Combination of Sequencing Batch Operation and A/O Process to Achieve Partial Mainstream Anammox: Pilot-Scale Demonstration and Microbial Ecological Mechanism.

In this study, sequencing batch operation was successfully combined with a pilot-scale anaerobic biofilm-modified anaerobic/aerobic membrane bioreactor to achieve anaerobic ammonium oxidation (anammox) without inoculation of anammox aggregates for municipal wastewater treatment. Both total nitrogen and phosphorus removal efficiencies of the reactor reached up to 80% in the 250-day operation, with effluent concentrations of 4.95 mg-N/L and 0.48 mg-P/L. In situ enrichment of anammox bacteria with a maximum relative abundance of 7.86% was observed in the anaerobic biofilm, contributing to 18.81% of nitrogen removal, with denitrification being the primary removal pathway (38.41%). Denitrifying phosphorus removal (DPR) (40.54%) and aerobic phosphorus uptake (48.40%) played comparable roles in phosphorus removal. Metagenomic sequencing results showed that the biofilm contained significantly lower abundances of NO-reducing functional genes than the bulk sludge (p < 0.01), favoring anammox catabolism in the former. Interactions between the anammox bacteria and flanking community were dominated by cooperation behaviors (e.g., nitrite supply, amino acids/vitamins exchange) in the anaerobic biofilm community network. Moreover, the hydrolytic/fermentative bacteria and endogenous heterotrophic bacteria (Dechloromonas, Candidatus competibacter) were substantially enriched under sequencing batch operation, which could alleviate the inhibition of anammox bacteria by complex organics. Overall, this study provides a feasible and promising strategy for substantially enriching anammox bacteria and achieving partial mainstream anammox as well as DPR.

Free ammonia-free nitrous acid based partial nitrification in sequencing batch membrane aerated biofilm reactor.

Membrane aerated biofilm reactor (MABR) has attracted a lot of attention as an energy-efficient integrated nitrogen removing technology in recent years. However, it is lacking of understanding to realize stable partial nitrification in MABR because of its unique oxygen transfer mode and biofilm structure. In this study, free ammonia (FA) and free nitrous acid (FNA) based control strategies for partial nitrification with low NH4+-N concentration were proposed in a MABR of sequencing batch mode. The MABR was operated for over 500 days under different influent NH4+-N concentrations. With the influent NH4+-N of around 200 mg-N/L, partial nitrification could be established with relatively low concentration of FA (0.4-2.2 mg-N/L) which suppressed nitrite oxidizing bacteria (NOB) on the biofilm. With lower influent NH4+-N concentration of around 100 mg-N/L, the FA concentration was lower and strengthened suppression strategies based on FNA were needed. With the final pH of operating cycles below 5.0, the FNA produced in the sequencing batch MABR could stabilize partial nitrification by eliminating NOB on the biofilm. Since the activity of ammonia oxidizing bacteria (AOB) was lower without the blow-off of dissolved carbon dioxide in the bubbleless MABR, longer hydraulic retention time was required to reach a low pH for high concentration of FNA to suppress NOB. After exposures to FNA, the relative abundance of Nitrospira was decreased by 94.6%, while the abundance of Nitrosospira increased greatly which became another dominant AOB genus in addition to Nitrosomonas.

Optimization of protein recovery from sewage sludge via controlled and energy-saving ultrasonic-alkali hydrolysis.

The abundant protein in excess sludge can be recovered to prepare high value-added products. However, this sustainable treatment method still has large challenges, such as high energy consumption. In this work, the classical batch operation (BO) and semi-batch operation (SBO) modes were adopted and compared for ultrasonic-alkali hydrolysis. The results showed that the reaction time of SBO significantly decreased to half of that of BO with the same efficiency (ca. 70 %), indicating that SBO was much more energy-efficient. Moreover, analysis of the nitrogen solubility index and trichloroacetic acid-soluble nitrogen index demonstrated that the further proteolysis of protein under SBO was limited. Furthermore, the first-order reaction model fitted the hydrolysis data well (R2 ≥ 0.91) for both modes, in which the rate constant of SBO (k = 0.44 min-1) was 2.3 times that of BO. Finally, the properties of both products met the standards of foaming extinguishers.

Effects of combined enzymatic hydrolysis and fed-batch operation on efficient improvement of ferulic acid and p-coumaric acid production from pretreated corn straws.

In the present work, the effects of combined enzymatic hydrolysis by cellulase and xylanase (CXEH), fed-batch enzymatic hydrolysis (FBEH) operation and kinetics on production of ferulic acid (FA) and p-coumaric acid (pCA) from pretreated corn straws were investigated. The results showed that CXEH could efficiently increase production of FA and pCA. When performed the FBEH operation by feeding 150 mL enzymatic hydrolysis solution (1.5 % enzyme concentration, 5:4 (v/v) ratio of cellulase to xylanase and 2.0 % substrate loading) to 250 mL batch enzymatic hydrolysis solution at 36 h, the maximum production (2178.58 and 2710.17 mg/L) and production rate (590.95 and 727.89 mg/L.h) of FA and pCA were respectively obtained. Moreover, the disruption of fiber tissues, enhancement of crystallinity and accelerated degradation of hemicelluloses and lignocelluloses caused by CXEH contributed to effectively improving production of FA and pCA in corn straws.

Modelling of autogenerative high-pressure anaerobic digestion in a batch reactor for the production of pressurised biogas.

BACKGROUND: Pressurised anaerobic digestion allows the production of biogas with a high content of methane and, at the same time, avoid the energy costs for the biogas upgrading and injection into the distribution grid. The technology carries potential, but the research faces practical constraints by a.o. the capital investment needed in high-pressure reactors and sensors and associated sampling limitations. In this work, the kinetic model of an autogenerative high-pressure anaerobic digestion of acetate, as the representative compound of the aceticlastic methanogenesis route, in batch configuration, is proposed to predict the dynamic performance of pressurised digesters and support future experimental work. The modelling of autogenerative high-pressure anaerobic digestion in batch configuration, which is not extensively studied and simulated in the present literature, was developed, calibrated, and validated by using experimental results available from the literature. RESULTS: Under high-pressure conditions, the assessment of the Monod maximum specific uptake rate, the half-saturation constant and the first-order decay rate was carried out, and the values of 5.9 kg COD kg COD-1 d-1, 0.05 kg COD m-3 and 0.02 d-1 were determined, respectively. By using the predicted values, excellent fittings of the final pressure, the CH4 molar fraction and the specific methanogenic yield calculation were obtained. Likewise, the variation in the gas-liquid mass transfer coefficient by several orders of magnitude showed negligible effects on the model predictive values in terms of methane molar fraction of the produced biogas, while the final pressure seemed to be slightly influenced. CONCLUSIONS: The proposed model allowed to estimate the Monod maximum specific uptake rate for acetate, the half-saturation rate for acetate and the first-order decay rate constant, which were comparable with literature values reported for well-studied methanogens under anaerobic digestion at atmospheric pressure. The methane molar fraction and the final pressure predicted by the model showed different responses towards the variation of the gas-liquid mass transfer coefficient since the former seemed not to be affected by the variation of the gas-liquid mass transfer coefficient; in contrast, the final pressure seemed to be slightly influenced. The proposed approach may also allow to potentially identify the methanogens species able to be predominant at high pressure.

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty.

Astaxanthin is a high-value compound commercially synthesized through Xanthophyllomyces dendrorhous fermentation. Using mixed sugars decomposed from biowastes for yeast fermentation provides a promising option to improve process sustainability. However, little effort has been made to investigate the effects of multiple sugars on X. dendrorhous biomass growth and astaxanthin production. Furthermore, the construction of a high-fidelity model is challenging due to the system's variability, also known as batch-to-batch variation. Two innovations are proposed in this study to address these challenges. First, a kinetic model was developed to compare process kinetics between the single sugar (glucose) based and the mixed sugar (glucose and sucrose) based fermentation methods. Then, the kinetic model parameters were modeled themselves as Gaussian processes, a probabilistic machine learning technique, to improve the accuracy and robustness of model predictions. We conclude that although the presence of sucrose does not affect the biomass growth kinetics, it introduces a competitive inhibitory mechanism that enhances astaxanthin accumulation by inducing adverse environmental conditions such as osmotic gradients. Moreover, the hybrid model was able to greatly reduce model simulation error and was particularly robust to uncertainty propagation. This study suggests the advantage of mixed sugar-based fermentation and provides a novel approach for bioprocess dynamic modeling.

Semi-batch cultivation of Chlorella sorokiniana AK-1 with dual carriers for the effective treatment of full strength piggery wastewater treatment.

In this study, process optimization for the microalgae-based piggery wastewater treatment was carried out by growing Chlorella sorokiniana AK-1 on untreated piggery wastewater with efficient COD/BOD/TN/TP removal and high biomass/protein productivities. Integration of the immobilization carriers (sponge, activated carbon) and semi-batch cultivation resulted in the effective treatment of raw untreated piggery wastewater. With 100% wastewater, 0.2% sponge and 2% activated carbon, the semi-batch cultivation (90% media replacement every 6 days) exhibited a COD, BOD, TN and TP removal efficiency of 95.7%, 99.0%, 94.1% and 96.9%, respectively. The maximal protein content, protein productivity, lutein content, and lutein productivity of the obtained microalgal biomass was 61.1%, 0.48 g/L/d, 4.56 mg/g, and 3.56 mg/L/d, respectively. The characteristics of the treated effluent satisfied Taiwan Piggery Wastewater Discharge Standards (COD < 600 mg/L, BOD < 80 mg/L). This innovative approach demonstrated excellent performance for simultaneous piggery wastewater treatment and microalgal biomass production.

Characteristics of heavy metal separation and determination of limiting current density in a pilot-scale electrodialysis process for plating wastewater treatment.

Recent development in industry has led to increased water usage, while intensifying water shortage. Electrodialysis has been proposed as a technique for minimizing the generation of secondary environmental pollution problems and effectively treating harmful substances such as heavy metals in industrial wastewater. As electrodialysis is affected by several factors, it is crucial to provide necessary information about the operating elements. This study investigates the effect of linear flow velocity on the removal of heavy metals in an electrodialysis pilot plant. The results of the experiment showed that increasing the linear flow velocity from 0.6 to 5.1 cm/min increased the voltage from 17.3 to 40 V. In addition, the limiting current density (LCD) showed a linear relationship with the linear flow velocity, increasing from 1.4 to 5.9 A/m2 as the linear flow velocity increased proportionally in the same voltage range. The empirical correlation coefficients a and b for the mass transfer coefficient K, which can be expressed as a nonlinear function of the linear flow velocity, were estimated to be 1.8519 and 0.7016, respectively. In the batch operation, the ion-separation rate in the electrodialysis process was estimated with the shift order kinetics of the first-order and zero-order constants via regression analysis of experimental data. The ion separation rate in the diluate and the ion concentration rate in the concentrate decrease as the experiment number increase. This may be due to the reverse diffusion of ions transferring to the diluate owing to the high concentration of ions in the concentrate. Therefore, ion concentration in the concentrate has to be maintained at an appropriate level. Copper ions are deposited on the cathode electrode surface, although not uniformly.

Designing scalable ultrafiltration/diafiltration process of monoclonal antibodies via mathematical modeling by coupling mass balances and Poisson-Boltzmann equation.

Ultrafiltration/diafiltration (UF/DF) operations are employed for achieving the desired therapeutic monoclonal antibody (mAb) formulations. Due to electrostatic interactions between the charged proteins, solute ions, and uncharged excipients, the final pH and concentration values are not always equal to those in the DF buffer. At high protein concentrations, typical for industrial formulations, this effect becomes predominant. To account for challenges occurring in industrial environments, a robust mathematical framework enabling the prediction of pH and concentration profiles throughout the UF/DF process is provided. The proposed mechanistic model combines a macroscopic mass balance approach with a molecular approach based on a Poisson-Boltzmann equation dealing with electrostatic interactions and accounting for protein exclusion volume effect. The mathematical model was validated with experimental data of two commercially relevant mAbs obtained from an industrial UF/DF process using scalable laboratory equipment. The robustness and flexibility of the model were tested by using proteins with different isoelectric points and net charges. The latter was determined via a titration curve, enabling realistic protein charge-pH evaluation. In addition, the model was tested for different DF buffer types containing both monovalent and polyvalent ions, with various types of uncharged excipients. The model generality enables its implementation for the UF/DF processes of other protein varieties.

Combining model structure identification and hybrid modelling for photo-production process predictive simulation and optimisation.

Integrating physical knowledge and machine learning is a critical aspect of developing industrially focused digital twins for monitoring, optimisation, and design of microalgal and cyanobacterial photo-production processes. However, identifying the correct model structure to quantify the complex biological mechanism poses a severe challenge for the construction of kinetic models, while the lack of data due to the time-consuming experiments greatly impedes applications of most data-driven models. This study proposes the use of an innovative hybrid modelling approach that consists of a simple kinetic model to govern the overall process dynamic trajectory and a data-driven model to estimate mismatch between the kinetic equations and the real process. An advanced automatic model structure identification strategy is adopted to simultaneously identify the most physically probable kinetic model structure and minimum number of data-driven model parameters that can accurately represent multiple data sets over a broad spectrum of process operating conditions. Through this hybrid modelling and automatic structure identification framework, a highly accurate mathematical model was constructed to simulate and optimise an algal lutein production process. Performance of this hybrid model for long-term predictive modelling, optimisation, and online self-calibration is demonstrated and thoroughly discussed, indicating its significant potential for future industrial application.

Fermentation optimization and kinetic model for high cell density culture of a probiotic microorganism: Lactobacillus rhamnosus LS-8.

To develop a practical food-grade medium and optimal fermentation process for the cost-effective fermentation of Lactobacillus rhamnosus LS-8, both culture medium and conditions were optimized by combining single-factor experimental design, Plackett-Burman design and Box-Behnken design. The medium was simplified to five ingredients (g/L): whey powder (62.5), maltose syrup (50), corn steep liquor (55), NaCl (1) and lysine (0.05), and the optimal culture conditions were initial pH (6.28), constant fermentation pH (4.7), neutralizing agent (NaOH), aeration rate (0.2 v/v/min) and stirrer speed (200 rpm). After culturing in this optimized medium and conditions, the cell density of L. rhamnosus LS-8 was improved to 4.5 × 109 CFU/mL, which was elevated about 9 times higher than that obtained in MRS medium. Moreover, cell growth and substrate consumption kinetic constants were determined by the logistic equation and Luedeking-Piret model, and the R2 values from the model equation were 0.9900 and 0.9971, respectively, indicating that these models were able to simulate the growth and substrate consumption of L. rhamnosus LS-8 accurately. In addition, a high-efficient production process of L. rhamnosus LS-8 was developed by repeated-batch operation, which was verified by five cycles of fermentation with good stability and repeatability. In conclusion, the efficiency of L. rhamnosus LS-8 fermentation was greatly improved as well as the reduction of the cost using the medium and process developed in the present study.

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization.

Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re-fitting the simple kinetic model (soft sensor) using the data-driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed-batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open-loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.

Comparison of physics-based and data-driven modelling techniques for dynamic optimisation of fed-batch bioprocesses.

The development of digital bioprocessing technologies is critical to operate modern industrial bioprocesses. This study conducted the first investigation on the efficiency of using physics-based and data-driven models for the dynamic optimisation of long-term bioprocess. More specifically, this study exploits a predictive kinetic model and a cutting-edge data-driven model to compute open-loop optimisation strategies for the production of microalgal lutein during a fed-batch operation. Light intensity and nitrate inflow rate are used as control variables given their key impact on biomass growth and lutein synthesis. By employing different optimisation algorithms, several optimal control sequences were computed. Due to the distinct model construction principles and sophisticated process mechanisms, the physics-based and the data-driven models yielded contradictory optimisation strategies. The experimental verification confirms that the data-driven model predicted a closer result to the experiments than the physics-based model. Both models succeeded in improving lutein intracellular content by over 40% compared to the highest previous record; however, the data-driven model outperformed the kinetic model when optimising total lutein production and achieved an increase of 40-50%. This indicates the possible advantages of using data-driven modelling for optimisation and prediction of complex dynamic bioprocesses, and its potential in industrial bio-manufacturing systems.

Microbial production of mannitol by Lactobacillus brevis 3-A5 from concentrated extract of Jerusalem artichoke tubers.

In the present study, the conversion of the extract of Jerusalem artichoke tubers for mannitol production by Lactobacillus brevis 3-A5 was investigated. When the bacterium utilized enzymatic hydrolysates of Jerusalem artichoke extract as the main substrates in batch fermentation, the significant decrease in mannitol productivity was observed when the initial concentration of reducing sugar increased. Then, a strategy of continuous fed-batch fermentation was adopted for improving mannitol production with enzymatic hydrolysates of Jerusalem artichoke extract as main substrates. Although the concentration of mannitol could reach 199.86 g/L at the end of the fermentation, the productivity for the overall process of the fermentation was only 1.67 g/L/H. To improve the mannitol productivity with both higher yield and concentration, the simultaneous enzymatic saccharification and fermentation (SSF) was studied. In SSF, the mannitol production reached 176.50 g/L in 28 H with a productivity of 6.30 g/L/H and a yield of 0.68 g/g total sugar. Our study provides a cost-effective and eco-friendly method for mannitol production from a cheap biomass.

Model comparison to describe BHK-21 cell growth and metabolism in stirred tank bioreactors operated in batch mode

Baby Hamster Kidney cells (BHK-21) are commonly used in research and the biopharmaceutical industry. This work aimed to model the kinetic performance in batch operation mode of BHK-21 cells cultured in two stirred tank configurations using different dissolved oxygen concentrations and pH control strategies. Viable and dead cell concentrations, as well as glucose, glutamine, lactate and ammonium concentrations, were monitored. Statistical multiple linear regression, logistic equation and multiplicative Monod kinetic models were fitted. Statistical models for viable cells concentration as a function of nutrient and metabolite concentrations were significant (R2 >0.91). Logistic model parameters: intrinsic growth rate, cell density level in the medium and time for reaching maximum cell concentrations were within 0.061-0.083 h-1, 1.85-5.39 x 109 cell L-1 and 52-90 h ranges, respectively. A Monod-type model was satisfactorily fitted to the experimental data. Relative errors were lower than 10% for six monitored state variables in most of the assessed experimental conditions. The three models developed in this work can be used in bioprocesses involving BHK-21 with good fitting.

An efficient model construction strategy to simulate microalgal lutein photo-production dynamic process.

Lutein is a high-value bioproduct synthesized by microalga Desmodesmus sp. It has great potential for the food, cosmetics, and pharmaceutical industries. However, in order to enhance its productivity and to fulfil its ever-increasing global market demand, it is vital to construct accurate models capable of simulating the entire behavior of the complicated dynamics of the underlying biosystem. To this aim, in this study two highly robust artificial neural networks (ANNs) are designed for the first time. Contrary to conventional ANNs, these networks model the rate of change of the dynamic system, which makes them highly relevant in practice. Different strategies are incorporated into the current research to guarantee the accuracy of the constructed models, which include determining the optimal network structure through a hyper-parameter selection framework, generating significant amounts of artificial data sets by embedding random noise of appropriate size, and rescaling model inputs through standardization. Based on experimental verification, the high accuracy and great predictive power of the current models for long-term dynamic bioprocess simulation in both real-time and offline frameworks are thoroughly demonstrated. This research, therefore, paves the way to significantly facilitate the future investigation of lutein bioproduction process control and optimization. In addition, the model construction strategy developed in this research has great potential to be directly applied to other bioprocesses. Biotechnol. Bioeng. 2017;114: 2518-2527. © 2017 Wiley Periodicals, Inc.

Fed-batch operation for the synthesis of lactulose with ß-galactosidase of Aspergillus oryzae.

Fed-batch synthesis of lactulose from lactose and fructose with Aspergillus oryzae ß-galactosidase was evaluated, obtaining a concentration of 40.4g·L-1, which is 20% higher than obtained in batch, while the concentration of transgalactosylated oligosaccharides (TOS) was reduced by 98%. Therefore, selectivity of lactulose synthesis can be significantly higher by operating in fed-batch mode. The enzyme-limiting substrate mass ratio (E/S) is a critical variable in fed-batch operation. Higher values favor lactose hydrolysis over transgalactosylation, being 400IU/g the limit for proper lactulose synthesis in fed-batch operation. Selectivity of lactulose synthesis increased with E/S being quite high at 800IUH·g-1 or higher. However, this increase was obtained at the expense of lactulose yield. Lactulose synthesis in fed-batch operation was a better option than conventional batch synthesis, since higher product concentration and selectivity of lactulose over TOS synthesis were obtained.

Feeding strategy design for recombinant human growth hormone production by Bacillus subtilis.

Defined and semi-defined medium-based feeding strategies were developed to enhance recombinant human growth hormone (rhGH) production by Bacillus subtilis BGSC-1A178 (scoC (-)) strain carrying pMK4::pre(subC)::hGH. Defined medium-based feeding strategies were designed by exponential feeding of glucose and (NH4)2HPO4 at two pre-determined specific growth rates, µ 0 = 0.10 and 0.17 h(-1). Semi-defined medium-based feeding strategies were designed by exponential feeding of substrate solution consisting of glucose, (NH4)2HPO4, peptone, and trace salt solution (PTM1) at three pre-determined specific growth rates, µ 0 = 0.10, 0.17, and 0.25 h(-1). At all the strategies applied, transition cultivation time from batch to fed-batch operation was t T = 4 h. The highest rhGH concentration was obtained as C rhGH = 0.5 g L(-1) with semi-defined medium-based feeding strategy designed with µ 0 = 0.25 h(-1) using feed substrate stock solution containing 200 g L(-1) glucose, 117 g L(-1) (NH4)2HPO4, 100 g L(-1) peptone, and 5 mL L(-1) PTM1 at t = 22 h when the cell concentration reached to C X = 8.29 g L(-1). The overall product and cell yields on glucose were obtained as [Formula: see text] = 7.21 mg g(-1) and [Formula: see text] = 0.12 g g(-1), respectively. The results indicate the requirement of designing continuous feed stream in fed-batch production to enhance rhGH production by r-B. subtilis.

Perspectives on engineering strategies for improving biofuel production from microalgae--a critical review.

Although the potential for biofuel production from microalgae via photosynthesis has been intensively investigated, information on the selection of a suitable operation strategy for microalgae-based biofuel production is lacking. Many published reports describe competitive strains and optimal culture conditions for use in biofuel production; however, the major impediment to further improvements is the absence of effective engineering strategies for microalgae cultivation and biofuel production. This comprehensive review discusses recent advances in understanding the effects of major environmental stresses and the characteristics of various engineering operation strategies on the production of biofuels (mainly biodiesel and bioethanol) using microalgae. The performances of microalgae-based biofuel-producing systems under various environmental stresses (i.e., irradiance, temperature, pH, nitrogen depletion, and salinity) and cultivation strategies (i.e., fed-batch, semi-continuous, continuous, two-stage, and salinity-gradient) are compared. The reasons for variations in performance and the underlying theories of the various production strategies are also critically discussed. The aim of this review is to provide useful information to facilitate development of innovative and feasible operation technologies for effectively increasing the commercial viability of microalgae-based biofuel production.

Exploring the high lipid production potential of a thermotolerant microalga using statistical optimization and semi-continuous cultivation.

A recently isolated thermotolerant microalga Desmodesmus sp. F2 has the traits of becoming potential biodiesel feedstock, such as high growth rate, high lipid content, and quick precipitation. Its overall lipid productivity was 113 mg/L/d when grown under non-optimal conditions using batch cultivation. A two-step response surface methodology was adopted to optimize its cultivation conditions. The overall lipid productivity was increased to 263 mg/L/d when the cells were grown under the optimized conditions of 6.6mM initial nitrogen level and 6 days nitrogen depletion treatment in 700 µmol/m(2)/s light intensity at 35°C using batch cultivation. Fed-batch and semi-continuous cultivations were employed to further increase its lipid productivity to 213 and 302 mg/L/d, respectively. The 302 mg/L/d is the highest overall lipid productivity of microalgae ever reported in the literature. This study provides the information required for the design and operation of photobioreactors for large scale outdoor cultivation of this species.