Microscale and Macroscale Modeling of Microalgae Cultivation in Photobioreactor: A Review and Perspective

Chapter 1

Microscale and Macroscale Modeling of Microalgae Cultivation in Photobioreactor: A Review and Perspective Choon Gek Khoo1, Man Kee Lam2,3 and Keat Teong Lee1 1

School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Malaysia, 2Chemical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia, 3Centre for Biofuel and Biochemical Research (CBBR), Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

1.1 INTRODUCTION In recent decades, microalgae biomass has emerged as a new and sustainable energy source for third-generation biofuels production. The potential biofuels that can be derived from microalgae biomass are biodiesel, bioethanol, biohydrogen, and biomethane [1
4]. Microalgae have a simple cellular structure that allows them to grow rapidly (100 times faster than terrestrial plants) and are able to double their biomass in less than 1 day under favorable cultivation conditions. Some microalgae species are able to accumulate a significant amount of lipids within their cells, such as Botryococcus braunii (lipid content of 25%
75%), Chlorella sp. (28%
32%), Scenedemus sp. (20%
21%), and Nannochloropsis sp. (31%
68%), in which the lipid can be further converted to biodiesel [2,3]. From an environmental perspective, the cultivation of microalgae coupled with CO2 fixation and biotreatment of wastewater has evolved as a clean and green energy producer [5,6].

1.2 PHOTOBIOREACTOR SYSTEM A photobioreactor (PBR) is a closed system that is usually used for microalgae cultivation. It is designed based on several basic features, such as illumination surface area, liquid circulation, and gas exchange to supply CO2 to the system and to degas O2 [7]. A PBR that has high degree of cultivation Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts. DOI: https://doi.org/10.1016/B978-0-12-817937-6.00001-1 © 2019 Elsevier Inc. All rights reserved.

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process control is more suitable and reliable for large-scale microalgae cultivation [3]. In addition, it is also easier to optimize biomass productivity based on the biological and physiological characteristics of the cultivated microalgae species [7]. The general conceptual design of a PBR is that it is a solar receptor that is usually made of glass or plastic, coupled with a gas exchange vessel where CO2 and nutrients are added to the cultivation system. Excess O2 is then removed through the end of the PBR tubing. A pump or airlift system is usually used to circulate the microalgae culture in the PBR to prevent microalgae from settling and to improve the CO2
liquid mass transfer [8]. This photoautotrophic cultivation system utilizes protons (e.g., sunlight) as the main driven energy source, in which these protons are emitted through the transparent PBR walls before reaching to the cultivated microalgae cells instead of impinging directly on the culture’s surface [9].

1.3 MATHEMATICAL MODELS Mathematical models are one of the important interpretations of experimental results that provide a greater insight toward the studied biological system [10]. Throughout the decades, there has been a great deal of importance placed on predicting the productivity of the microalgae biomass and the transport phenomena in PBR through mathematic modeling [11]. The mathematical models can be conceptually categorized into macroscale and microscale, which described the operational performance in PBRs and the growth of microalgae cells, respectively [12
14]. Fig. 1.1 illustrates the phenomena

FIGURE 1.1 Phenomena occurring during microalgae cultivation within photobioreactors: (A) macroscale transport phenomena and (B) microscale kinetic growth for microalgae cells.

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occurring at the macroscale and microscale of microalgae growth in PBR. Generally, the developed mathematical modeling follows the sequences from microscale of microalgae growth to the macroscale of transport phenomena in PBR. The integration between micro- and macroscale modeling are theoretically driven, which is applicable to the scale-up the microalgae cultivation process. However, the complexity of macroscale modeling with respect to the simple expressions of microscale modeling is not widely reported.

1.4 MACROSCALE MODELING There are two types of PBR that have been widely used for microalgae cultivation, namely column and flat plate types, each with pros and cons in operation (Table 1.1) [4,8,15]. The aeration strategies in the PBR are different, in which the air can either be circulated around or blown forcefully. By categorizing the PBR according to bioprocess operation rather than

TABLE 1.1 The Advantages and Drawbacks of Different Types of Photobioreactors for Microalgae Cultivation PBR Design

Advantages

Column

G

G G G G G G G G G G

Flat plate

G G G G

G G

Drawbacks

Radial movement of fluid for improved light
dark cycle with less photoinhibition and photooxidation Greater gas hold up High mass transfer rate Lower energy consumption Easy to sterilize Readily tempered Good for algae immobilization Suitable for outdoor cultures Large illuminated surface area Good biomass productivities Cheap

G

Suitable for outdoor cultures Large illuminated surface area Good for algae immobilization Better biomass productivities than bubble columns Ease of maintenance Readily tempered

G

G G G G G

G G

G

G G

G

G

Low surface/volume Expensive Smaller illuminated surface area Required sophisticated materials Shear stress to algal cultures Large number of units required for commercial plants due to a fixed diameter to height ratio Fouling Some degree of wall growth, dissolved O2, and CO2 along the tubes pH gradients Difficulty in culturing temperature controlled Some degree of wall growth Required support materials for scale-up Hydrodynamic stress to algal strains Higher power supply cost

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by physical design features would result in a more accurate mathematical model. Perfect mixing is usually assumed when mathematical modeling an aerated PBR, to simplify the models. In macroscale modeling, the PBR characteristics are defined in terms of volumetric mass transfer coefficient (kLa), fluid hydrodynamics, mixing time determination, superficial gas velocity, specific power input, and fluid velocity [16
18]. These parameters are often used to scale-up the bioprocess in a PBR. The advancement of bioprocessing established a series of engineering characterizations for a single-use system in a PBR, which are (1) parametric, (2) experimental, and (3) computer-based numeric analysis [19]. Application of each method will further enhance the understanding of the PBR system’s performance and process optimization. Table 1.2 summarizes the general parametrical methods used for the engineering characterization of a PBR.

TABLE 1.2 General Parametrical Methods Used for Engineering Characterization of an Aerated Photobioreactor Parametrical

Mathematical Models

Nomenclature

Reference

Flow regime

Re 5 ρL Nda2 =ηL Ne 5 P =ρL N 3 da5

Re: Reynold number ρL: Fluid density (kg m23) da: Agitator diameter (m) ηL: Fluid viscosity (kg m21 s21) Ne: Newton number P: Power input

[23]

Fluid velocity

umax 5 uTip 5 πNda

umax: Maximal fluid velocity (m s21) utip: Tip speed (m s21)

[16,19,23]

Superficial gas velocity

UG 5

UG: Superficial gas velocity (m s21) VG: Flow rate of gas (m3 s21) A: Area (m2)

[19]

Power consumption

P =V 5 ððM 2 Md Þ2πN Þ=V

V: Working volume (m3)

[17,19]

VG A

(Continued )

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TABLE 1.2 (Continued) Parametrical Mixing time distribution

Volumetric mass transfer coefficient

Mathematical Models



H ðt Þ 5 Cτ;0 2 C ðtÞ = Cτ;0 2 Cτ;ω

kL a 5 C1 P =V


C2

ðVG ÞC3

Nomenclature

Reference

H: Homogeneity Cτ,0: Tracer concentration at the start of experiment (kg m23) Cτ,ω: Tracer concentration at the end of experiment (kg m23) C(t): Tracer concentration at time t (kg m23)

[24,25]

kLa: Volumetric mass transfer coefficient (s21) C: Concentration (kg m23)

[21]

1.4.1 Volumetric Mass Transfer Coefficient CO2 is supplied to photosynthetic microalgae cells to grow; however, it is usually regarded as a rate-limiting factor as the cultivation scale increases [20]. The CO2 transfer rate is expressed as the overall volumetric mass transfer coefficient (kLa) [21]. According to Kadic and Heindel [22], there are a few methods to measure the kLa, which are (1) static gassing out, (2) sulfite methods, and (3) dynamic gassing out. The static gassing out is measured based on the dissolved oxygen (DO) during microalgae cultivation, where N2 gas is first introduced to the PBR system to create an inert environment before sparging with compressed air or CO2-enriched air. On the other hand, the sulfite method is measured based on chemical reaction of sulfite (SO22 3 ) to sulfate (SO22 ) in the presence of DO and is catalyzed by copper, ferric, 3 cobalt, or manganese ions. As for the dynamic gassing out method, the DO is measured during microalgae cell growth.

1.4.2 Fluid Hydrodynamics The fluid hydrodynamics in a PBR can be described though flow regimes and can be examined experimentally. The flow regimes included laminar,

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transitional, and turbulent flow. The dimensionless Reynold’s number (Re) estimation is directly correlated to the physical properties (e.g., density and viscosity) of the fluid’s flow [23].

1.4.3 Mixing Time Determination The operation of a PBR greatly depends on the microalgae cells’ productivity throughout the cultivation period. Mixing time can be determined by the acid tracer method that combines both acid injection and pH measurement over time [24,25].

1.4.4 Superficial Gas Velocity The theoretical superficial gas velocity (UG) is important in predicting the liquid dispersal of an aerated process. In the typical bioprocess operation of a PBR, the diameter of the gas bubbles is influenced by UG in the turbulent flow regime [19].

1.4.5 Specific Power Input The specific power input plays a vital role in mechanically driving a scaleup operation of a typical PBR. The power input can be correlated by temperature and a numerical dimensionless number, which enables the prediction of the Newton number (Ne) [19]. In addition, the specific power input can be correlated with the superficial gas velocity in a pneumatically mixing PBR [17].

1.4.6 Fluid Velocity Fluid velocity is generally dependent on the type and design of the PBR. The maximal fluid velocity (umax) is generated by the aeration system and is influenced by the diameter of the agitator or sparger and the microalgae cultivation vessel [16,19,23].

1.5 MICROSCALE MODELING The following sections discuss the parameters required for microalgae cell growth, which can be employed for kinetic growth modeling. Generally, the growth models can be categorized into primary and secondary models. The primary models describe the growth with the least parameters, which only provide a rough estimation of microalgae productivity. As for the secondary models, a few features are taken into consideration to enhance the accuracy of the predicted microalgae productivity, such as temperature, light, photosynthesis rate, pH, and nutrients concentration [26,27]. Besides, sensitivity of model input with microalgae productivity rate could be used to further strengthen the understanding of microalgae growth in a PBR.

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FIGURE 1.2 The empirical kinetic profiles for primary models. In applying nonlinear regression to experimental data, zero time will typically be taken as the end of the lag-phase.

1.5.1 Basic Kinetic Equations: Primary Models The main objective of the primary models is to express the microalgae cells growth with the fewest parameters at their respective growth phases [28]. There are three basic primary kinetics models: (1) Malthusian model, (2) logistic model, and (3) Gompertz models, as shown in Fig. 1.2. Among the three basic kinetic models, the logistic model is the most widely used in expressing microbial growth kinetics. This is due to the simplicity of the mathematic models, which described the entire growth curve from lag-phase until the latter stages of the cell’s death [29]. The detailed description of each basic kinetic model is discussed in the following sections.

1.5.1.1 Malthusian Model The Malthusian model is often referred to as the exponential law [30], in which the concentration of microalgae cells is expressed as exponentially increasing with time. This phenomenon indicates the potential ability of microalgae cells to boost its productivity. A direct proportional relationship between the microalgae cells concentration with biomass accumulation can be expressed by the first-order rate equation as shown in the following equation: rX 5 μX

ð1:1Þ

where rX represents the cell growth rate (kg cell m23 h21), X is the cell concentration (kg cell m23), and μ represents the kinetic growth constant,

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namely the specific growth rate (1/h). On the other hand, the cell growth over a discrete period of time (t) for a batch system can be expressed in the following equation: dX 5 μUXðtÞ dt

ð1:2Þ

This equation can be further differentiated and expressed in the following equation: XðtÞ 5 X0 UexpðμtÞ

ð1:3Þ

where X0 represents the initial cell concentration at the starting cultivation time, t 5 0 (as in Fig. 1.2). However, the Malthusian model has violated the first kinetic principle [31], where cell growth cannot achieve its saturation point as time is extended.

1.5.1.2 Logistic Model The logistic model is expressed in the following equation:   dX X 5 μX 1 2 dt K

ð1:4Þ

where K (kg cell m23) represents the maximum cell concentration that can be supported by the cultivation environment. The main drawback of logistic model is that it cannot be validated if the microorganism does not grow continuously until it reaches the death phase. The integration of the logistic model can be further expressed as follows: X ðtÞ 5

K 1 1 CKexpð 2μtÞ

ð1:5Þ

The logistic trend is illustrated in Fig. 1.2, where C 5 1/X0 2 1/K is determined by the initial condition X0.

1.5.1.3 Gompertz Model The Gompertz model is usually expressed as a sigmoid function, which is similar to the logistic curve. The Gompertz model exhibits a slower growth at the initial cultivation stage and at the end of the cells’ growth (Fig. 1.2). The Gompertz model is expressed in the following equation: X ðtÞ 5 aexp½ 2 bexpð 2ctÞ

ð1:6Þ

where a is the upper asymptotic value, b is the x displacement, and c is the growth rate, while the initial cell concentration at t 5 0 is X0 5 a exp(2b).

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1.5.2 Dynamics Kinetic Equations: Secondary Models The secondary models described the microalgae cells’ growth dynamics as a function of environmental conditions. It is usually modified based on the Monod and Droop models [32]. The Monod model [33] is a general kinetic model that describes the correlation of microalgae cell growth under a limited nutrient concentration environment as expressed in the following equation: μ 5 μmax

S ðKs 1 SÞ

ð1:7Þ

where μmax represents the maximum specific cells’ growth rate, S represents the concentration of nutrients in the cultivation medium, and KS represents the half-saturation constant (the nutrient concentration at which the specific growth rate is half of the maximum). The Monod model is preferred due to the ease of measuring the external substrate concentration. Nonetheless, the Monod model has low accuracy in predicting the microalgae cells’ growth rate due to the high uptake of nutrients by the microalgae [34]. Besides, Richmond [35] also indicated that the growth rate of microalgae is more dependent on internal cellular concentrations rather than the external quantities. In contrary, the Droop model [36] relates the microalgae cells’ growth rate with internal nutrients concentrations as shown in the following equation:   Kq μ 5 μmax 1 2 ð1:8Þ q where Kq represents the limiting cell quota for the limiting nutrient while q represents the cell quota for the limiting substrate. Factors that affect microalgae growth rates include the availability of carbon sources (measured by inorganic carbon concentration), nutrients concentration (nitrogen and phosphorus), light intensity, inhibition, temperature, and combination of multiple factors. Table 1.3 summarizes some of the commonly used microalgae kinetic models derived from the Monod or Droop models, together with models that integrate with multiple factors from Lee et al. [37] and Be´chet et al. [38]. The Monod and Droop models are basically used to express activity of microalgae growth performance in single variant, either by considering the external [39
41] or internal [42] changes. After all, the modification of the Monod models with the integration of multiple factors (preferably with the consideration of light intensity and temperature [43
46]), yield a sophisticated model that is able to attain greater accuracy in the prediction of cells productivity.

TABLE 1.3 Commonly Used Kinetic Models With Correlation Factors of Inorganic Carbon Concentration, Nitrogen Concentration, Phosphorus Concentration, Light Intensity, Inhibition, Temperature, and Combination of Multiple Factors Kinetic Model

Nomenclature

Correlation Factor(s)

Example of Kinetic Experimental Results

Ref.

a.1

μmax: Maximum specific cells growth rate Sc: Carbon concentration KS,c: Half-saturation constant

Inorganic carbon concentration

CO2 source: 13.1 mg L21 TIC Scenedesmus quadricauda pH: 7.10
7.61 T: 27 C μmax: 2.29 day21 KSc: 0.30
0.71 mg L21 Scenedesmus capricornutum pH: 7.05
7.59 T: 27 C μmax: 2.45 day21 KSc: 0.40
1.20 mg L21

[39]

a.2

μmax: Maximum specific cells growth rate SN: Nitrogen concentration KS,N: Half-saturation constant

Nitrogen concentration

N source: 41.8
92.8 mg L21 NH4-N Chlorella vulgaris pH: 7 T: 20 C k 5 μmax/YN: 1.5 mg mg21 day21 KSN: 31.5 mg L21

[40]

(a) Monod Model—External Factor μ 5 μmax Sc = KSc 1 Sc





μ 5 μmax SN = KSN 1 SN





(Continued )

TABLE 1.3 (Continued) Kinetic Model μ 5 μmax SP = KSP 1 SP

μ 5 μmax I= KS;I 1 I









Nomenclature

Correlation Factor(s)

Example of Kinetic Experimental Results

Ref.

a.3

μmax: Maximum specific cells growth rate SP: Phosphorus concentration KS,P: Half-saturation constant

Phosphorus concentration

P source: 7.7 mg L21 PO4-P C. vulgaris pH: 7 T: 20 C k 5 μmax/YN: 0.5 mg mg21 day21 KSN: 10.5 mg L21

[41]

a.4

I: Light intensity KS,I: Saturation light intensity

Light intensity limitation

I source: 0
71.8 mW m22 C. vulgaris pH: 6.8 T: 22 C Air and CO2 flow: 200 mL min21 μmax: 0.040 h21 KI: 2.8 mW L21

[42]

b.1

μmax : Maximum specific cells growth rate Qmin: Limiting cell quota for the limiting nutrient

Phosphorus concentration

P source: 0.352
324 3 10215 mol cell21 Scenedesmus pH: 7.2 T: 12 C 0 μmax : 0.755 day21 Qmin: 5.16 fmol cell21 Chlorella pH: 7.2 T: 12 C 0 μmax : 0.842 day21 Qmin: 0.352 fmol cell21

[42]

(b) Droop Model—Internal Factor 0

μ 5 μmax 1 2 Qmin =Q





0

(Continued )

TABLE 1.3 (Continued) Kinetic Model

Nomenclature

Correlation Factor(s)

Example of Kinetic Experimental Results

Ref.

(c) Models With Consideration of Multiple Factors μ5K

00




ðε∙al ∙X∙I0 Þ=ðX∙V Þ 2 Im ð1 2 VF Þ

  μ 5 μmax ðI Þ= I 1

μmax
α

  I Iopt

21

2 

00

c.1

K : A proportionality constant (kg mol21) ε: A constant al: Effective light absorption surface area of each cell (m2) X: Cell concentration (kg m23) V: Liquid volume in the reactor (m3) I0: Incident light intensity (mol m22 day21) Im: Maintenance rate (mol kg21 day21) VF: Illuminated volume fraction of the reactor

Light-limitation associated with light attenuation by cells

Chlorella pyrenoidosa Aeration rate: 0.6 vvm K: 0.8 kg mol21 X: 0.01905 kg m23 V: 0.00075 m3 Imax: 0.13 mol kg21 day21

[43]

c.2

α: Initial slope of light response curve Iopt: Irradiance for which growth is maximal with respect to light μmax: Maximum growth rate for optimal irradiance

Light-limitation and photoinhibition

C. pyrenoidosa (wild type) Topt: 38.7 C α: 0.05 Iopt: 275 µE m22 s21 μmax: 2.00 day21

[44]

(Continued )

TABLE 1.3 (Continued) Kinetic Model  
S I=ðIS 1 I Þ μ 5 μm ðS Þ=S 1 KS 1 KI



1 2 Cx =Cxm 1 2 Cp =Cpm

   2  C1 μ 5 μmax C1 = Kn 1 C1 1 Ki  m
 En = Kem 1 Enm IðT Þ where IðT Þ 5 1:066ðT 220Þ

TIC, total inorganic carbon.

Nomenclature

Correlation Factor(s)

Example of Kinetic Experimental Results

Ref.

c.3

Cpm: Maximum product concentration (mg L21) Cp: Product concentration (mg L21) Cxm: Achievable maximum cell concentration (g L21) Cx: Cell concentration (g L21)

Acetate concentration, microalgae cell concentration, and light intensity

Haematococcus pluvialis μmax: 0.5258 day21 Cxm: 2.92 g L21 Cpro,m: 55.6 mg L21 KS,OC: 0.0211 g L21 Ki,OC: 56.6813 g L21 KI: 53.26 µmol m22 s21

[45]

c.4

C1: Concentration of bicarbonate in culture (mol L21) Kn: Half-saturation constant of carbon (mol CO2 L21) Ki: Inhibition constant of carbon (mol CO2 L21) En: Average radiant energy within bulk culture medium (µE m22 s21) Ke: Half-saturation constant of light (µE m22 s21) I(T): Function to include temperature effects T 5 temperature ( C)

Inorganic carbon concentration, light intensity, and temperature

Scenedesmus sp. μmax: 1.4 day21 Ke: 131 µE m22 s21 Kn: 9.818e 2 4 mol L21 kd: 0.165 day21 Kc: 0.16 cm2 mg21 Chl a fi: 0.016 Kw: 0.001 cm21 Kni: 1 mol L21 m: 2.27

[46]

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1.6 CHALLENGES IN SCALE-UP MICROALGAE CULTIVATION Currently, the production of microalgae biomass is identified as a green and sustainable feedstock for biofuel production [2,47,48]. There are four key steps in designing and optimizing the microalgae cultivation process on an industrial scale which are (1) strain identification, (2) strain modification, (3) process design and optimization, and (4) process scale-up and validation. However, there is still a lack of evidence to support the industrial cultivation of microalgae biomass due to the challenges of economic feasibility and technology maturity [48,49]. There are a number of challenges in large-scale microalgae cultivation, which include the selection of microalgae strains, cultivation methods, nutrients, and carbon sources for microalgae growth [50]. In addition, an ideal microalgae scale-up cultivation process should be able to meet a positive energy balance, especially when the biomass is further processed to biofuels. Therefore the cultivation systems should be designed to promote the high photosynthetic activities of microalgae [1,2]. Besides, transport processes such as the loses of CO2 due to diffusion and high evaporation rates that cause high salinity values should be taken into consideration during the scale-up study [4,51].

1.7 POTENTIAL INDUSTRY APPLICATION The current trends in modern biotechnology development intend to extend the knowledge of life science to a scalable industrial production for commercialization purposes. A typical bioprocess system description can be generated through the adaption of biological, chemical, or physical analytical methods and are then recorded and interpreted for process control. Additionally, bioprocess optimization and intensification involves sophisticated data analysis, which requires data pooled from variety sources. Therefore more information on measuring, monitoring, and modeling are required to understand the actual performance in a biosystem and to further enhance production sustainability [52]. Among all these, the mathematical modeling plays a vital role in designing and optimizing the intensified bioprocesses. Quinn et al. [53] reported that the productivity of microalgae could not be directly extrapolated from laboratory-scaled to industrial production. The application of the constructed mathematical models should be validated with the respective industrial facilities under regional environmental scenarios in order to minimize the inaccuracy due to locational factors. Also, the biological
physical interaction of the microalgae with the cultivation system plays an essential role in microalgae productivity performance. For instance, the influence of hydrodynamics and mass transfer plays a critical role in determining microalgae productivity at pilot-scaled cultivation [54]. In addition,

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the use of dynamic modeling in growth kinetics enables a more precise of microalgae productivity [55
57], which includes consideration for both biotics and abiotic factors. The advancements of microalgae growth models that integrate the variability of biological function with respect to their seasonal and geographical influences are expected to make a significant scientific breakthrough at commercial scale. A study carried by Khoo et al. [54] showed the potential of model integration yielding insights into the interaction of macro- and microscale phenomena in a PBR during microalgae cultivation.

1.8 CONCLUSION By considering macro- and microscale factors when constructing a mathematical model can improve the understanding of complex microalgae growth in a PBR. Besides, mathematical descriptions on reaction-diffusion provide a clear insight on the scale-up process. Most of the available mathematical models predict the dynamic state of microalgae growth and their transport phenomenon separately by using numerical methods with certain modification. However, more theoretical frameworks on the integration of both macro- and microscale modeling associated with experimental validation are required to enhance the performance of PBRs at commercial scale and to improve the economic feasibility of microalgae biofuel production.

1.9 FUTURE OUTLOOK The main challenge of mathematic modeling in a biological process is dealing with the complicated integration of cell’s activity within the hierarchy of numerous biological levels, which include molecular, cellular, and multicellular sources [58]. In order to have a better understanding on biological process development, future mathematical models should consider the following: 1. A more realistic prediction for a microalgae cells’ kinetics study (growth and death kinetic) and transport phenomena (mass and heat transfer) at macro- and microscale operation. 2. Detailed evaluation on the energy, water, and O2 balances are required for PBR control schemes design, and to maintain the PBR temperature and water content at the optimum value for high microalgae biomass productivity. 3. Improvement in computing power for routine control of the optimized bioprocess in the PBR.

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ACKNOWLEDGMENT The authors would like to acknowledge the financial support received from the Universiti Sains Malaysia, through Short Term Grant (304/PJKIMIA/6315016) and the Ministry of Higher Education (MOHE) Malaysia, through Fundamental Research Grant Scheme Malaysia’s Rising Star Awards 2016 (FRGS MRSA 2016) (203/PJKIMIA/6071362) and Fundamental Research Grant Scheme (FRGS) with cost center 0153AB-L25. C.G. Khoo wishes to acknowledge the financial support from the MOHE Malaysia through Ph.D. scholarship scheme (MyBrain15-MyPhD) and M.K. Lam wishes to acknowledge the technical support from Centre for Biofuel and Biochemical Research (CBBR) of Universiti Teknologi PETRONAS.

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