rate algal pond
Pham, L. A., Laurent J., Bois P., Wanko A.
ICube, UMR 7357, ENGEES, CNRS, Université de Strasbourg, 2 rue Boussingault, 67000 Strasbourg, France Reviewer’s comments:Add some performance results
Key words:oxygen transfer rate, mixing characteristics, residence time distribution, HRAP.
Abstract
Different combinations of operational parameters including water level, paddle rotation speed and influent flow rate were applied to investigate their impacts on mixing characteristics and gas transfer coefficients in pilot-scale high rate algal pond (HRAP). Paddle rotation speed had positive correlation with Bodenstein number, water velocity and oxygen transfer coefficient while increasing water level put negative impact on these parameters, although the impact of water level on water linear velocity was small. Amplification effect of water level and paddle rotation speed on sensitivity of Bodenstein number (Bo) and kLaO2 should be noticed and considered before applying operational parameters for HRAP system. On the other hand, paddle rotation speed had more impact on kLaO2 than on Bo. Small impact of inlet flow rate as well as HRT on effective volume/total volume ratio was noticed, while higher paddle rotation speed seemed to increase dead zone in HRAP. In this study, the optimal operational conditions included 0.1m water level and 11.6rpm paddle rotation speed (7 Volt applied).
Introduction
Microalgae have received considerable attention as material for biofuel production due to their capacity of accumulating high amount of lipids and carbohydrates. When cultured in suitable conditions, microalgae showed potential oil yield of 58.7 m3/ha/year, while current terrestrial plant used for producing biofuel only reached 5.4 m3/ha/year. Microalgae can use wastewater and flue gas as nutrient sources, which also serve as a treatment unit (Mata, Martins, et al., 2010). In the context of cultivating microalgae (algal biomass) for biofuel production, high rate algal pond (HRAP) showed strong advantages over closed photobioreactor including low energy, financial requirement, easier for maintenance and more feasible in expanding to large scale.
HRAP is a shallow raceway-type pond with paddlewheel as the only source of movement (Park, Craggs, et al., 2010). It was estimated HRAP accounted for 95% of large scale microalgae production facility worldwide (Kumar, Mishra, et al., 2015).One major aspect when operating HRAP is the hydrodynamics because proper mixing allows materials to be evenly distributed in the pond, avoids sedimentation and thus anaerobic condition. Extensive studies had been conducted to investigate the impacts of pond or paddlewheel designs as well as some operational
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conditions on hydrodynamic conditions or energy consumption in the HRAP. Advanced mathematical models were employed to understand flowing patterns in the raceway under such influences (Mendoza, Granados, et al., 2013; Bitog, Lee, et al., 2011; Liffman, Paterson, et al., 2013; Hreiz, Sialve, et al., 2014; Hadiyanto, Elmore, et al., 2013), yet there is still need for experimental validation (Hadiyanto, Elmore, et al., 2013).
Due to its advantages, HRAP can be applied in many places which make its operation, shape and sizes vary depending on local conditions. However, the operational conditions such as water level, paddle wheel movement or inlet flow rate can govern the hydrodynamic characteristics inside HRAP. Moreover, hydrodynamic is one of the major factors influencing gas transfer in open aerobic biological reactor like HRAP. Therefore, varying operational conditions could have a direct impact on biochemical processes or gas transfer and thus on the performance of the system. Thus understanding such difference is important for choosing the suitable operational conditions for optimizing performance of HRAP.
This study aims to determine the impacts of operational conditions including water level, inlet flow rate and paddle wheel movement on hydrodynamics in a pilot scale HRAP. Classical method employing tracer experiment to obtain residence time distributions (RTD) was applied due to its financial saving, availability and effectiveness. Moreover, to understand how such variation in hydrodynamics impacts gas transfer in HRAP, oxygen transfer rate will also be investigated. Finally, an optimal operational condition will be chosen to apply in the pilot HRAP for algal-bacterial biomass cultivation.
Material and Methods Pilot description
The pilot HRAP consists of a single loop race way pond with two straight channels separated by a separation wall and connected by 180o bend at each end. Liquid circulation in the pilot was ensured by a paddlewheel which was driven by a brushed DC motor (DMN37K, 24V, Nidec Servo Corporation, Japan) which was controlled by a bench power supply (ISO-TECH IPS303DD, England). The pilot and paddlewheel were made of transparent plastic (Fig. 1).
Operational conditions applied
Combinations of different water level (0.1, 0.15, 0.2 m), inlet flow rate (6 and 9 L/h) and paddle movement in terms of voltage applied (3.5, 7, 10.5 Volt) were applied. Total water volume of the pilot at water level of 0.1, 0.15 and 0.2 m were 72, 108 and 144 L, respectively. The average paddle rotation speed obtained at voltage applied of 3.5, 7, 10.5 Volt were 5.6 ± 0.4, 11.6 ± 0.9 and 16.8 ± 2.1 rpm, respectively.
HRAP mixing characteristics and hydrodynamic modeling under different operational conditions
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Mixing characteristics and hydrodynamics of pilot HRAP under different operational conditions were investigated by using tracer test (Levenspiel, 1999). Water conductivity correlated with NaCl concentration was measured every 5s by conductivity electrode (TetraCon® 325, WTW, Germany) connected to a multi-parameter portable meter (Multiline P4, WTW, Germany) and recorded with communications software (Multi/Achat II, ver. 1.05, WTW, Germany) (Fig. 1).
For evaluating mixing characteristics inside the pilot, which was mainly due to paddle movement, HRAP was operated in closed condition (inlet flow rate = 0). RTD data obtained was calculated following Voncken, Holmes, et al., 1964 and then used to assess mixing characteristics inside the HRAP. Moreover, in practice, HRAP will be operated in continuous condition, thus RTD data from experiments with continuous operational conditions was calculated based on Levenspiel, 1999 and used to evaluate hydrodynamic behavior of the pilot HRAP. Detailed calculation procedure is indicated in the appendix.
Figure 1. General illustration of pilot HRAP with tracer experiments in open condition (normal figures and text), in closed condition (dashed figure and italic text) and oxygen transfer rate experiments in closed
condition (dashed figures, italic text in brackets)
HRAP gas transfer rates
Oxygen transfer rate determination under different operational conditions was performed following European standard (NF EN 12255-15). Evolution of dissolved oxygen (DO) in water was measured by dissolved oxygen electrode (WTW Inolab Oxi Level II Dissolved Oxygen Meter) connected to a multi-parameter portable meter (Multiline P4, WTW, Germany) and recorded with communications software (Multi/Achat II, ver. 1.05, WTW, Germany). Oxygen transfer coefficient (kLaO2) was calculated following procedure reported by Garcia-Ochoa and Gomez, 2009 which takes into account the dynamic respond of the electrodes.
Sensitivity analysis
Two sensitivity functions were used including the absolute-relative (a-r) sensitivity function measuring the absolute change in the variable for a 100% change in input parameter, and the relative-relative (r-r) sensitivity function measuring the relative change in the variable for a 100% change in input parameter. The a-r sensitivity was used for quantitative comparisons of the
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effect of different parameters (water level, paddle rotation speed) on a common variable y (Bo, kLa). While the r-r sensitivity was used to compare effects of different parameters on different variables (Reichert, 1994). One-way ANOVA following by Holm tests (95 % confidence interval) was applied in R software (version 3.3.1 (2016-06-21)) to compare these effects.
Detailed calculation procedure is indicated in the appendix.
Results and Discussion
Impacts of operational conditions on mixing characteristics
The Bodenstein (Bo) number in the pilot HRAP was calculated according to RTD data obtained.
High values of Bo in every experiment suggested plug flow behavior in the pilot HRAP which is in accordance with literature (Miller and Buhr, 1981). Results indicated that Bo had positive correlation with paddle rotation speed but negative relation with water level (Fig. 2 a.). The average water velocity along the raceway channel which had direct correlation with Bo was also calculated. In practice, it was suggested that water velocity of 0.2 to 0.3 m/s was sufficient for a HRAP. In this study, the required velocity was satisfied even with the lowest power applied (3.5 V or 5.6 rpm). Moreover, higher velocity may cause more energy consumption (Andersen, 2005). Obviously, paddle rotation speed had strong influence on the circulation in the raceway and their correlation was positive. The change in water level has impact on water velocity due to the fact that as the water level increases, a larger paddle area will be immersed: it results in a decrease of the rotation speed of the paddle. However, this impact was small in this study (Fig. 2 b.).
a. b.
Figure 2. Influence of paddle rotation speed, water level to Bodenstein number (a.) and water velocity (b.) in pilot HRAP.
Sensitivity analysis showed that at one water level, Bo was more sensitive with the change of paddle rotation speed from 11.6 to 16.8 rpm than from 5.6 to 11.6 rpm. Moreover, as the water level increased, the sensitivity of Bo with paddle rotation speed also increased (Fig. 3 a.). On the other hand, except at the highest paddle rotation speed, Bo was more sensitive with the change of water level from 0.15 to 0.2 m than from 0.1 to 0.15 m. As the paddle rotation speed decreased, the sensitivity of Bo with water level increased (Fig. 3 b.). Since the Bodenstein number
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represents the ratio of the total momentum transfer over the molecular mass transfer, any increase in Bo value may lead to an increase in advection and hence shear stress which can damage algal cells (Mata, Martins, et al., 2010). Therefore, the amplification of Bo sensitivity with paddle rotation speed at high speed and/or high water level should be considered before choosing the operational conditions for HRAP.
a. b.
Figure 3. Absolute-Relative sensitivity (dimensionless) of Bodenstein number versus paddle rotation speed (a.) and water level (b.). The sign represents positive (no sign) or negative (- sign) correlation.
Impacts of operational conditions on O2transfer rates
Oxygen transfer coefficients (kLaO2) due to mechanical mixing were calculated from experimental data. It was showed that kLaO2 in HRAP had positive correlation with paddle rotation speed and negative correlation with water level which was in good agreement with Bo values obtained (Fig. 4). It suggests that higher paddle rotation speed causes more mixing in water and thus more oxygen can be transferred. On the other hand, higher water level (higher volume of fluid) in the reactor decreases mechanical mixing caused by the paddle and thus decreases oxygen transfer coefficient.
Figure 4. Influence of paddle wheel rotation, water level to oxygen transfer coefficient in pilot HRAP.
Results from sensitivity analysis indicated that kLaO2 was more sensitive with the change of paddle rotation speed from 11.6 to 16.8 rpm than from 5.6 to 11.6 rpm. The decreasing of water level also caused higher sensitivity of kLaO2 with paddle rotation speed (Fig. 5. a.). Water level
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changing from 0.1 to 0.15 m caused more change in kLaO2 than when changing from 0.15 to 0.2 m. As the paddle rotation speed decreasing, the sensitivity of kLaO2 with water level also decreased with the only exception in rotation speed of 5.6 rpm (Fig. 5. b.). In practice, better gas transfer rate has benefits for the HRAP system including reducing the occurrence of oxygen saturation or anaerobic condition, and hence avoid stressful condition for algae. Therefore, the increased sensitivity of kLaO2 at high paddle rotation speed and/or at low water level should be considered as precaution.
a. b.
Figure 5. Absolute-Relative sensitivity (d-1) of oxygen transfer coefficient (kLaO2) versus paddle rotation speed (a.) and water level (b.). The sign represents positive (no sign) or negative (- sign) correlation.
Impacts of operational conditions on HRAP hydrodynamics in continuous mode
Result of effective volume/total volume ratios indicated that there is a significant proportion of dead zone (up to 50% at the highest paddle rotation) in the reactor. The increase of paddle rotation speed induced a significant decrease of the effective volume ratio. At the lowest paddle rotation speed, impacts of higher water levels and inlet flow rates resulted to slight increases of the effective volume/total volume ratio. However, these differences decreased as the paddle rotation speed increased. Therefore, the paddle wheel rotation speed and inlet/outlet positions should be optimized to favor mixing time while avoiding dead zone.
Figure 6. Influence of paddle wheel rotation, water level and inlet flow rate (corresponding HRT put in brackets) to effective volume/total volume ratio in pilot HRAP.
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Evaluating the impacts of operational conditions on HRAP performance
To compare the impacts of different operational parameters including water level, paddle rotation speed on kLaO2 and Bo in closed operational condition, relative-relative sensitivity of kLaO2 and Bo with water level and paddle rotation speed was employed (Fig. 7). kLaO2 was more sensitive with paddle rotation than with water level (p value < 0.05). In addition, the sensitivities of Bo with water level and paddle rotation speed were similar (p value > 0.05). On the other side, water level had similar sensitivities with kLaO2 and Bo (p value > 0.05), while paddle rotation speed had higher sensitivity with kLaO2 than with Bo (p value < 0.05). These results suggested stronger impacts on kLaO2 from paddle rotation speed than from water level. Similar degree of influences was seen between water level and paddle rotation speed on Bo. Moreover, paddle rotation speed had more impacts on kLaO2 than on Bo. It may suggest changing paddle rotation speed would be more efficient if one wants to improve the kLaO2.
Although according to results in this study, a combination of lowest water level and highest paddle rotation should be the best option to achieve optimal hydrodynamics and gas transfer efficiency in HRAP, it may not the case when considering system performance. High paddle rotation speed can provide better mixing, increase gas transferring and reduce dead zone but it also increases shear stress causing cell damage and consumes greater power, thus increasing the cost (Hadiyanto, Elmore, et al., 2013). Moreover, in this study, global hydrodynamics result indicated that higher rotation speed is likely to keep more biomass staying in the reactor.
Therefore, the best combination of operational conditions in this study should be between water level of 0.1m and paddle rotation speed of 11.6rpm (7 Volt applied).
Figure 7. Average Relative-Relative sensitivities (dimensionless) of oxygen transfer coefficient and Bodenstein number versus water level and paddle rotation speed. Data was converted to absolute value for comparison.
Conclusion
In this study, different combinations of water level, paddle rotation speed and influent flow rate were applied to investigate their impacts on mixing characteristics oxygen and carbonic transfer coefficients of the pilot HRAP. In general, the pilot HRAP showed good mixing level even with the lowest paddle rotation speed applied, and hence the entire HRAP can be considered as a CSTR. Bodenstein number, water velocity and oxygen transfer coefficient had positive
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correlation with paddle rotation speed but negative correlation with water level although the impact of water level on water linear velocity was small. Amplification effect of water level and paddle rotation speed on sensitivity of Bo and kLaO2 should be noticed and considered before applying operational parameters for HRAP system. Paddle rotation speed had more impact on kLaO2 than on Bo. Small impact of inlet flow rate as well as HRT on effective volume/total volume ratio was noticed, while higher paddle rotation speed seemed to increase dead zone in HRAP. The optimal operational conditions included 0.1m water level and 11.6rpm paddle rotation speed (7 Volt applied). These data obtained could be useful for calibrating 3D hydrodynamic model for better studying the impact of operational conditions on HRAP.
References
Andersen, R. A. (2005) Algal Culturing Techniques, Academic Press.
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(2011) Application of computational fluid dynamics for modeling and designing photobioreactors for microalgae production: a review. Computers and Electronics in Agriculture, 76(2), 131–147.
El Ouarghi, H., Boumansour, B. E., Dufayt, O., El Hamouri, B., and Vasel, J. L. (2000) Hydrodynamics and oxygen balance in a high-rate algal pond. Water science and technology, 42(10-11), 349–356.
Garcia-Ochoa, F. and Gomez, E. (2009) Bioreactor scale-up and oxygen transfer rate in microbial processes: An overview. Biotechnology Advances, 27(2), 153–176.
Hadiyanto, H., Elmore, S., Van Gerven, T., and Stankiewicz, A. (2013) Hydrodynamic evaluations in high rate algae pond (HRAP) design. Chemical engineering journal, 217, 231–239.
Hreiz, R., Sialve, B., Morchain, J., Escudié, R., Steyer, J.-P., and Guiraud, P. (2014) Experimental and numerical investigation of hydrodynamics in raceway reactors used for algaculture. Chemical Engineering Journal, 250, 230–239.
Jupsin, H., Praet, E., and Vasel, J.-L. (2003) Dynamic mathematical model of high rate algal ponds (HRAP). Water science and technology, 48(2), 197–204.
Kumar, K., Mishra, S. K., Shrivastav, A., Park, M. S., and Yang, J.-W. (2015) Recent trends in the mass cultivation of algae in raceway ponds. Renewable and Sustainable Energy Reviews, 51, 875–885.
Levenspiel, O. (1999) Chemical reaction engineering, Wiley.
Liffman, K., Paterson, D. A., Liovic, P., and Bandopadhayay, P. (2013) Comparing the energy efficiency of different high rate algal raceway pond designs using computational fluid dynamics. Chemical Engineering Research and Design, 91(2), 221–226.
Mata, T. M., Martins, A. A., and Caetano, N. S. (2010) Microalgae for biodiesel production and other applications:
A review. Renewable and Sustainable Energy Reviews, 14(1), 217–232.
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Miller, S. B. and Buhr, H. O. (1981) Mixing characteristics of a high-rate algae pond. Water SA, 7(1). [online]
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WaterSA_1981_%207_0202.PDF (Accessed November 25, 2016).
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Appendix
Calculation details:
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Bodenstein (Bo) number calculation:
ಮ =ට
ସగఏσ ݁ݔ ቂെ
ସఏ(݆ െ ߠ)ଶቃ
ஶୀଵ (1A)
With C is the concentration of tracer detected, C is the concentration of tracer at infinite time and șis the dimensionless time which is denoted as ș=t/tc (tcis circulation time and t is time).
Oxygen transfer coefficient (kLaO2) calculation:
ܥ =ܥכ+ כିబ
ଵିఛೝಽכ ቂ߬݇ܽ݁ݔ ቀି௧
ఛೝቁ െ ݁ݔ(݇ܽ כ ݐ)ቃ (2A)
With Cme is the oxygen concentration measured by the electrode and C0 is the oxygen concentration at the initial time of the aeration while C* is equilibrium value of oxygen concentrationIJris the response time of the electrode and t is time.
Sensitivity functions:
ߜ௬,,=డ௬
డ (3A)
ߜ௬,, =
௬ డ௬
డ (4A)
With y is the variable that changes due to the change of parameter p.
Calculation of hydrodynamic parameters in continuous condition:
Residence Time Distribution (RTD):
The RTD function E(t) can be defined as E(t)ǻt = fraction of incoming water that stays in the reactor for a length of time between t and ǻW,WLVFDOFXODWHGDVIROORZV
ܧ(ݐ) = ொ(௧)(௧)
ொ(௧)(௧)ௗ௧బಮ =σ ொ(௧ொ(௧)(௧)
)(௧)ο௧ (5A) Mean residence time (ݐҧ):
ݐҧ= ݐܧஶ (ݐ)݀ =σ ݐܧ(ݐ)οݐ (6A) Variance (ıZKLFKLVDPHDVXUHRIWKH57'FXUYH¶VVSUHDG
ߪ=ஶ(ݐ െ ݐҧ)ଶܧ(ݐ)݀ݐ=σ ݐଶܧ(ݐ)οݐ െ ݐҧଶ (7A) Dimensionless variance:
ߪఏଶ =ఙమ
௧ҧమ (8A)
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Calculation of uL/D ratio for a closed vessel:
ߪఏଶ = 2ቀ
௨ቁ െ2ቀ
௨ቁଶൣ1െ ݁ି௨ Τ ൧ (9A)
With D is dispersion coefficient, u is water velocity and L is the length between input and measurement points.
Calculation of effective volume/total volume ratio:
௧ҧ
ுோ் (10A) With HRT is hydraulic retention time of the reactor.
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