Bioremediation of aquaculture wastewater with the microalgae Tetraselmis suecica: Semi-continuous experiments, simulation and photo-respirometric tests.

Tetraselmis suecica was cultivated in a semi-continuously operated tubular photobioreactor fed on aquaculture wastewater (AW) testing two hydraulic retention times (HRT): 10 and 7 days (RUN_1 and RUN_2, respectively). The integrated mechanistic model BIO_ALGAE was validated with experimental data in order to simulate the biomass production and nutrient uptake of T. suecica. Moreover, AW was used as substitute synthetic cultivation medium to test the production of lipids, proteins, and carbohydrates in the microalgal biomass. Preliminary photo-respirometric tests were carried out on the AW suspension containing microalgae and bacteria. Dissolved Inorganic Nitrogen (DIN) and Dissolved Inorganic Phosphorus (DIP) were analyzed for the two RUNs, and no significant difference was highlighted (p > 0.05). On the contrary, the productivity of the Total suspended solids (TSS) was significantly higher (p < 0.05) for RUN_1 (900 mg TSS/L) than for RUN_2 (550 mg TSS/L). The analysis of the biochemical composition of biomass has demonstrated a higher content of proteins than of lipids and carbohydrates for the two RUNs. BIO_ALGAE model was validated by comparing simulated results to experimental data. The model was able to reproduce the pattern of these experimental data quite well, for both nutrient uptake and biomass production. The simulated curve follows the same pattern as the experimental data for both RUNs. The wavelike trend indicates the good accuracy of the simulated curves to reproduce the microalgae growth and nutrient uptake that occurring during daytime and at night. With this study, BIO_ALGAE Model was demonstrated to be useful to simulate bioremediation and microalgae production in aquaculture wastewater in a semi-continuous system with different environmental factors. The photo-respirometric outputs were compared with the process rates affecting dissolved oxygen dynamics computed by the mathematical model.

BIO_ALGAE 2: improved model of microalgae and bacteria consortia for wastewater treatment.

A new set up of the integral mechanistic BIO_ALGAE model that describes the complex interactions in mixed algal-bacterial systems was developed to overcome some restrictions of the model. BIO_ALGAE 2 includes new sub-models that take into account the variation of microalgae and bacteria performance as a function of culture conditions prevailing in microalgae cultures (pH, temperature, dissolved oxygen) over daily and seasonal cycles and the implementation of on-demand dioxide carbon injection for pH control. Moreover, another aim of this work was to study a correlation between the mass transfer coefficient and the hydrodynamics of reactor. The model was calibrated using real data from a laboratory reactor fed with real wastewater. Moreover, the model was used to simulate daily variations of different components in the pond (dissolved oxygen, pH, and CO2 injection) and to predict microalgae (XALG) and bacteria (XH) proportions and to estimate daily biomass production (Cb). The effect of CO2 injection and the influence of wastewater composition on treatment performance were investigated through practical study cases. XALG decreased by 38%, and XH increased by 35% with respect to the system under pH control while microalgae and bacteria proportions are completely different as a function of influent wastewater composition. Model simulations have indicated that Cb production (~ 100 gTSS m-3 day-1 for manure and centrate) resulted lower than Cb production obtained using primary influent wastewater (155 gTSS m-3 day-1).

Microalgae and bacteria dynamics in high rate algal ponds based on modelling results: Long-term application of BIO_ALGAE model.

The mechanistic model (BIO_ALGAE) for microalgae-bacteria based wastewater treatment systems simulation was validated in the long-term (months) using experimental results from a pilot high rate algal pond (HRAP) treating municipal wastewater. Simulated results were compared with data gathered during two different seasons (summer and winter), and with the HRAP operating at different hydraulic retention times (HRT, 4 and 8 days, respectively). The model was able to simulate with a good degree of accuracy the dynamics of different components in the pond, including the total biomass (bacteria and microalgae). By means of practical study cases, the influences of different HRT operating strategies and seasonal variations of temperature and irradiance were investigated for the relative proportion of microalgae and bacteria, and biomass production over a year cycle. Model predictions show that the proportion of microalgae in the microalgal/bacterial biomass is quite similar in warmer months if the pond is operated with 8-day HRT (76-78%) or 4-day HRT (60-75%). Significant differences were observed in colder months (4-day HRT (27-33%) and 8-day HRT (65-68%)). The model identified a scenario in which overall microalgae production and ammonium removal efficiency were optimized. By operating the HRAP with lower HRT (4 days) in warmer months and higher HRT (8 days) in colder months, the average annual microalgae production increased up to 14.1 gTSS m-2d-1, in contrast with 10.2 gTSS m-2d-1 and 9.2 gTSS m-2d-1 operating with constant HRAP (4 and 8 days, respectively) over a year cycle.

Microalgae-bacteria models evolution: From microalgae steady-state to integrated microalgae-bacteria wastewater treatment models - A comparative review.

The search for environmentally neutral alternative fuels had revived the interest for microalgae-bacteria wastewater treatment systems. The potential achieving of bioproducts from microalgae biomass has also greatly contributed. The reactions that occur in these systems are complex, and the degree of scientific knowledge is still scarce compared to that of conventional bacteria wastewater treatments. Mathematical models offer a great opportunity to study the simultaneous effect of the multiple factors affecting microalgae and bacteria, thus allowing for the prediction of final biomass production, and contributing to the system design optimization in terms of operation and control. During the last decades, numerous models describing microalgae growth have been proposed. However, a lower number of integral models considering microalgae as well as bacteria is available. In this paper, the evolution of microalgae models from simple steady-state models (usually dependent on one factor) to more complex dynamic models (with two or more factors) has been revised. A summary of integrated microalgae-bacteria models has been reviewed, outlining their main features and presenting their processes and value parameters. Eventually, a critical discussion on integrated models has been put forward.

Integral microalgae-bacteria model (BIO_ALGAE): Application to wastewater high rate algal ponds.

An integral mechanistic model describing the complex interactions in mixed algal-bacterial systems was developed. The model includes crucial physical, chemical and biokinetic processes of microalgae as well as bacteria in wastewater. Carbon-limited microalgae and autotrophic bacteria growth, light attenuation, photorespiration, temperature and pH dependency are some of the new features included. The model named BIO_ALGAE was built using the general formulation and structure of activated sludge models (ASM), and it was implemented in COMSOL Multiphysics™ platform. Calibration and validation were conducted with experimental data from two identical pilot HRAPs receiving real wastewater. The model was able to simulate the dynamics of different components in the ponds, and to predict the relative proportion of microalgae (58-68% in average of total suspended solids (TSS) and bacteria (30-20% in average of TSS). Microalgae growth resulted strongly influenced by the light factor fL(I), decreasing microalgae concentrations from 40 to 60%. Furthermore, reducing the influent organic matter concentration of 50% and 70%, model predictions indicated that microalgae production increased from (8.7gTSSm-2d-1 to 13.5gTSSm-2d-1) due to the new distribution of particulate components. The proposed model could be an efficient tool for industry to predict the production of microalgae, as well as to design and optimize HRAPs.