Optimised hydrodynamic parameters for the design of ...

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Research Paper

Optimised hydrodynamic parameters for the design of photobioreactors using computational fluid dynamics and experimental validation Jessie Pascual P. Bitog a, In-Bok Lee b,*, Hee-Mock Oh c, Se-Woon Hong d, Il-Hwan Seo b, Kyeong-Seok Kwon b a

Department of Agricultural Engineering, Nueva Vizcaya State University, 3700 Bayombong, Nueva Vizcaya, Philippines b Department of Rural Systems Engineering and Research Institute for Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, 599, Gwanakno, Gwanakgu, 151-921 Seoul, South Korea c Biosystems Research Division Laboratory, Korea Research Institute of Bioscience and Biotechnology, Daejeon, South Korea d M3-BIORES: Measure. Model, Manage Bioresponses, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium

article info

A numerical simulation using computational fluid dynamics (CFD) was utilised to inves-

Article history:

tigate the flow hydrodynamics of cylindrical bubble column type photobioreactors (PBRs)

Received 18 July 2013

with a 30 l culture medium. To establish the reliability of the simulation study, the CFD

Received in revised form

model was validated using particle image velocimetry (PIV) computed data under various

14 February 2014

air flow rates. There were 32 simulation cases for the study comprising two PBR designs,

Accepted 7 March 2014

four air flow rates and four nozzle size diameters. Hydrodynamic analyses such as %

Published online

volume of dead zones, average circulation time and turbulence intensity inside the simulated PBRs were evaluated. Results have shown that the most appropriate PBR for

Keywords:

microalgae cultivation was a design with internal baffle and an extended cone-shaped

Algae biomass concentration

bottom section. In addition, the recommended nozzle diameter was found to be 10 mm

Circulation time

and a minimum air flow rate of 0.10 vvm. To eliminate dead zones inside the PBR, the flow

Computational fluid dynamics (CFD)

rate can be slightly increased but should not exceed 0.15 vvm. Practical evaluation through

Particle image velocimetry (PIV)

laboratory experiments has further confirmed the results of the study where the biomass

Dead zones

concentration of Chlorella vulgaris from the proposed PBR was significantly higher

Turbulence intensity

compared to the standard PBR design. Based on the numerical investigation and practical evaluation, the improved PBR can be seen to be more effective in culturing microalgae particularly for larger scale mass production. ª 2014 IAgrE. Published by Elsevier Ltd. All rights reserved.

* Corresponding author. Tel.: þ82 2 880 4586; fax: þ82 2 873 2087. E-mail addresses: [email protected] (J.P.P. Bitog), [email protected] (I.-B. Lee). http://dx.doi.org/10.1016/j.biosystemseng.2014.03.006 1537-5110/ª 2014 IAgrE. Published by Elsevier Ltd. All rights reserved.


Nomenclature k 3

turbulent kinetic energy turbulent dissipation rate

Abbreviations CFD computational fluid dynamics PBRs photobioreactors PIV particle image velocimetry TSS total suspended solid RNG renormalization group vvm air volume added to the culture medium volume per minute

1.

Introduction

Bubble column photobioreactors (PBRs) which are efficient in growing photosynthetic cells have received enormous attention over the past decade. This design of PBR offers many advantages such as simplicity in the design with no moving parts, simplicity of construction and ease of operation (Bitog et al., 2011). They have suitable heat and mass transfer characteristics and require less operational cost because of their low energy input requirements. From an engineering perspective, the structural configuration and design of the PBRs have a critical role in the flow hydrodynamics which is also important in providing ideal growth conditions for the cells. Thus, the hydrodynamics of bubble column PBRs of various geometries has been investigated both in the laboratory and through simulation studies (Table 1). However, the current PBR designs still need to resolve many hydrodynamic issues within the PBR especially for the process of scaling-up. Many equally important factors should be considered such as light penetration and distribution, gas injection and mixing, the cell species and the cells response to shear stress (Bitog et al., 2011; Michels, van der Goot, Norsker, & Wijffels, 2010; Perez, Porcel, Lopez, Sevilla, & Chisti, 2006). In culturing high density cells in PBRs, the main criteria are good mixing, mass transfer and light utilisation (Chiu, Tsai, Kao, Ong, & Lin, 2009). These factors are closely interrelated such that the penetration and diffusion of light inside the PBRs is affected by the mixing characteristics which in turn are strongly influenced by the gas injection method (Bitog et al., 2011). In bubble column PBRs, flow hydrodynamics is realised through bubbles which are usually introduced at the bottom section. The bubbles also provide more time for mass transfer and CO2 supply. The reactant gas itself provides the stirring action that is required to conduct gaseliquid and gaseliquidesolid interactions and reaction (Rampure, Kulkarni, & Ranade, 2007). The higher superficial gas velocity generated by air bubbles in PBRs should result in better mixing, but also increase the shear forces which have been long suspected to cause cell death. Tramper, William, Joustra, and Vlak (1986) distinguished three regions where the cells are likely to experience too much stress which may cause the death of the cells. High shear stresses are suspected to occur at the sparger where the bubbles are formed, in the path

43

where the bubbles rise, and at the surface where bubbles break-up. Reducing the shear stress of microalgae cultures in sparged PBRs was the focus of the study by Barbosa, Hadiyanto, and Wijffels (2003) who reported that bubble formation at the sparger was the main cause of cell death. Therefore, finding an optimum air flow rate which will drive significant mixing but not exceed the shear forces that can cause cell death is important. Various reports have shown different growth rates of algae when cultivated in different growing conditions and the geometry of the PBR cannot be ignored. It is probably one of the most critical factors to consider especially when scaling up is required. Zuzuki et al. (1995) found a linear correlation between the specific death rate and the inverse of culture height; however, they provided no explanation for this. Camacho, Gomez, Sobczuk, and Grima (2000) reported the inverse behaviour, i.e. an increase in culture height caused an increase in death rate. The authors related the effect to cell attachment of bubbles and suggested that a greater height of rise meant that more cells can be captured by the rising bubbles and carried to the surface where they die as the bubbles rupture. However, Barbosa et al. (2003) disputed this claim by stressing that bubble bursting is not the only factor and might not even be the most important factor leading to cell death. Despite the different conclusions drawn, these authors have shown that the sparger site has a major effect on cell damage and the gas entrance velocity should be considered as a possible indication of cell death. However, more work is still required to clarify the influence of sparging on cell death and its scalability in reactor-design for different microalgae strains (Barbosa et al., 2003). Increasing the culture height may also cause longer cycle times which can decrease photosynthetic efficiency (Janseen, Slenders, Tramper, Mur, & Wijffels, 2001). Also, rapid circulation has been shown to give rise to considerable higher photosynthetic efficiency (Matthijs et al., 1996). Thus, the required growing conditions of photosynthetic cells in the height of bubble column PBRs are limited in scale. According to Miron, Gomez, Camacho, Grima, and Chisti (1999) the PBR diameter is also limited to some extent. For instance, to ensure light penetration inside the PBR, the diameter should not exceed 200 mm or light availability will be severely reduced. However, until now, no standard PBR geometry or working volume has been recommended in terms of the mass production of photosynthetic cells such as algae. Also, the hydrodynamics inside the PBR are strongly influenced by the geometry of the structure which directly affects the light intensity and mixing conditions, thus structure optimisation is not only necessary, but also critical (Yu et al., 2009). Recently, numerical simulations have been applied to investigate reactor designs which are always guided by the purpose of the production facility, the cell strain and product of interest. Computational techniques have been used to simulate a large variety of engineering and physical systems. Specifically, the simulation approach attempts to imitate the hydrodynamic behaviour of a system and predict the sequences of events which control that behaviour (Oran & Boris, 2001). In PBR design and analysis, numerical simulation approach in studying fluid flow inside PBRs is now

44


Table 1 e A summary list of typical bubble column PBRs. Type

Culture volume

Cylindrical column Rectangular bubble column Cylindrical column

20 l

Cylindrical column, Cylindrical column with centric tube and porous centric tube Square type column

4l

Rectangular column Cylindrical column with centric tube Rectangular column

15 l 20 l

Rectangular column

12 l

Cylindrical column

30 l

Cylindrical column

63 l

Cylindrical column

60 l

Cylindrical column

35 l

Cylindrical column Cylindrical column

13 l 24 l

Rectangular column

4.5 l

Square type column

26 l

Cylindrical column

20 l

21 l

10 l

8l

Focus of the study Comparison of Multi-phase models, Validation of CFD code from PIV data Mass transfer and shear, Mathematical model to improve reactor design and performance Laboratory study on flow patterning high-density cultures of microalgae and carbon dioxide removal EulerianeEulerian modelling of flow, mass transfer and chemical reaction Optimisation of inner structure parameters Local characteristics of hydrodynamics in the reactor Simulation on drag force formulation Modelling of two-phase flow using class method of population balance Application of volume of fluid (VOF) model and study on the effect of air distributor and validated with laboratory experiment on superficial gas velocity CFD and Laboratory study on the effect of high gas velocity Mixing as a new approach to characterize dispersion coefficients CFD analysis of flow validated with laboratory experiment Simulation of algal growth Validation of a transient, two-dimensional axisymmetric simulation Simulation using EulerianeEulerian modelling approach Effect of the aspect ratio on of the bubble column on flow pattern Laboratory experiment of the stirred tank photobioreactor for biohydrogen production

widely recognised among design engineers as an effective tool in predicting the complex inherent phenomena inside PBRs especially in cases where using an experimental approach is restricted by technical constraints (Bitog et al., 2011). The flow hydrodynamics inside a PBR plays a critical role in terms of providing ideal growth environment for cells. The flow affects gas holdup and volumetric mass transfer in the PBRs which are equally important to achieve sufficient CO2 and nutrients for the cells. This becomes complex because the liquid flow is affected by several factors such as the PBR size and design, gas flow velocity. Numerical simulation studies have already attempted to investigate one or more of these factors. For instance, gas hold-up and the volumetric mass transfer coefficients on a phenomenological model for bubble breakup and coalescence in column reactors were investigated by Shimizu, Takada, Minekawa, and Kawase (2000) using computational fluid dynamics (CFD). They proposed a compartment model to describe the bubble movements. Their simulation study did not provide a complete description of bubble behaviours although it gave

Reference/year Seo et al., 2012 Bannari, Bannari, Selma, & Proulx, 2011 Chiu et al., 2009

Zhang et al., 2009 Yu et al., 2009 Luo & Al-Dahhan, 2008 Simonnet, Gentric, Olmos, & Midoux, 2008 Bannari, Kerdouss, Selma, Bannari, & Proulx, 2008 Akhtar et al., 2007

Rampure et al., 2007 Rubio et al., 2004 Baten et al., 2003 Wu & Merchuk, 2002 Sanyal et al., 1999

Pfleger, Gomes, Gilbert, & Wagner, 1999 Delnoij, Kuipers, & van Swaaij, 1999 Ogbonna, Soejima, & Tanaka, 1998

significant insights into the phenomena occurring in bubble column reactors. A similar study was conducted by Baten and Krishna (2002) where CFD was utilised to investigate bubble characteristics inside a bubble column PBR under homogenous and heterogeneous regimes. Their results revealed that in heterogeneous flow regimes, the larger bubbles are found to concentrate in the central core of the bubble column, while the small bubbles are distributed throughout the column. The holdup of small bubbles was found to be constant in the heterogeneous flow regime. A follow-up study was conducted by Baten, Ellenberger, and Krishna (2003) where the hydrodynamics of internal air-lift reactors was investigated and the experimental results were compared with CFD simulations. A scaling up model was developed from the results but it is only applicable in air-lift reactors. In their scaled-up model, there was a significant reduction in the gas holdup caused by significantly higher liquid recirculation. The authors stressed the need for experimental verification especially when scaling up is involved. Yamashita and Suzuki (2007) studied gas holdup inside the PBR, which is an important parameter for the


2.

Fig. 1 e Flowchart of the study.

Materials and methods

The flowchart of the study is presented in Fig. 1. PIV was utilised to validate the CFD code and approach in the design of PBRs. After establishing the reliability of the numerical approach, CFD simulations of the PBRs followed where several PBR cases were analysed in terms of their suitability in microalgae production. In the CFD simulation, PBR designs and operating conditions were selected and recommended for the practical cultivation of microalgae. The hydrodynamic parameters which were analysed to evaluate the PBRs included the percentages of dead zones, the average circulation time and turbulence intensity. These are the main and critical hydrodynamic factors which have significant effects on the performance of PBRs as a vessel for microalgae production (Bitog et al., 2011; Suh & Lee, 2003). Cultivation of microalgae in the standard PBR, and in the recommended PBR design, was conducted to confirm the results obtained by the simulation.

2.1. design and scale up of bubble columns PBRs. Their CFD investigation showed that gas holdup depends on many factors such as gas and liquid velocity, physical properties of gas and liquid, type and arrangement of gas spargers, gas inlet height and the inclination of the column. Drag forces on bubbles in bubble swarms were investigated by Roghair, Annaland, and Kuipers (2009) who focused on the presence of neighbouring bubbles on the drag as a function of the void fraction. They found out that the normalised drag coefficient increases for higher void fractions. They recommended that the effects of preferential horizontal alignment on the averaged drag experienced by the bubbles should be taken into account explicitly in drag closure correlations. This research has provided a profound understanding of the complex flow characteristics in the PBRs. However, despite of the numerous research attempts, simulation results which can be applied in designing a PBR suitable for the mass production of microalgae is still limited. In this study, a numerical simulation using CFD technique was implemented to investigate hydrodynamics characteristics for cylindrical 30 l bubble reactors. Particle image velocimetry (PIV) was used to validate the initial CFD code and the results were utilised to improve the simulation models. Flow hydrodynamics, as affected by various air flow rates, nozzle diameters and the effect of additional internal baffles were investigated. The hydrodynamic measuring parameters used in the analysis were the volume percentages of dead zones, average circulation time and turbulence intensity. Turbulence intensity measures the ratio of the root-meansquare of the velocity fluctuations to the mean free stream velocity. The optimum air flow rate and recommended nozzle size diameter appropriate for the 30 l PBR was determined. The effect of adding an internal baffle, in terms of hydrodynamic measuring parameters, was also quantified. The cultivation of microalgae followed a final step towards further validating the CFD technique as a promising tool in PBR designs.

45

Particle image velocimetry

PIV is an optical method for flow visualisation of a system. The technique is used to obtain instantaneous velocity measurements and related properties of the fluids inside a system. PIV works by seeding tracer particles which are assumed to faithfully follow the flow dynamics following the principle of Stokes number (Raffel, Willert, & Kompenhans, 1998). Based upon the definition of velocity, i.e. the first derivative of position with respect to time, the technique consists in measuring the displacement of fluid over a given time interval (Brossard et al., 2009). The particles are illuminated using a laser so that they become visible for the specialised digital camera which captures multiple frames at high speed. The images of the tracer particles are recorded at least twice with a small time-delay with the displacement of the particle images representing the fluid’s motion. The flow movements of particles are then being tracked where the velocity and direction of the particles are recorded for computer analysis. More details of PIV principle and procedure can be found in literature such as Brossard et al. (2009), Stanislas, Okamoto, and Kahler (2003), Raffel et al. (1998), and Westerweel (1997). This methodology of validating CFD simulations in reactors was used by Sheng, Meng, and Fox (1998) in a stirred tank, and the use of the PIV technique has become increasingly important in simulation studies where it plays an important role in validating the modelling approaches. Several related studies which utilised the PIV technique in column reactors are available such as Shigeya, Hiroyuki, Zhong, Taro, and Shunji (2005), Medvitz, Reddy, Deutsch, Manning, and Paterson (2009) and Seo et al. (2012). A schematic diagram of a PIV system in this study is presented in Fig. 2A plane within the flow is illuminated twice by means of two superimposed laser light sheets and the light scattered by the particles is recorded on two separate frames on a special cross-correlation CCD camera sensor (Brossard et al., 2009). A high speed camera (Nikon AF Nikkor 50 mm f/1.8D, Japan) was used to capture images every 0.1 s. The collected PIV data were post processed using available commercial computer software such as

46


Fig. 2 e Schematic diagram for PIV test used in analysing internal fluid flow pattern. Source: Seo, 2013.

Insight 3G and Tecplot software (TSI Incorporated, Shoreview, MN, USA). Insight 3G acquires, analyse images such as velocity field, or particle images as well as scalar image file and displays the global properties while Tecplot is an additional tool which provides viewing options in processing the results. In this study, a two dimensional velocity vector field is obtained which is used to validate the CFD simulations.

2.2.

Computational fluid dynamics

In multiphase flow systems, CFD has proved to be a powerful tool used to study fluid flows showing the ability to predict the hydrodynamics and related flow characteristics inside a reactor (Hutmacher & Singh, 2008). This tool uses the power of computers and the numerical techniques to solve problems involving the movement of fluids in a system. Using CFD, a

computational model can be built that represents a system or device, and by applying fluid flow physics and chemistry to this virtual prototype, a prediction of the fluid dynamics and the related physical phenomena can be achieved (Bitog et al., 2011). The CFD technique numerically solves the NaviereStokes equations within each cell of the computational domain. The use of CFD is nowadays regarded as an effective tool for overcoming the limitations of field and laboratory experiments at limited cost. CFD can be used to study factors in PBRs that influence the flow hydrodynamics that influence the liquid currents in the column; e.g. superficial gas velocity, gas holdup, bubble diameter, column geometry, antifoaming material and pressure. Predicting the hydrodynamics of bubble columns in laboratory experiments is difficult, and the liquid currents are also hard to predict. However, research has shown that the hydrodynamics of bubble columns can be

Fig. 3 e Image of the PBR investigated in the PIV experiment.


47

30 l, the water level was approximately 1115 mm. An injection hole with a nozzle diameter of 10 mm was drilled at the centre bottom of the PBR where air could be easily introduced. The PBR was vertically supported by square frames with movable and adjustable wheels. Air pump was connected to the bottom of the PBR via a hose connected to the nozzle that in turn was directly connected to the injection hole. The air flow rate was controlled with a regulator and a flow meter attached to the air pump. PIV was conducted with 8 experimental cases testing four air flow rates (0.05 0.1, 0.15 and 0.2 vvm) and 2 experimental regions (regions 1 and 2, Fig. 3). The CFD code to be implemented in the modelling and simulation of the PBRs was validated by comparing the average flow velocities computed from the PIV data and CFD simulations. Aside from visual comparison of the flow between the PIV and simulation results, their computed average velocity magnitudes were quantitatively compared. The PIV data were obtained from each flow rate after approximately 5 min whereupon the flow becomes stable and the flow characteristics inside the PBR could be assumed to be solely affected by the set air flow rate.

2.3.2.

Fig. 4 e The 30-l PBR design models investigated in the study in terms of their flow hydrodynamics, (a) PBR without baffle and (b) PBR with baffle and protruded bottom design (dimensions in mm).

simulated using CFD simulations (Kommareddy & Anderson, 2004). The application of CFD to PBR design is becoming more popular, as computer performance increases and becomes affordable, providing more rapid computation and the capability to solve even complex geometries (Bitog et al., 2011). To implement the CFD technique in this study, the geometry and mesh of the 30 l cylindrical bubble column PBRs were created using Gambit software (ver. 2.4, Fluent, Inc., Lebanon, NH, USA) while the calculations were performed using Fluent software (ver. 6.3, Fluent, Inc., Lebanon, NH, USA) as well as post-processing of the results.

2.3.

Experimental procedure

2.3.1.

PIV tests for CFD validation

Validation is a critical part in simulation studies and is required to establish the reliability of the numerical approach. PIV tests were initially conducted in a model PBR to validate the CFD code to be used in the numerical modelling and simulation. The bubble column PBR used in the PIV test was a standard design type as shown in Fig. 3 but was made of 5 mm transparent acrylic plastic with an inner diameter of 185 mm. The height was 1400 mm. Therefore, for a culturing capacity of

CFD simulation of the 30 l PBRs

Pre-processing of models is very crucial to obtain reliable results in simulation studies. These require proper experience and skills on how to create the geometry of the model and make the right decisions on what mesh type and mesh size to be used. The geometry and mesh of the 30 l cylindrical bubble column PBRs were created using Gambit software (ver. 2.4, Fluent Inc., Lebanon, NH, USA). The geometry of the standard bubble column PBR is not complex (Fig. 4), thus only the type of mesh and its size should be appropriately executed. However, the PBR structure with internal baffle and extended cone-shaped bottom was complex, and several approaches were attempted to achieve an acceptable mesh quality. In Fig. 5 is the top view of the PBR according to a mesh interval size ranging from 0.001 to 0.011 m which was simulated to study mesh size dependence. The mesh type implemented was tetrahedron. The total number of mesh elements employing varied mesh interval sizes of 0.001, 0.003, 0.005, 0.007, 0.009 and 0.011 m and the estimated time for simulations is presented in Table 2. Mesh quality was checked using equiangular skewness value, which should range from 0.25 to 0.50 (Bakker, 2002; Fluent Manual, 2006). The meshing of the designed geometry of the PBRs was realised by dividing the PBR to 4 sub-parts such as: 1) the injection zone or the protruded geometry, 2) the internal baffle, 3) the main column (which is hollow without the internal baffle), and 4) the upper part which comprises the air zone. This approach can allow comfortable meshing, and an acceptable mesh quality can also be achieved. In the end, the mesh geometry of PBR with internal baffle and an extended cone-shaped bottom was realised using pyramid and mostly hexahedron meshes. Fig. 6 shows the mesh design of the PBR with internal baffle and an extended cone-shaped bottom. The calculations were performed using the Fluent software (ver. 6.3, Fluent Inc., Lebanon, NH, USA) as well as postprocessing of the results. To ensure a good agreement with the PIV experimental data, the grid mesh size interval as well

48


Fig. 5 e Top view of the PBR showing the employed mesh interval size.

as the time step size employed in the simulation were varied and the average velocity magnitude of both PIV and simulation data were compared. The LagrangianeEulerian multiphase model was utilised in the study. This model has been widely used for modelling gaseliquid or liquideliquid flows. Furthermore, it is applicable over a wide range of volume fractions and a turbulence model is automatically included. More detail information on multiphase models is available elsewhere in the literature (Akhtar, Pareek, & Tade, 2007; Bertola, Vanni, & Baldi, 2003; Bitog et al., 2011; Fluent Manual, 2006). The data and values employed in the simulation are presented in Table 3.The renormalisation group (RNG)-based- ke3 -turbulence model was used. The model uses constants that differ from those in the standard-ke3 -model with additional terms and functions in the transport equations (Fluent Manual, 2006). A comprehensive description of RNG theory and its application to turbulence can be found in Choudhury (1993). This turbulence model has been successfully used in similar PBR simulations by other authors (Akhtar et al., 2007; Perner et al., 2003; Sanyal, Vasquez, Roy, & Dudukovic, 1999; Trujillo et al., 2007). To simulate bubble rising, the bubble size diameter was fixed at 0.005 m which

Table 2 e Total mesh nodes at varied mesh size interval and the estimated time for simulation. Mesh size interval, m 0.001 0.003 0.005 0.007 0.009 0.011

Total no. of mesh nodes

Approximate simulation time, h

3,465,247 1,825,403 622,500 226,077 111,061 64,071

336 225 140 115 100 90

coincides with the minimum mesh grid size implemented in the mesh design. Simulations of the PBR with and without baffle at increasing nozzle diameters of 5, 10, 15 and 20 mm and air flow rates of 0.05 0.1, 0.15 and 0.2 vvm were conducted. The evaluation parameters employed to evaluate the hydrodynamic performance of the PBRs were the volume percentages of dead zones, average circulation time and turbulence intensity. The simulation results were analysed from the point of view of providing a good hydrodynamic environment for microalgae cells to grow. Based on the hydrodynamic parameters used to evaluate the PBRs, a PBR simulation case was chosen with the design that provided the optimum performance in terms of lower volume of dead zones, higher average circulating time and low to moderate turbulence intensity. The main factors investigated in the PBR design were as follows: 1) Air flow rate with 4 sub-factors (0.05, 0.10, 0.15 and 0.20 vvm), 2) Nozzle size diameter with 4 sub-factors (5, 10, 15 and 20 mm), and 3) PBR geometries with 2 sub-factors. This gave 4 4 2 ¼ 32 simulation cases. In implementing the simulation of the PBRs, the air inlet velocity was varied according to the simulation cases based particularly on the air flow rates and nozzle size diameters as presented in Table 4. The 2 sub-factors of the PBR geometry simulated were with or without internal baffle. In addition in the PBR that included an internal baffle, the lower part was re-designed to have an extended cone-shaped bottom. As previously stated, the PBR performance was evaluated in terms of the volume percentages of dead zones, average circulation time and the turbulence intensity. Dead zones as defined by Yu et al. (2009) are the zones in which liquid velocity is 1.0 103 m s1. The percentages of dead zones in the PBR were quantified comparing the 32 simulation cases. The regions where some dead zones appeared could provide

49


Fig. 6 e The mesh design of the 30-l PBR with internal baffle and an extended cone-shaped bottom pre-processed using Gambit software.

information and suggest some measures to be taken in order to eliminate such zones. The presence of dead zones causes settling of cells which could result in cell deterioration, anaerobic decomposition and lowering of the quality of the product (Suh & Lee, 2003). The average circulation time referred to this study is defined as the average travel time it takes for the particles to travel from the bottom of the PBR to the watereair interface and back. The average circulation can be determined by introducing particle cells at the bottom of the PBRs and

monitoring them to determine their locations as they move vertically to the watereair interface and back. The cells were simulated appropriately assuming them to be mass-less and volume-less particles and treated as a passive particulate tracer in the continuous phase, because the size of the microalgae is very small, i.e. about 0 105 to 0 106 m (Sato, Yamada, & Hirabayashi, 2010). In this manner, Sato et al. (2010) pointed out that the cells are transported even with small eddies in a spatial scale smaller than the mesh. Therefore, to account for this phenomenon, a random walk tracking

Table 3 e Data and variables implemented in the simulations. Pre-processing (Gambit software)

PBR without baffle

PBR with baffle

Main module (Fluent program)

Inner diameter Height of water zone Height of air zone Total number of mesh Inner diameter Height of water zone Height of air zone Height of injection zone (cone-shaped) Total number of mesh

185 mm 1150 mm 250 mm 500,428 185 mm 1032 mm 218 mm 250 mm 622,500

Solver Multiphase model Phases Specified operating density Turbulence Near-wall treatment Discretization Conditions Bubble size Time interval

Pressure based (implicit) Lagrangian-Eulerian Water (primary), Air (secondary) 1.225 kg m3 RNG ke3 turbulence model (dispersed) Standard wall functions Second order upwind Unsteady state 0.005 m 0.01 s

50


Table 4 e The air inlet velocity implemented in the simulation of the PBRs. Air flow rate, vvm Inlet velocity implemented in the simulation, m s1 (according to air flow rate and nozzle size diameter)

0.05 0.10 0.15 0.20

5 mm nozzle

10 mm nozzle

15 mm nozzle

20 mm nozzle

1.27 2.55 3.82 5.09

0.32 0.64 0.95 1.27

0.14 0.28 0.42 0.57

0.08 0.16 0.24 0.32

model was implemented. The discrete random walk tracking approach works by tracking each particle injection individually through the domain. Each injection is repeatedly tracked in order to generate a statistically meaningful sampling (Fluent Manual, 2006). Considering bubble column PBRs where air, CO2 and other nutrients are usually introduced at the bottom, a shorter travel time would be preferable to maximize the mass transfer rate. To ensure enough nutrition is supplied to the PBRs, both the CO2 fixation rate and O2 evolution rate will depend mainly on the gas bubble travel time and the gas liquid mass transfer rate (Fan et al., 2007). Furthermore, rapid circulation can imply shorter cycle time and thus can result in faster mixing. This can also result in higher photosynthetic efficiency (Janseen et al., 2001; Matthijs et al., 1996). Turbulence has been proven to be advantageous to cells because it causes continuous shift in the relative position of the cells with respect to the photic zones which relates to the flashing light effect (Terry, 1986). However, too much turbulence can be very detrimental to cells which can result not only in restrictions to the algal growth and metabolic activity but also in cell death. The three main zones inside the PBR where shear stress to cells occurs as identified by Tramper

et al. (1986) were investigated in terms of their turbulence. These zones were previously investigated by Camacho (2000), Barbosa et al. (2003) and Zhong and Yuan (2009) through laboratory experiments. The results obtained by Camacho (2000) showed that more cell deaths occurred at the watereair interface zones. Zhong and Yuan (2009) also reported similar results to those of Camacho (2000); however, this was earlier disputed by Barbosa et al. (2003) who found out that most cell death took place at the injection zone, but, the effect of bubble size on cell death has not been related to bubble formation at the sparger. Such conflicting results from previous studies were investigated analysing the CFD simulations in this study. Based on the three hydrodynamic parameters such as the volume percentage of dead zones, average circulation time and turbulence intensity, the most appropriate PBR was chosen and were recommended for the actual cultivation of microalgae cells. Applying the hydrodynamic parameters using elimination technique was executed here. There is no available standard in terms of allowable dead zones inside the PBRs, thus the case with the lowest volume percentage of dead zone would preferable if the dead zone were the only criterion. However, this was not the case since the circulation time and

Fig. 7 e The standard bubble column PBR (right) and the upgraded PBR (left) utilised in the two successive experiments, (a) view at the start of the experiment when the light is off and (b) view at the end of the experiment and light is on.

51


Table 5 e Comparison between the standard and the upgraded PBR where Cholera vulgaris cells are cultivated. Characteristics

Typical PBR

Upgraded PBR

Design geometry

Without Baffle and the bottom part is flat

Nozzle size Air flow rate Culture medium height PBR diameter Baffle diameter Baffle height Light intensity Temperature CO2 Initial cell concentration

10 mm 0.10 vvm 1050 mm 185 mm

With Baffle and the bottom part is protruded (cone-shaped) 10 mm 0.10 vvm 1110 mm 185 mm 85 mm 700 mm

e e Equally supplied with 20pcs of 28 kW Fluorescent light 15e25 C 15e25 C Under 10% level Under 10% level Approximately 0.5 g l1 and 2.0 g l1 for Experiments 1 and 2, respectively.

turbulence intensity need be considered as well. The same problem occurs for the average circulation time and turbulence intensity which also should not be used as the only criterion. Generally, it was observed that the cases with lower volume percentages of dead zones also displayed shorter circulation time. Considering that this was the first attempt of combining the hydrodynamic parameters to select the appropriate PBR, the following criteria were developed for choosing which PBR designs are preferable among the 32 simulated cases: criterion 1 e volume percentages of dead zones shall not be more than 10%; criterion 2 e average circulation time shall not exceed 10 s; and criterion 3 e the

turbulence intensity should be within the low to moderate category. Although it is true that the growth rates of microalgae increase initially with increasing turbulence, this decreases sharply with further increases in gas velocity due to cell damage (Merchuk et al., 2000).

2.3.3.

Laboratory experiment in 30 l PBRs

Finally, laboratory experiments were conducted using the standard PBR design and the recommended PBR design developed using the CFD simulations in order to compare and quantify the growth of microalgae cultivated to verify if the new design, with its better hydrodynamics, increased

Fig. 8 e Visual comparison of average velocity magnitude compared to PIV data at 0.005 mesh grid size interval.

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Fig. 9 e Velocity magnitude obtained from simulation compared to PIV at varied air flow rates.

productivity accordingly. The standard PBR described earlier in the PIV test was used as a comparison. The upgraded PBR was constructed with internal baffle while the lower have an extended cone-shaped bottom. It was also constructed from 5 mm transparent acrylic panel with an inner diameter of 185 mm. The internal baffle was basically an open acrylic cylindrical tube with an inner diameter of 65 mm and a height of 700 mm. The total height of the upgraded PBR including the cone-shaped structure was 1500 mm. The vertical height of the cone-shaped bottom part was 250 mm with a nozzle diameter of 20 mm. The introduction of air and CO2 for consumption by the cells was executed via bubbles from the lower part of the PBRs. The PBRs were surrounded with 28 kW artificial fluorescent lights where 5 fluorescent lights were equally distributed in each side of the PBRs resulting in a supply of approximately 560 kW artificial light. Although the quality of the light provided was not exactly the same as daylight, the high intensity light was achieved which is similar to the average daylight intensity. The distance

between the PBRs and the fluorescent bulbs was approximately 150 mm. The PBRs were supported with square aluminium frames where the fluorescent light bulbs were also installed. A photograph of the PBRs with their light installations is shown in Fig. 7, and a comparison of the standard and upgraded PBR is presented in Table 5. Both PBRs received the same amount of light intensity and supplied with equal levels of CO2 under the same temperature for the entire duration of the experiment. Two sets of laboratory experiments were conducted where the first and second set of experiment has an initial biomass concentration of approximately 0.5 g l1, and 2.0 g l1, respectively. Microalgae were cultivated in N-8 culture medium following Lee and Palsson (1994); Lee and Lee (2001), and included 1.0 g l1 [KNO3], 0.74 g l1 [KH2PO4], 0.21 g l1 [Na2HPO4], 0.05 g l1 [MgSO4$7H2O], 0.0132 g l1 [CaCl2$2H2O], 0.01 g l1 [FeeNa EDTA]. The N8 medium is commonly used for culturing Chlorella vulgaris because of its capacity to support high-density cultures on the basis of elemental stoichiometric composition of the algae (Ramkumar, Mandalam, & Palsson, 1998). Samples of approximately 50 ml were collected daily at the same particular time from the top, middle and bottom parts of the PBRs. The samples were weighed immediately after the sampling and the biomass content of the collected samples was estimated gravimetrically using total suspended solid (TSS) in a 47 mm diameter Whatman GF/C filter paper. The filters used in the test were initially dried overnight in a container with silica gel and weigh before being used to filter the collected samples. The wet mass of the filter paper was determined after allowing water to pass though it using suction pump in a specialised container. Immediately after determining the wet mass of the filter paper, the samples were poured into the container and were forced to pass through the wet filter using the same suction pump. The cells and water content from the samples were then separated where the cells remained into the filter paper where its weight was immediately measured. The difference in mass between the wet filter paper and the wet filter paper with cells gives an estimate of the biomass content or the fresh cell mass.

Table 6 e Comparison of average velocity magnitude computed from PIV and CFD simulation.


Fig. 10 e Percentage comparison of CFD computed average velocity magnitude according to mesh grid size to PIV at varied air flow rates (air flow rates (vvm): > e 0.05, , e 0.10, 6 e 0.15, B e 0.20).

3.

Results and discussion

3.1.

Validation of the CFD model

In this study, the average velocity magnitude obtained from PIV and simulation data were visually and quantitatively compared (Figs. 8 and 9; Table 6). In Fig. 8, the PIV results correspond to the average velocity magnitudes obtained from 300 captured images in each test case, which were postprocessed using Insight 3G and Tecplot software (TSI Incorporated, Shoreview, MN, USA). In Table 6, the computed

53

velocity magnitude obtained from CFD simulations at 0.001, 0.003 and 0.005 mesh grid sizes was comparable to the computed average velocity magnitude obtained with the PIV measurements as highlighted in dotted square shape. The computed percentage differences of overall average velocity magnitudes compared to PIV were approximately 5.28, 5.81, 6.78, 12.67, 21.83, and 29.31% for 0.001, 0.003, 0.005, 0.007, 0.009 and 0.011 mesh grid interval sizes, respectively. Since the air flow rate is the main catalyst of flow characteristics in the PBR, it is expected that as the air flow rate is increased, the average velocity magnitude will also increase. As a consequence of that, the percentage differences in velocity magnitude compared to the PIV measurements was shown to increase at higher flow rates. The percentage differences can neither be solely attributed to the air flow rate nor to the mesh grid size used in the model as previously discussed. Figure 10 shows the percentage difference between CFD computed average velocity magnitude according to mesh grid size and PIV values for increasing air flow rates. The percentage comparison showed that mesh grid sizes of 0.007, 0.009, and 0.011 were decreasing while mesh grid sizes of 0.005, 0.003, and 0.001 m displayed comparable values to PIV which is approximately 95%. The difference is acceptable (Bitog et al., 2009, 2011; Celik, 1993; Lee et al., 2013; Roache, Ghia, & White, 1986). Therefore, any of these 3 mesh grid sizes could be chosen in simulations. However, to minimise computational time without sacrificing accuracy, a mesh grid size of 0.005 was considered a suitable choice. To demonstrate this, the percentage differences of the average velocity magnitude between the PIV data and simulation results at varied air flow rates of 0.05, 0.10, 0.15 and 0.20 vvm using 0.005

Fig. 11 e Fluid flow visualisation inside the PBR obtained in one of the PIV test cases showing chaotic behaviour with regards to the flow of bubbles.

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mesh grid size were as follows: 4.05, 6.78, 7.09 and 9.18%. These percentage differences were also below 10%, a value generally acceptable in computer modelling studies.

3.2.

Mesh grid interval size

Simulation results showed that the mesh grid interval size does not only affect the circulation time but also has a significant effect on the computed average velocity magnitude. Mesh grid interval sizes of 0.007, 0.009 and 0.011 m particularly displayed higher percentage difference of velocity magnitude compared to PIV (Fig. 10) with a root mean square error of 0.031, 0.059, 0.077, respectively while mesh grid sizes of 0.001, 0.003 and 0.005 m displayed comparable values to the PIV findings with root mean square error of 0.006, 0.011 and 0.016, respectively. These results suggest that any of these mesh grid interval sizes could be chosen for succeeding simulations. The smaller the mesh grid size, the longer time was required to complete the simulation. However, this problem could be

Table 7 e Volume percentages of dead zones of the 32 CFD simulated cases. Case A1B 1C 1 A1B 1C 2 A1B 2C 1 A1B 2C 2 A1B 3C 1 A1B 3C 2 A1B 4C 1 A1B 4C 2 A2B 1C 1 A2B 1C 2 A2B 2C 1 A2B 2C 2 A2B 3C 1 A2B 3C 2 A2B 4C 1 A2B 4C 2 A3B 1C 1 A3B 1C 2 A3B 2C 1 A3B 2C 2 A3B 3C 1 A3B 3C 2 A3B 4C 1 A3B 4C 2 A4B 1C 1 A4B 1C 2 A4B 2C 1 A4B 2C 2 A4B 3C 1 A4B 3C 2 A4B 4C 1 A4B 4C 2

Volume percentage of dead zones, %

Average circulation time, s

8.21 3.66 18.86 11.41 34.58 26.61 42.51 33.67 7.08 3.12 11.01 4.87 22.21 14.61 29.81 20.67 6.61 3.12 6.31 4.18 14.88 9.51 26.66 18.85 5.24 2.87 9.51 4.65 13.67 8.11 20.08 11.66

14.88 9.21 19.57 13.81 25.66 20.01 26.89 22.81 15.65 9.08 16.21 10.08 20.51 14.48 22.31 17.71 14.05 8.41 15.55 9.51 18.51 13.67 21.41 15.66 13.84 8.05 15.01 9.29 17.22 12.41 19.88 13.91

A: Air flow rates of 0.05, 0.10, 0.15 and 0.20 vvm corresponding to A1, A2, A3 and A4, respectively. B: Nozzle size diameter of 5, 10, 15 and 20 mm corresponding to B1, B2, B3 and B4, respectively. C: PBR geometry without baffle and with baffle corresponding to C1 and C2, respectively.

reduced by employing a parallel computing system which would significantly lessen the simulation time. The percentage difference of average velocity magnitude compared to PIV at mesh grid interval sizes of 0.001, 0.003 and 0.005 m considering all air flow was determined to be less than 10%. Therefore, a mesh grid size of 0.005 m was chosen as a compromise between quality of the simulation results and simulation time, which is minimised. Furthermore, these results are almost in congruence with the results obtained by Seo et al. (2012) where a mesh size of 0.004 m was selected to simulate bubble flow in a 2 l PBR.

3.3.

Visualisation of fluid flow using PIV

From the PIV tests it is also possible to analyse the hydrodynamic parameters such as the mixing or dead zones inside the PBR. Dead zones are regions where the fluid velocity is too low and can be assumed that the plug flow is experienced in that region. The flow is also visualised to investigate if a uniform flow distribution is achieved. This is very important for the microalgae cells particularly affecting for their light absorption. The movement of fluid inside the PBR caused by the injection of air at the bottom of the PBR can be clearly visualised by eye during the conduct of the experiments. The formation of bubbles, their flow characteristics as the bubble rise in the PBR, as well the areas where they disintegrate can be easily observed. Moreover, as the air flow rate was increased, more bubbles were introduced and more turbulence inside the PBR was clearly observed. More detailed observation showed that the velocity of the fluid was higher in sections where the bubbles were rising. The larger the bubbles, the higher the velocity observed and computed. This is clearly shown in Fig. 11 where a series of PIV snap shot were taken in one of the experimental cases. The flow of the bubbles inside the PBR showed a chaotic behaviour which made the validation from this data almost impossible. Thus, the average flow velocity data obtained from the total 300 images in each case was used. This also showed the regions where good mixing and higher

Fig. 12 e Effect of inlet velocity used as boundary condition in the simulation in terms of the volume percentage of dead zones inside the PBRs (> e with internal baffle, 6 e without baffle).

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diameter, a decreasing trend on the volume percentages of dead zones was observed (Fig. 12) following the power-law, y ¼ 10.57x52 with R2 value of 0.921 for the PBRs without baffle while y ¼ 5.3652x0.695with an R2 value of 0.945 for the PBRs with baffle. Furthermore, simulation results revealed that most of the dead zones were located at the lower part of the PBR and near the walls particularly for the PBRs without internal baffle, whilst for the PBRs with baffle and cone-shaped bottom design; the dead zones were found at the outer surface of the inner baffle. They were, however, minimal. This strongly suggests that the cone-shaped bottom of the upgraded PBR nearly eliminated dead zones, particularly at the bottom and injection regions of the PBRs. Fig. 13 e Effect of inlet velocity used as boundary condition in the simulation in the terms of the computed average circulation time.

turbulence could occur. Thus, the regions where bubbles were rising experienced better mixing and turbulence, which usually coincided with the inner section of the PBR whilst poor mixing was likely to occur close to and near walls.

3.4.

PBR design study using CFD

3.4.1.

Percentage of dead zones inside the PBRs

The quantitative results of the volume percentage of dead zones from the 32 simulated CFD multiphase models are presented in Table 7.The PBR cases were represented using an ABC notation where A stands for the air flow rates of 0.05, 0.10, 0.15 and 0.20 vvm which corresponds to A1, A2, A3, and A4, respectively. B stands for the nozzle size diameter of 5, 10, 15, and 20 mm which corresponds to B1, B2, B3, and B4, respectively; while C was used for the PBR geometry with baffle and without baffle which corresponds to C1 and C2, respectively. In terms of the percentages of dead zones inside the PBRs, the case with the smallest volume percentage was observed for Case A4B1C2 (2.87%) while the highest was observed for Case A1B4C1 with 42.51%. The effect of a baffle and a cone-shaped geometry decreased the percentages of dead zones in the PBRs. Regardless of air flow rate and nozzle size diameter, the average percentage of dead zones for the PBRs without baffle and with baffle was estimated to be 17.25 and 11.35%, respectively, while the highest reduction of the percentage of dead zones for the PBRs without baffle to PBRs with internal baffle was approximately 10%. Also, considering the effect of air flow rate regardless of nozzle diameter and PBR geometry, the percentages of dead zones were observed to decrease from 22.44, 14.21, 11.35 and 9.20% at air flow rates of 0.05, 0.10, 0.15, and 0.20 vvm, respectively. However, if the effect of nozzle diameter is considered, an increasing trend is observed with the percentage of dead zones estimated to be 4.55, 9.13, 18.02, and 25.45% when the nozzle diameter was increased from 5, 10, 15, and 20 cm, respectively. The various results obtained in all the simulated cases suggested that the three factors such as air flow rate, nozzle size diameter and PBR geometry all have significant effect on the volume percentages of dead zones within the PBR. For the case of air flow rate and nozzle

3.4.2.

Average circulation time in the PBRs

The average circulation time for the particles to travel from the bottom of the PBR to the watereair interface and back is

Table 8 e Average turbulence intensity value of the 32 CFD simulated cases. Case

A1B 1C 1 A1B 1C 2 A1B 2C 1 A1B 2C 2 A1B 3C 1 A1B 3C 2 A1B 4C 1 A1B 4C 2 A2B 1C 1 A2B 1C 2 A2B 2C 1 A2B 2C 2 A2B 3C 1 A2B 3C 2 A2B 4C 1 A2B 4C 2 A3B 1C 1 A3B 1C 2 A3B 2C 1 A3B 2C 2 A3B 3C 1 A3B 3C 2 A3B 4C 1 A3B 4C 2 A4B 1C 1 A4B 1C 2 A4B 2C 1 A4B 2C 2 A4B 3C 1 A4B 3C 2 A4B 4C 1 A4B 4C 2

Average turbulence intensity (%) Injection zone

Bubble rising zone

Watereair interface

4.45 2.67 0.57 2.55 8.57 10.02 6.31 7.81 10.05 11.68 1.55 1.61 11.67 12.01 9.02 9.88 9.91 10.51 1.77 1.79 13.58 13.66 9.51 10.12 5.95 7.98 2.12 2.22 14.52 12.31 13.04 13.88

0.43 0.88 0.24 2.24 6.64 7.71 5.04 5.57 8.50 9.55 1.22 1.26 9.06 9.80 7.81 8.21 7.81 7.97 1.44 1.55 12.01 12.18 8.12 9.16 4.89 5.92 1.54 1.78 12.11 11.88 10.55 11.02

2.68 3.82 0.97 3.86 10.81 11.00 10.21 11.20 10.88 10.89 4.25 4.45 13.61 14.05 11.50 12.01 11.66 12.96 3.33 3.78 16.67 16.51 12.61 13.81 7.56 8.88 3.58 4.99 18.21 17.74 14.41 15.01

A: Air flow rates of 0.05, 0.10, 0.15 and 0.20 vvm corresponding to A1, A2, A3 and A4, respectively. B: Nozzle size diameter of 5, 10, 15 and 20 mm corresponding to B1, B2, B3 and B4, respectively. C: PBR geometry without baffle and with baffle corresponding to C1 and C2, respectively.

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Table 9 e Selection of cases appropriate for microalgae cultivation based on the set hydrodynamic criteria.

also summarized (Table 7). These results were obtained by introducing virtual particle cells at the bottom of the PBRs and monitoring the particles to determine their locations as they move vertically from the bottom of the PBR to the watereair interface and back. The shortest average circulation time, comparing all the simulated cases was observed in Case A4B1C2 with approximately 8.05 s. This case also provided the lowest simulated percentages of dead zones, as previously stated. The longest circulation was observed for Case A1B4C1 with approximately 26.89 s. General observations also revealed that the average circulation time was directly influenced by the air flow rate, in a similar way to the observations made in the volume percentages of dead zones as presented in Fig. 13. As the air flow rate is increased, the distribution of the computed average

circulation time tended to decrease following a power-law equation (y ¼ 10.195x0.259) with an R2 value of 0.743. Further analysis of the results has revealed that with the same flow rate and nozzle size diameter, the average circulation time of PBRs with baffle was lower by an average of about 5.5 s when compared with the PBRs without baffle. Regardless of the nozzle diameter and PBR geometry, the average circulation times were estimated to be 19.10, 15.63, 14.60, and 13.70 s for 0.05, 0.10, 0.15, and 0.20 vvm flow rates, respectively. This shows that except for the lowest simulated air flow rate (0.05 vvm), the average circulation time obtained by the rest of simulated air flow rates was very similar. As nozzle diameter was varied, regardless of the geometry and air flow rates, the average circulation times were 11.52, 13.63, 17.81 and 20.07 s, for the nozzle diameters of 5, 10, 15 and 20 mm, respectively.


This implies that nozzle diameter played a significant role in terms of circulation times in the PBRs. This was expected since the air velocity input value used in the simulation depended on the nozzle size diameter. This therefore suggests that nozzle diameter can be selected based on the average circulation time. This suggestion also supports earlier laboratory findings of Meier et al. (1999) and Zhong and Yuan (2009) that nozzle diameter has a minimal effect on the mortality of the cells.

3.4.3.

Turbulence intensity in the PBRs

The three regions where shear forces are generated, and are implicated in cell death, were investigated and compared in terms of turbulence intensity. These regions correspond to the zones which are at 20 mm above the injection zone, at the vertical path where the bubble rises and at 20 mm below the watereair interface, following Tramper et al. (1986). In the Fluent program, the turbulence is characterised as high, moderate and low turbulence when the turbulent intensity is 10%, 1% 10%, and 1%, respectively (Fluent Manual, 2006). The quantitative results presented in Table 8 show that higher turbulent intensities occur in the watereair interface zone where bubble breaks-up, followed by the injection zone and then the bubble rising region. The highest turbulence was observed at the watereair interface in case A4B3C1 with 18.21%. Furthermore, results revealed that high turbulence intensity was always registered in the simulation cases when the nozzle size diameter was 15 and 20 mm regardless of the PBR design (with or without internal baffle). Because of the wider nozzle size diameter, the bubbles formed were also larger. As these bubbles travel and disintegrate at the watereair interface, higher turbulence can be expected. Interestingly, with wider nozzle size diameter of 15 and 20 mm, the set air flow rate was lower compared to 5 and 10 mm, and if the flow rate had a significant effect on turbulence, the turbulence obtained in 5 and 10 mm should have been higher. However, the results showed otherwise. Thus, the bubbles which are being formed when air is introduced to the PBRs are therefore suspected to cause higher turbulence during break-up. This was also observed in the investigation conducted by Yu et al. (2009) where they utilised turbulence as one of the parameters to optimise the inner structure of a flat PBR in their two-dimensional CFD simulations, which also showed higher turbulence values at the watereair interface. Generally, most of the simulated PBR cases revealed a low to medium turbulence intensity in the bubble rising zone while all the PBR designs revealed medium to high turbulence intensity at the injection zone. Although a direct link between turbulence intensity to growth and death of cells is not

Table 10 e The cases that passed the set criteria which can be chosen for cultivating microalgae. Case A1B1C2 A2B2C2 A3B2C2 A4B1C2

Air flow rate, vvm

Nozzle size diameter, cm

0.05 0.10 0.15 0.20

5 10 10 5

PBR design With With With With

baffle baffle baffle baffle

57

strongly established in the literature, the findings obtained here further support the work of Camacho et al. (2000) and Zhong and Yuan (2009) who observed that more cell deaths took place at the watereair interface zones where the bubble ruptures.

3.4.4. Selection of appropriate PBRs combining the three hydrodynamic evaluation parameters Based on the hydrodynamic investigation, particularly on the percentages of dead zones, average circulation time and turbulence intensity, the most appropriate PBR for improved productivity was chosen and recommended for the cultivation of microalgae cells. Table 9 presents the results of applying the elimination method with the previously mentioned criteria. In the table, cases marked with letter “O” are those that passed the set criterion while those with “X” did not. The cases that were highlighted in a rectangular box passed the 3 set criteria and therefore it was concluded that they were appropriate cases for microalgae cultivation. These cases are further summarised in Table 10. In the selected cases, in terms of the PBR design parameters, all of them include the internal baffle and protruded geometry while two cases have the 5 mm nozzle diameter and the remaining 2 cases have the 10 mm nozzle size. However, considering the construction of the PBR, it would be very difficult to implement a 5 mm nozzle diameter especially when the lower section of the PBR is extended and cone-shaped. Therefore, case A1B1C2 and case A4B1C2were dropped from the selection list and only cases with nozzle diameters of 10 mm are therefore chosen. In terms of the air flow rate, the selected 2 remaining cases have air flow rates of 0.10 and 0.15 vvm. Air flow rate is an operating parameter, thus it can be easily adjusted in practise. However, considering that air flow rate has an influence in terms of volume percentages of dead zones in the PBRs, it is be recommended that the minimum air flow rate for the PBR under investigation of 0.10 vvm is used. This value can be slightly increased to minimise the dead zones inside the PBR, but it should not exceed 0.15 vvm air flow rate.

3.5.

Cultivation of microalgae

The cultivation of microalgae on the selected PBR was undertaken to further cement the findings of the study. Two sets of laboratory experiments were conducted from September to October, 2012. Microalgae were cultivated in the standard bubble type PBR and the selected PBR based on the hydrodynamic investigation reported here as the upgraded PBR. C. vulgaris was chosen to be cultivated considering its great potential as a resource for biodiesel production due to faster growth and ease of cultivation. Chlorella is a single celled microalga that grows in fresh water ponds and lakes. Its colour is a brilliant deep green, due to its high amount of chlorophyll. However, lipids content in C. vulgaris under general growing conditions consists of up to 20e30% by mass of dry biomass (Illman, Scragg, & Shales, 2000; Spolaore, JoannisCassam, Duran, & Isambert, 2006), which does not meet the standard industrial requirements. The fresh cell weight of C. vulgaris measured from the standard and upgraded PBR for experiment 1 is presented in

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Fig. 14 e Fresh cell mass of Chlorella vulgaris obtained in experiment 1.

Fig. 14. From the figure, it can be observed that higher cell concentrations can be achieved in days 7, 8, and 9 for the upgraded PBR with values of approximately 11.17, 11.55 and 11.96 g l1, respectively. The maximum concentration was achieved in day 10 for the standard PBR with 5.26 g l1. Computing the average cell concentration for the upgraded PBR where the concentration is divided by the days of cultivation gave values of 1.60, 1.44 and 1.33 g l1 for days 7, 8, and 9 respectively. Obviously, day 7 gave the highest average cell concentration which clearly showed that maximum cultivation of microalgae could be achieved three days earlier for the upgraded PBR compared to the standard PBR. The decrease in time to obtain a higher cell concentration using the upgraded PBR is very significant. For instance, in one month, batch cultivation of microalgae could be implemented four times in the upgraded PBR while only three times can be achieved with the standard PBR. Furthermore, the average cell concentration for the standard PBR was only approximately 0.53 g l1 as compared to the upgraded PBR which was 1.60 g l1. From day 0 to day 3, the biomass concentration obtained from both PBRs showed minimal increase in growth which can be interpreted as the cells being in their adaptation phase. The exponential

growth phase of microalgae cultivated in the upgraded PBR occurred over day 3 to day 7, thus delivering higher multiplication of cells. The standard PBR showed slightly increasing growth. In terms of their specific growth rates, the maximum specific growth rate for the standard PBR was obtained in day 4 with an approximate value of 2.21 day1, while approximately 2.58 day1 was achieved in the upgraded PBR in day 5. The maximum productivity of the PBRs, in g l1 day1, which is computed by multiplying the maximum specific growth rate (day1) and the average fresh cell weight or density (g l1) yielded a productivity of approximately 1.15 g l1 day1 and 3.43 g l1 day1 for the standard and the upgraded PBRs, respectively. The results obtained from experiment 2 displayed similar growth trends despite of the higher initial biomass concentration as shown in Fig. 15. However, higher cell concentrations could be obtained in shorter time from day 1. The peak cell concentration for the standard PBR was approximately 5.64 g l1 in day 8 and 11.55 g l1 for the upgraded PBR obtained in day 6. Thus, computing the average fresh cell weight concentration for experiment 2 gave values of approximately 0.71 and 1.93 g l1 for the standard and the upgraded PBRs, respectively. The specific growth rate obtained in Experiment 2 showed a maximum value approximately 1.40 day1 for the typical PBR in Day 8 while approximately 1.56 day1 was obtained for the upgraded PBR in Day 3. This further confirms the result obtained in Experiment 1 where maximum fresh cell weights were obtained in the upgraded PBR in shorter time than the typical PBR. Estimating the productivity of the PBRs by applying similar procedure to that of experiment 1, yielded maximum productivities of approximately 0.99 g l1 day1 and 3.01 g l1 day1 for the standard and upgraded PBRs, respectively. Computing the average maximum productivities for the two successive experiments showed values of approximately 1.03 g l1 day1 and 3.23 g l1 day1 for the standard PBR and upgraded PBR, respectively. This showed an increase in productivity of approximately 214% by the upgraded PBR.

4.

Fig. 15 e Fresh cell mass of Chlorella vulgaris obtained in experiment 2.

Conclusions

From an engineering perspective, the structural configuration and design of the PBRs have a critical effect in the flow hydrodynamics which in turn are very significant in providing ideal growth conditions for the microalgae cells. In this study, CFD was utilised to investigate the flow hydrodynamics inside a standard and upgraded bubble column cylindrical PBRs. The standard PBR and the upgraded PBR are similar in size and configurations; however, the geometry of the bottom section differs where the upgraded PBR is extended with a coneshaped geometry and also has a cylindrical baffle. The baffle used to guide the movement of the growing medium and provide better circulation and mixing. Thirty-two cases were simulated in the study with various air flow rates, nozzle diameters and PBR geometry designs. The percentage volume of dead zones, circulation time and turbulence intensity were used to investigate the performance


of the PBRs. Simulation results revealed the hydrodynamic advantages of the PBRs designed with internal baffle and an extended cone-shaped bottom. This design can eliminate the dead zones and at the same time give better mixing and mass transfer because of its shorter average circulation time. The volume percentages of the dead zones in the PBRs without baffle were located at the bottom of the reactor and near the walls. Thus, it is suspected that cell coagulation and settling are likely to happen in these regions. Nozzle diameter has been shown to significantly influence the average circulation time. This is consistent with the expected hydrodynamic flow analysis since the input value of air velocity in the simulation depends on the nozzle diameter, especially when the PBRs have the same air flow rates. The regions where cell death is suspected to occur because of shear forces were also investigated in terms of turbulence intensity, which was classified as low (1%), moderate (1% 10%) and high turbulence (10%). Results showed that turbulence intensities were higher in the watereair interface zones further confirming the results reported by Camacho (2000) and Zhong and Yuan (2009). Moderate to high turbulence was observed in the injection zones while low to moderate turbulence was observed in the bubble rising region. Based on the hydrodynamic investigation, the most appropriate PBR design was chosen and utilised for the actual cultivation of microalgae cells. C. vulgaris were cultivated in the standard bubble column PBR and the upgraded design and the measured daily biomass concentrations of the microalgae cells were compared. Computing the average maximum productivities for the two successive experiments showed values of approximately 1.03 g l1 day1 and 3.23 g l1 day1 for the standard PBR and upgraded PBR, respectively. This showed an increase in productivity of approximately 214% utilising the upgraded PBR. This shows that the upgraded PBR is significantly more efficient in culturing C. vulgaris compared with the standard bubble column cylindrical design. Hence, based on the present investigation, the upgraded design should be tested for larger scale microalgae biomass production. Finally, it can be concluded that the CFD approach has demonstrated its capability in investigating flow hydrodynamics inside the PBRs which can result in design of efficient and effective PBRs for the cultivation of microalgae. The approach is very promising for new research designs of PBRs while reducing the time, labour and resources required to develop the designs.

references

Akhtar, A., Pareek, V., & Tade, M. (2007). CFD simulations for continuous flow of bubble through gaseliquid columns: Application of VOF method (Vol. 2). The Berkeley Electronics Press. Issue 1, Article 9. Bakker, A. (2002). Applied computational fluid dynamics, Lecture 7: Meshing. Fluent Inc.. http://www.bakker.org Bannari, R., Bannari, A., Selma, B., & Proulx, P. (2011). Mass transfer and shear in an airlift bioreactor: using a

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mathematical model to improve reactor design and performance. Chemical Engineering Science, 66(10), 2057e2067. Bannari, R., Kerdouss, F., Selma, B., Bannari, A., & Proulx, P. (2008). Three-dimensional mathematical modeling of dispersed twophase flow using class method of population balance in bubble columns. Computers and Chemical Engineering, 32, 3224e3237. Barbosa, M. J., Hadiyanto, & Wijffels, R. H. (2003). Overcoming shear stress of microalgae culture in sparged photobioreactors. Wiley InterSciencehttp://dx.doi.org/10.1002/bit.10862. Baten, J. M., Ellenberger, J., & Krishna, R. (2003). Hydrodynamics of internal air-lift reactors: experiments versus CFD simulations. Chemical Engineering and Processing, 42, 733e742. Baten, J. M., & Krishna, R. (2002). CFD simulations of a bubble column operating in the homogeneous and heterogeneous flow regimes. Chemical Engineering Technology, 25, 1081e1086. Bertola, F., Vanni, M., & Baldi, G. (2003). Application of computational fluid dynamics to multiphase flow in bubble columns. International Journal of Chemical Engineering, 1. Article A3. Bitog, J. P., Lee, I.-B., Lee, C.-G., Kim, K.-S., Hwang, H.-S., Hong, S.W., et al. (2011). Application of computational fluid dynamics for modeling and designing photobioreactors for microalgae production: a review. Computers and Electronics in Agriculture, 76, 131e147. Bitog, J. P., Lee, I. B., Shin, M. H., Hong, S. W., Hwang, H. S., Seo, I. H., et al. (2009). Numerical simulation of an array of fences in Saemangeum reclaimed land. Atmospheric Environment, 43, 4612e4621. Brossard, C., Monnier, J.-C., Barricau, P., Vandernoot, F.-X., Le Sant, Y., Champagnant, F., et al. (2009). Principles and applications of particle image velocimetry. The Onera Journal Aerospace Lab.. Issue 1. Camacho, F. G., Gomez, A. C., Sobczuk, T. M., & Grima, E. M. (2000). Effects of mechanical and hydrodynamic stress in agitated, sparged cultures of Porphyridiumcruentum. Process Biochemistry, 35, 1045e1050. Celik, I. (1993). Numerical uncertainty in fluid flow calculations: needs for future research. ASME Journal of Fluid Engineering, 115, 194e195. Chiu, S.-Y., Tsai, M.-T., Kao, C.-Y., Ong, S.-C., & Lin, C.-S. (2009). The air-lift photobioreactors with flow patterning for highdensity cultures of microalgae and carbon dioxide removal. Engineering in Life Sciences (Vol. 9)(3), 254e260. Choudhury, D. (1993). Introduction to the renormalization group method and turbulence modelling. Technical Memorandum, Fluent Incorporated. Delnoij, E., Kuipers, J. A. M., & van Swaaij, W. M. (1999). A threedimensional CFD model for gaseliquid bubble columns. Chemical Engineering Science, 54, 2217e2226. Fan, L., Zhang, Y., Cheng, L., Zhang, L., Tang, D., & Chen, H. (2007). Optimization of carbon dioxide fixation by Chlorella vulgaris cultivated in membrane-photobioreactor. Chemical Engineering Technology, 30, 1094e1099. Fluent manual. (2006). New Hampshire, USA: Fluent Co. Hutmacher, D. W., & Singh, H. (2008). Computational fluid dynamics for improved bioreactor design and 3D culture. Trends Biotechnol, 26, 166e172. Illman, A. M., Scragg, A. H., & Shales, S. W. (2000). Increase in Chlorella strains calorific values when grown in low nitrogen medium. Enzyme and Microbial Technology, 27, 631e635. Janseen, M., Slenders, P. M., Tramper, J., Mur, L. R., & Wijffels, R. H. (2001). Photosynthetic efficiency of Dunaleilla tertiolecta under short light/dark cycles. Enzyme and Microbial Technology, 29, 298e305. Kommareddy, A. R., & Anderson, G. A. (2004). Analysis of current and mixing in a modified bubble column reactor. ASAE paper No. 043071. St. Joseph, Michigan, USA: ASAE.

60


Lee, C. G., & Palsson, B. O. (1994). High density algal photobioreactors using light-emitting diodes. Biotechnology and Bioengineering, 44(10). Lee, I.-B., Bitog, J. P. P., Hong, S.-W., Seo, I.-H., Kwon, K.-S., Bartzanas, T., et al. (2013). The past, present and future of CFD for agro-environmental applications. Computers and Electronics in Agriculture, 93, 168e183. Lee, K., & Lee, C. G. (2001). Effect of light/dark cycles on wastewaster treatments by microalgae. Biotechnology and Bioprocess Engineering, 6(3), 194e199. Luo, H. P., & Al-Dahhan, M. H. (2008). Local characteristics of hydrodynamics in draft tube airlift bioreactor. Chemical Engineering Science, 63, 3057e3068. Matthijs, H. C. P., Balke, H., Hes van, H. M., Kroon, B. M. A., Mur, L. R., & Binot, R. H. (1996). Application of light-emitting diodes in bioreactors. Flashing light effects and energy economy in algal culture (Chlorella pyrenoidosa). Biotechnology and Bioengineering, 50, 98e107. Medvitz, R. B., Reddy, V., Deutsch, S., Manning, K. B., & Paterson, E. G. (2009). Validation of a CFD methodology for positive displacement LVAD analysis using PIV data. Journal of Biomechanical Engineering, 131(11). http://dx.doi.org/10.1115/ 1.4000116. Meier, S. J., Hatton, T. A., & Wand, D. I. (1999). Cell death from bursting bubbles: role of cell attachment to rising bubbles in sparged reactors. Biotechnology and Bioengineering, 62(4), 468e478. Merchuk, J. C., Gluz, M., & Mukmenev, I. (2000). Comparison of photobioreactors for cultivation of the red Porphyridiumsp. Journal of Chemical Technology, 75, 1119e1126. Michels, M. H., van der Goot, A. J., Norsker, N.-H., & Wijffels, R. H. (2010). Effects of shear stress on the microalgae Chaetoceros muelleri. Bioprocess Biosystems Engineering, 33, 921e927. Miron, A. S., Gomez, A. C., Camacho, F. G., Grima, E. M., & Chisti, Y. (1999). Comparative evaluation of compact photobioreactors for large-scale monoculture of microalgae. Journal of Biotechnology, 70, 249e270. Ogbonna, J. C., Soejima, T., & Tanaka, H. (1998). Development of efficient large scale photobioreactors; a key factor for practical production of biohydrogen. In O. R. Zaborsky (Ed.), Biohydrogen (pp. 329e343). New York & London: Plenum Press. Oran, E. S., & Boris, J. P. (2001). Numerical simulation of reactive flows (2nd ed.) (pp. 1e14). Cambridge University Press. Perner, I., Posten, C., & Broneske, J. (2003). CFD optimization of a plate photobioreactor used for cultivation of microalgae. Engineering Life Sciences, 3, 7. Perez, J. A. S., Porcel, E. M. R., Lopez, J. L. C., Sevilla, J. M. F., & Chisti, Y. (2006). Shear rate in stirred tank and bubble column bioreactors. Chemical Engineering Journal, 124, 1e5. Pfleger, D., Gomes, S., Gilbert, N., & Wagner, H. G. (1999). Hydrodynamic simulations of laboratory bubble columns fundamental studies of the EulerianeEulerian modelling approach. Chemical Engineering Science, 54, 5091e5099. Raffel, M., Willert, C., & Kompenhans, J. (1998). Particle image velocimetry e A practical guide. Berlin Heidelberg: SpringerVerlag. Ramkumar, K., Mandalam, R. K., & Palsson, B. O. (1998). Elemental balancing of biomass and medium composition enhances growth capacity in high density Chlorella vulgaris cultures. Biotechnology and Bioengineering, 59(5). Rampure, M. R., Kulkarni, A. A., & Ranade, V. V. (2007). Hydrodynamics of bubble column rectors at high gas velocity: experiments and computational fluid dynamics (CFD) simulations. Industrial and Engineering Chemistry Research, 46, 8431e8447. Roache, P. J., Ghia, K. N., & White, F. M. (1986). Editorial policy statement on the control of numerical accuracy. ASME Journal of Fluid Engineering, 108, 2.

Roghair, I., Annaland, M. S., & Kuipers, H. Drag force on bubbles in bubble swarms. Seventh International Conference on CFD in the Minerals and Process Industries, CSIRO, Melbourne, Australia, December 9e11, 2009. Rubio, F. C., Miron, S. A., Garcia, M. C., Camacho, G. F., Grima, M. E., & Chisti, Y. (2004). Mixing in bubble columns: a new approach for characterizing. Chemical Engineering Science, 59, 4369e4376. Sanyal, J., Vasquez, S., Roy, S., & Dudukovic, M. P. (1999). Numerical simulation of gas-liquid dynamics in cylindrical bubble column reactors. Chemical Engineering Science, 54, 5071e5083. Sato, T., Yamada, D., & Hirabayashi, S. (2010). Development of virtual photobioreactor for microalgae culture considering turbulent flow and flashing light effect. Energy Conversion and Management, 51, 1196e1201. Seo, I. H. (2013). Development and application of microalgal photobioreactor performance model using CFD (Ph.D. thesis). South Korea: Seoul National University. Seo, I. H., Lee, I. B., Hwang, H. S., Hong, S. W., Bitog, J. P., Kwon, K. S., et al. (2012). Numerical investigation of a bubblecolumn photo-bioreactor design for microalgae cultivation. Biosystems Engineering, 113, 229e241. Sheng, J., Meng, H., & Fox, R. O. (1998). Validating of CFD simulations of a stirred tank using particle image velocimetry data. The Canadian Journal of Chemical Engineering, 76, 611e625. Shigeya, W., Hiroyuki, K., Zhong, L., Taro, I., & Shunji, E. (2005). CFD code validation via particle image velocimetry (PIV). JAXA Special Publication No. 04-012 (pp. 178e183). Shimizu, K., Takada, S., Minekawa, K., & Kawase, Y. (2000). Phenomenological model for bubble column reactors: prediction of gas hold-ups and volumetric mass transfer coefficients. Chemical Engineering Journal, 78, 21e28. Simonnet, M., Gentric, C., Olmos, E., & Midoux, N. (2008). CFD simulation of the flow field in a bubble column reactor: importance of the drag force formulation to describe regime transitions. Chemical Engineering and Processing, 47, 1726e1737. Spolaore, P., Joannis-Cassam, C., Duran, E., & Isambert, A. (2006). Commercial applications of microalgae. Journal of Bioscience and Bioengineering, 101(2), 87e96. Stanislas, M., Okamoto, K., & Kahler, C. J. Particle image velocimetry: recent improvements. Proceedings of the EUROPIV2 workshop, Zaragoza, Spain, March 31eApril 1, 2003. Suh, I. S., & Lee, C. G. (2003). Photobioreactor engineering: design and performance. Biotechnology and Bioprocess Engineering, 8, 313e321. Terry, K. L. (1986). Photosynthesis in modulated light: quantitative dependence of photosynthetic enhancement on flashing rate. Biotechnology and Bioengineering, 28, 988e995. Tramper, J., William, J. B., Joustra, D., & Vlak, J. M. (1986). Shear sensitivity of insect cells in suspension. Enzyme and Microbial Technology, 8, 33e36. Trujillo, F. J., Safinski, T., & Adesina, A. A. (2007). CFD analysis of the radiation distribution in a new immobilized catalyst bubble column externally illuminated photoreactor. Journal of Solar Energy Engineering, 129(1), 27e35. http://dx.doi.org/ 101115/1.2391013. Westerweel, J. (1997). Fundamentals of digital particle image velocimetry. Measuring Science and Technology, 8, 1379e1392. Wu, X., & Merchuk, J. C. (2002). Simulation of algal growth in a bench-scale column reactor. Biotechnology and Bioengineering, 80(2), 156e168. Yamashita, F., & Suzuki, T. (2007). Simulation and measurement of a gas holdup in bubble columns. Proceedings of European Congress of Chemical Engineers (ECCE), Copenhagen. September 16e20, 2007. Yu, G., Li, Y., Shen, G., Wang, W., Lin, C., Wu, H., et al. (2009). A novel method using CFD to optimize the inner structure


parameters of flat photobioreactors. Journal of Applied Phycology, 21, 719e727. Zhang, D., Deen, N. G., & Kuipers, J. A. M. (2009). Euler-Euler modeling of flow, mass transfer, and chemical reaction in a bubble column. Industrial Engineering and Chemistry Research, 48(1), 47e57. http://dx.doi.org/10.102l/ie800233y.

61

Zhong, C., & Yuan, Y. J. (2009). Responses of Taxus cuspidata to hydrodynamics in bubble column bioreactors with different sparging nozzle sizes. Biochemical Engineering Journal, 45, 100e106. Zuzuki, T., Matsuo, T., Ohtaguchi, K., & Koide, K. (1995). Gassparged bioreactors for CO2 fixation by Dunaliella tertiolecta. Journal of Chemical Technology and Biotechnology, 62, 351e358.

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