable microalgal cultivation in water treatment and nutrient recovery

Species specific nitrogen-to-phosphorus molar ratio (NPR) has been suggested for green microalgae. Algae can store nitrogen and phosphorus, suggesting that the optimum feed concentration dynamically changes as function of the nutrient storage. We assessed the effect of varying influent NPR on microalgal cultivation in terms of microbial community stability, effluent quality and biokinetics. Mixed green microalgae (Chlorella sorokiniana and Scenedesmus sp.) and a monoculture of Chlorella sp. were cultivated in continuous laboratory-scale reactors treating used water. An innovative image analysis tool, developed in this study, was used to track microbial community changes. Diatoms proliferated as influent NPR decreased, and were outcompeted once cultivation conditions were restored to the optimal NPR range. Low NPR operation resulted in decrease in phosphorus removal, biomass concentration and effluent nitrogen concentration. ASM-A kinetic model simulation results agreed well with operational data in the absence of diatoms. The failure to predict operational data in the presence of diatoms suggest differences in microbial activity that can significantly influence nutrient recovery in photobioreactors (PBR). No contamination occurred during Chlorella sp. monoculture cultivation with varying NPRs. Low NPR operation resulted in decrease in biomass concentration, effluent nitrogen concentration and nitrogen quota. The ASM-A model was calibrated for the monoculture and the simulations could predict the experimental data in continuous operation using a single parameter subset, suggesting stable biokinetics under the different NPR conditions. Results show that controlling the influent NPR is effective to maintain the algal community composition in PBR, thereby ensuring effective nutrients uptake.

On-line monitoring of photosynthetic activity based on pH data to assess microalgae cultivation

Microalgae performance of outdoor cultivation systems is influenced by environmental and operating dynamics. Monitoring and control systems are needed to maximise biomass productivity and nutrient recovery. The goal of this work was to corroborate that pH data could be used to monitor microalgae performance by means of data from an outdoor membrane photobioreactor (MPBR) plant. In this system, microalgae photosynthetic activity was favoured over other physical and biological processes, so that the pH data dynamics was theoretically related to the microalgae carbon uptake rate (CUR). Short- and long-term continuous operations were tested to corroborate the relationship between the first derivate of pH data dynamics (pH') and microalgae photosynthetic activity. Short-term operations showed a good correlation between gross pH' values and MPBR performance. An indicator of the maximum daily average microalgae activity was assessed by a combination of on-line pH' measurements obtained in the long-term and a microalgae growth kinetic model. Both indicators contributed to the development of advanced real-time monitoring and control systems to optimise microalgae cultivation technology.

Microalgae-bacteria consortia in high-rate ponds for treating urban wastewater: Elucidating the key state indicators under dynamic conditions

On-line performance indicators of a microalgae-bacteria consortium were screened out from different variables based on pH and dissolved oxygen on-line measurements via multivariate projection analysis, aiming at finding on-line key state indicators to easily monitor the process. To fulfil this objective, a pilot-scale high-rate pond for urban wastewater treatment was evaluated under highly variable conditions, i.e. during the start-up period. The system was started-up without seed of either bacterial or microalgal biomass. It took around 19 days to fully develop a microalgal community assimilating nutrients significantly. Slight increases in the biomass productivities in days 26-30 suggest that the minimum time for establishing a performant bacteria-microalgae consortium could be of around one month for non-inoculated systems. At this point, the process was fully functional, meeting the European discharge limits for protected areas. The results of the statistical analyses show that both the pH and the dissolved oxygen concentration represent accurately the biochemical processes taking place under the start-up of the process. Both pH and dissolved oxygen represented accurately also the performance of the high-rate algal pond, being affordable, easily-implemented, options for monitoring, control and optimization of industrial-scale processes.

Digital quantification and selection of high-lipid-producing microalgae through a lateral dielectrophoresis-based microfluidic platform

Microalgae are promising alternatives to petroleum as renewable biofuel sources, however not sufficiently economically competitive yet. Here, a label-free lateral dielectrophoresis-based microfluidic sorting platform that can digitally quantify and separate microalgae into six outlets based on the degree of their intracellular lipid content is presented. In this microfluidic system, the degree of cellular lateral displacement is inversely proportional to the intracellular lipid level, which was successfully demonstrated using Chlamydomonas reinhardtii cells. Using this functionality, a quick digital quantification of sub-populations that contain different intracellular lipid level in a given population was achieved. In addition, the degree of lateral displacement of microalgae could be readily controlled by simply changing the applied DEP voltage, where the level of gating in the intracellular lipid-based sorting decision could be easily adjusted. This allowed for selecting only a very small percentage of a given population that showed the highest degree of intracellular lipid content. In addition, this approach was utilized through an iterative selection process on natural and chemically mutated microalgal populations, successfully resulting in enrichment of high-lipid-accumulating microalgae. In summary, the developed platform can be exploited to quickly quantify microalgae lipid distribution in a given population in real-time and label-free, as well as to enrich a cell population with high-lipid-producing cells, or to select high-lipid-accumulating microalgal variants from a microalgal library.

Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity

Microalgae are swift replicating photosynthetic microorganisms with several applications for food, chemicals, medicine and fuel. Microalgae have been identified to be suitable for biofuels production, due to their high lipid contents. Microalgae-based biofuels have the potential to meet the increasing energy demands and reduce greenhouse gas (GHG) emissions. However, the present state of technology does not economically support sustainable large-scale production. The biofuel production process comprises the upstream and downstream processing phases, with several uncertainties involved. This review examines the various production and processing stages, and considers the use of chemometric methods in identifying and understanding relationships from measured study parameters via statistical methods, across microalgae production stages. This approach enables collection of relevant information for system performance assessment. The principal benefit of such analysis is the identification of the key contributing factors, useful for decision makers to improve system design, operation and process economics. Chemometrics proffers options for time saving in data analysis, as well as efficient process optimization, which could be relevant for the continuous growth of the microalgae industry.

Fluorescence-based real time monitoring and diagnostics of recombinant Pichia pastoris cultivations in a bioreactor

This study describes the application of the multivariate curve resolution (MCR) analysis technique for real-time analysis of culture fluorescence during recombinant Pichia pastoris cultivation in a bioreactor. Fluorescence spectra were acquired with an on-line dual excitation wavelength fluorometer and then used to develop a real time MCR-based bioprocess monitoring and diagnostics tool. Initial bioreactor experiments using two similar recombinant antibody secreting P. pastoris cell lines showed significant differences in protein production. To distinguish between the contributions of operating conditions and the specific cell line's genetic composition to the observed differences in protein production, the bioreactor experiments were repeated and accompanied by real time MCR analysis. The tests demonstrated high sensitivity of MCR-derived "pure concentration" profiles to growth as well as to initial conditions, thus enabling real-time cultivation process trend diagnostics and fault detection. © 2018 Her Majesty the Queen in Right of Canada © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2761, 2019.

Industrial potential of carotenoid pigments from microalgae: Current trends and future prospects

Microalgae are rich source of various bioactive molecules such as carotenoids, lipids, fatty acids, hydrocarbons, proteins, carbohydrates, amino acids, etc. and in recent Years carotenoids from algae gained commercial recognition in the global market for food and cosmeceutical applications. However, the production of carotenoids from algae is not yet fully cost effective to compete with synthetic ones. In this context the present review examines the technologies/methods in relation to mass production of algae, cell harvesting for extraction of carotenoids, optimizing extraction methods etc. Research studies from different microalgal species such as Spirulina platensis, Haematococcus pluvialis, Dunaliella salina, Chlorella sps., Nannochloropsis sps., Scenedesmus sps., Chlorococcum sps., Botryococcus braunii and Diatoms in relation to carotenoid content, chemical structure, extraction and processing of carotenoids are discussed. Further these carotenoid pigments, are useful in various health applications and their use in food, feed, nutraceutical, pharmaceutical and cosmeceutical industries was briefly touched upon. The commercial value of algal carotenoids has also been discussed in this review. Possible recommendations for future research studies are proposed.

Laser reflectance measurement for the online monitoring of Chlorella sorokiniana biomass concentration

Fast and reliable methods to determine biomass concentration are necessary to facilitate the large scale production of microalgae. A method for the rapid estimation of Chlorella sorokiniana biomass concentration was developed. The method translates the suspension particle size spectrum gathered though laser reflectance into biomass concentration by means of two machine learning modelling techniques. In each case, the model hyper-parameters were selected applying a simulated annealing algorithm. The results show that dry biomass concentration can be estimated with a very good accuracy (R²=0.87). The presented method seems to be suited to perform fast estimations of biomass concentration in suspensions of microalgae cultivated in moderately turbid media with tendency to aggregate.

Maximizing the productivity of the microalgae Scenedesmus AMDD cultivated in a continuous photobioreactor using an online flow rate control

In this study, production of the microalga Scenedesmus AMDD in a 300 L continuous flow photobioreactor was maximized using an online flow (dilution rate) control algorithm. To enable online control, biomass concentration was estimated in real time by measuring chlorophyll-related culture fluorescence. A simple microalgae growth model was developed and used to solve the optimization problem aimed at maximizing the photobioreactor productivity. When optimally controlled, Scenedesmus AMDD culture demonstrated an average volumetric biomass productivity of 0.11 g L^-1 d^-1 over a 25 day cultivation period, equivalent to a 70 % performance improvement compared to the same photobioreactor operated as a turbidostat. The proposed approach for optimizing photobioreactor flow can be adapted to a broad range of microalgae cultivation systems.

Simulation Method Linking Dense Microalgal Culture Spectral Properties in the 400-750 nm Range to the Physiology of the Cells

This work describes a method to model the optical properties over the (400-750 nm) spectral range of a dense microalgal culture using the chemical and physical properties of the algal cells. The method was based on a specific program called AlgaSim coupled with the adding-doubling method: at the individual cell scale, AlgaSim simulates the spectral properties of one model, three-layer spherical algal cell from its size and chemical composition. As a second step, the adding-doubling method makes it possible to retrieve the total transmittance of the algal medium from the optical properties of the individual algal cells. The method was tested by comparing the simulated total transmittance spectra for dense marine microalgal cultures of Isochrysis galbana (small flagellates) and Phaeodactylum tricornutum (diatoms) to spectra measured using an experimental spectrophotometric setup. Our study revealed that the total transmittance spectra simulated for the quasi-spherical cells of Isochrysis galbana were in good agreement with the measured spectra over the whole spectral range. For Phaeodactylum tricornutum, large differences between simulated and measured spectra were observed over the blue part of the transmittance spectra, probably due to non-spherical shape of the algal cells. Prediction of the algal cell density, mean size and pigment composition from the total transmittance spectra measured on algal samples was also investigated using the reversal of the method. Mean cell size was successfully predicted for both species. The cell density was also successfully predicted for spherical Isochrysis galbana, with a relative error below 7%, but not for elongated Phaeodactylum tricornutum with a relative error up to 26%. The pigments total quantity and composition, the carotenoids:chlorophyll ratio in particular, were also successfully predicted for Isochrysis galbana with a relative error below 8%. However, the pigment predictions and measurements for Phaeodactylum tricornutum showed large discrepancies, with a relative error up to 88%. These results give strong support for the development of a promising tool providing rapid and accurate estimations of biomass and physiological status of a dense microalgal culture based on only light transmittance properties.

Estimation of Chlamydomonas reinhardtii biomass concentration from chord length distribution data

A novel method to estimate the concentration of Chlamydomonas reinhardtii biomass was developed. The method employs the chord length distribution information gathered by means of a focused beam reflectance probe immersed in the culture sample and processes the data through a feedforward multilayer perceptron. The multilayer perceptron architecture was systematically optimised through the application of a simulated annealing algorithm. The method developed can predict the concentration of microalgae with acceptable accuracy and, with further development, it could be implemented online to monitor the aggregation status and biomass concentration of microalgal cultures.

Growth of Cyanobacterium aponinum influenced by increasing salt concentrations and temperature

The increasing requirement of food neutral biofuels demands the detection of alternative sources. The use of non-arable land and waste water streams is widely discussed in this regard. A Cyanobacterium was isolated on the area of a possible algae production side near a water treatment plant in the arid desert region al-Wusta. It was identified as Cyanobacterium aponinum PB1 and is a possible lipid source. To determine its suitability of a production process using this organism, a set of laboratory experiments were performed. Its growth behavior was examined in regard to high temperatures and increasing NaCl concentrations. A productivity of 0.1 g L-1 per day was measured at an alga density below 0.75 g L-1. C. aponinum PB1 showed no sign of altered growth behavior in media containing 70 g L-1 NaCl or less. Detection of a negative effect of NaCl on the growth using Pulse-Amplitude-Modulation chlorophyll fluorescence analysis was not more sensitive than optical density measurement.

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.

The role of photo-osmotic adaptation in semi-continuous culture and lipid particle release from Dunaliella viridis

Although great efforts have been made to elucidate the phenotypic responses of alga to varying levels of nutrients, osmotic environments, and photosynthetically active radiation intensities, the role of interactions among these variables is largely nebulous. Here, we describe a general method for establishing and maintaining semi-continuous cultures of the halophilic microalgal production strain, Dunaliella viridis, that is independent of variations in salinity and illumination intensity. Using this method, the cultures were evaluated to elucidate the overlapping roles of photosynthetic and osmotic adaptation on the accumulation and compositional variation of the biomass, photosynthetic productivity, and physiological biomarkers, as well as spectroscopic and morphological details at the single-cell level. Correlation matrices defining the relationships among the observables and based on variation of the illumination intensity and salinity were constructed for predicting bioproduct yields for varying culture conditions. Following maintenance of stable cultures for 6-week intervals, phenotypic responses to photo-osmotic drift were explored using a combination of single-cell hyperspectral fluorescence imaging and flow cytometry. In addition to morphological changes, release of lipid microparticles from the cells that is disproportionate to cell lysis was observed under hypotonic drift, indicating the existence of a reversible membrane permeation mechanism in Dunaliella. This phenomenon introduces the potential for low-cost strategies for recovering lipids and pigments from the microalgae by minimizing the requirement for energy intensive harvesting and dewatering of the biomass. The results should be applicable to outdoor culture, where seasonal changes resulting in variable solar flux and precipitation and evaporation rates are anticipated.

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.