**Published at : ** 21 Jul 2020

**Volume :** IJtech
Vol 11, No 3 (2020)

**DOI :** https://doi.org/10.14716/ijtech.v11i3.3983

Shihab, M.A., Dhahir, M.A., Mohammed, H.K., 2020. Kinetic Study of Air Bubbles-Cetyltrimethylammonium Bromide (CTAB) Surfactant for Recovering Microalgae Biomass in a Foam Flotation Column.

205

Muayad A. Shihab | Petroleum and Gas Refinery Engineering, College of Petroleum Process Engineering, Tikrit University, Slah Al-deen, 34001, Iraq |

Mohammed A. Dhahir | Petroleum and Gas Refinery Engineering, College of Petroleum Process Engineering, Tikrit University, Slah Al-deen, 34001, Iraq |

Hamad K. Mohammed | Petroleum and Gas Refinery Engineering, College of Petroleum Process Engineering, Tikrit University, Slah Al-deen, 34001, Iraq |

Abstract

Owing
to their efficient photosynthesis, microalgae tend to possess superior growth
rates and high lipid production, hence their significance to the biofuel
sector. The bulk harvesting of microalgae from cultures is a substantial stage
in advancing the production of biomass-based fuels. However, a reliable and
cost-effective harvesting technology is not yet available. Foam flotation,
which is a subcategory of the adsorptive bubble separation process, shows
considerable promise for the harvesting and enrichment of microalgae biomass.
The available literature indicates that virtually no data has been reported on
the flotation kinetics of microalgae. Therefore, to
better describe the recovery of microalgae by the flotation process, this work studied the flotation kinetics of the freshwater
microalgae *Chlorella vulgaris*. The recovery of microalgae cells in a
batch foam flotation column over time at different operating conditions was
fitted to nine flotation kinetic models, including first, fractional, and
second order kinetic models; a first order kinetic model with rectangular,
exponential, gamma, and sinusoidal distributions of floatabilities; a second
order kinetic model with rectangular distribution of floatabilities; a fully
mixed reactor; and modified Kelsall flotation kinetic models. Evaluation of the
kinetic models showed that the discrete rate constant model (i.e. modified
Kelsall kinetic model) fitted the experimental data best. The modified Kelsall
model shows the highest values of adjusted R^{2} (>0.995) and the
lowest values of mean squared error (<2.63). Apart from the modified Kelsall
model, which has discrete rate constants, no single kinetic model, with or
without a continuous distribution, was sufficient to represent the flotation
data, and the optimal model may vary under different conditions. More work is
recommended using different freshwater and marine microalgae species.

Biofuel; Chlorella vulgaris; Foam flotation; Kinetic rate constant; Ultimate recovery

Introduction

Due to concerns
about the sustainability of fossil fuel, environmental pollution, and global
climate change, enormous attention has been given over the last two decades to
renewable energy resources, like microalgae. The growing interest in microalgae
is due to their superior growth rates, high lipid production, and ability to
be cultivated everywhere (Cercado
et al., 2018; Sukarni et al.,
2019; Rizaldi et al., 2019). The
bulk harvesting of microalgae from cultures is a substantial stage in advancing
the production of biomass-based fuels, but a reliable and cost-effective
harvesting technology is not yet available. Foam flotation, which is a subcategory of the adsorptive bubble separation process, shows notable promise for the
harvesting and enrichment of microalgae biomass. During the foam flotation process, a foaming
agent is added to generate the foam (i.e. stabilize it) and to

enhance the low
hydrophobicity of microalgae cells, as observed by Alkarawi and colleagues in
2018. Bubbles are generated, with different size distributions based on the
method used, which attach to the microalgae cells due to differences in the
physicochemical properties of the interfaces and cause them to travel up to the
surface, where they are recovered in the foamate stream (Alkarawi et al., 2018).

However,
the presence of interactions between solid, liquid, and gas phases, in addition
to chemicals (surfactants), in foam flotation makes it a very complex process;
hence, it is difficult to develop mathematical models for the foam flotation
process, unlike for other separation processes, such as distillation (Stevenson and Li, 2014). The availability of a
mathematical model for the flotation process is essential for its evaluation,
optimization, and automation (Bu et al., 2016b). Consequently, kinetic, probabilistic, and empirical models have been
developed to better describe the process. Of these, kinetic models are more popular because they are simple and can
reasonably imitate the batch flotation process (Alvarez-Silva
et al., 2016).

In general, the flotation kinetics of particles has been developed based on homogenous reaction kinetics, since the collision of chemical molecules in reactions is analogous to the collision of air bubbles with particles in the flotation process. Consequently, many studies have started by employing various reaction kinetic models to better characterize the flotation process. The generalized form of the flotation rate equation is given below (Miettinen et al., 2010; Bu et al., 2016a; Bu et al., 2016b):

where *k *(min^{-1}) is the flotation rate
constant, *t *(min) is the flotation time, and

The recovery of particles in the top product
(foamate) (*R*

where *C _{i}*

When Equations 2 and 3 are substituted into Equation 1, the flotation rate equation based on particle recovery can be obtained, as set out in Equation 4:

In this work, the flotation kinetics of the microalgae *Chlorella vulgaris* were studied for the
first time by performing flotation rate tests to better describe the
recovery of microalgae by the flotation process. The
recovery of microalgae cells in a batch foam flotation column over time at
different operating conditions was fitted to nine flotation kinetic models. The
flotation rate tests were carried out at different air flow rates and
surfactant concentrations. Cetyltrimethylammonium
bromide (CTAB) was used in this work because a previous work demonstrated that
it produced the best enhancement of the hydrophobicity of microalgae cells and
recovery efficiency (Alkarawi et al., 2018).
Other studies have also demonstrated the unique characteristics of CTAB in the
removal of microalgae (Laamanen et al., 2016)
and the preservation of nanofluid stability during agglomeration and
precipitation (Kusrini et al., 2019).

Conclusion

Foam flotation has been shown to be an attractive technique for
recovering and concentrating algal biomass from a culture medium. Nevertheless,
the development of mathematical models for the foam flotation process is
complex, due to the interactions between gas, liquid, and solids phases in the
process. Different kinetic models were therefore tested herein to better
understand the flotation process of the microalgae cells. Nine flotation
kinetic models, including single and distributed rate constants as well as a
discrete rate constant, were considered because of their trade-offs between
accuracy and simplicity. These kinetic equations were used to model the kinetic
data of microalgae (*Chlorella vulgaris*) in the presence of a CTAB
surfactant. The regression parameters of the proposed kinetic models were
compared to determine the one that fit best.

The
four-parameter modified Kelsall kinetic model showed most agreement with the
experimental data for the recovery of the microalgae. The maximum recovery
predicted by the fitted Kelsall model was 100% at all CTAB concentrations, but
only at the higher air flow rate (2 L min^{-1}), indicating that the
recovery of microalgae cells is favored at high air flow rates. The flotation
of the fast-floating particles was faster than that of slow-floating particles.
The air flow rate had larger effect than the slight increase in the surfactant
concentration on the flotation of slow-floating particles. These outcomes
indicated that the flotation process might require more time or higher air flow
rates to increase the recovery of slow-floating particles due to their low
flotation rate.

Acknowledgement

The
authors would like to express sincere gratitude to the College of Petroleum
Process Engineering, Tikrit University, Iraq, for supporting this work. The
authors would also like to extend thanks to Mr. Ayoob Ibrahim and Mr. Omer
Ibrahim for their help.

Supplementary Material

Filename | Description |
---|---|

R1-CE-3983-20200418113624.docx | --- |

R1-CE-3983-20200418113844.png | --- |

R1-CE-3983-20200418113925.png | --- |

R1-CE-3983-20200418114000.png | --- |

R1-CE-3983-20200418114041.png | --- |

R1-CE-3983-20200418114114.png | --- |

R1-CE-3983-20200418114200.png | --- |

R1-CE-3983-20200418114310.png | --- |

References

Alkarawi, M.A.S., Caldwell, G.S., Lee, J.G.M., 2018. Continuous Harvesting
of Microalgae Biomass using Foam Flotation. *Algal** Research*, Volume
36, pp. 125–138

Alvarez-Silva, M., Vinnett, L., Langlois, R., Waters, K.E., 2016. A
Comparison of the Predictability of Batch Flotation Kinetic Models. *Minerals Engineering*, Volume 99, pp. 142–150

Bu, X., Peng, Y., Ge, L., Ni, C., 2016a. Kinetics of Flotation. Order
of Process, Rate Constant Distribution and Ultimate Recovery. *Physicochemical Problems of Mineral
Processing*, Volume 53(1),
pp. 342–365

Bu, X., Xie, G., Peng, Y., Chen, Y., 2016b. Kinetic Modeling and Optimization
of Flotation Process in a Cyclonic Microbubble Flotation Column using Composite
Central Design Methodology. *International
Journal of Mineral Processing*,
Volume 157, pp. 175–183

Cercado, A.P., Ballesteros, F.C., Capareda, S.C., 2018. Biodiesel from
Three Microalgae Transesterification Processes using Different Homogenous
Catalysts. *International Journal of Technology*, Volume 9(4), pp. 645–651

Chisti, Y., 2013. Constraints to Commercialization of Algal Fuels. *Journal of Biotechnology*, Volume 167(3), pp. 201–214

Corona-Arroyo, M.A., López-Valdivieso, A., Song, S., 2018. Contact Angle
and Vacuum Floatability of Ultrafine Size Particles. *Journal of **Separation Science and Technology*, Volume 53(6), pp. 999–1005

Diao, J., Fuersteenau, D.W., Hanson, J.S., 1992. Kinetics of Coal Flotation.
*In:* SME-AIME Annual Meeting.
Phoenix, Arizona, Volume 92

Dowling, E.C., Klimpel, R.R., Aplan, F.F., 1985. Model Discrimination
in the Flotation of a Porphyry Copper Ore. *Journal of **Minerals and Metallurgical Processing*, Volume 2, pp. 87–101

Guerrero-Pérez, J.S., Barraza-Burgos, J.M., 2017. A New Mathematical
Model for Coal Flotation Kinetics. *DYNA
Journal*, Volume 84, pp. 143–149

Horst, W.R., Morris, T.M., 1956. Can Flotation Rates Be Improved? *Journal
of* *Engineering & Mining, *Volume 157(10), pp. 81–83

Imaizumi, T., Inoue, T., 1963. Kinetic Considerations of Froth
Flotation. *In:* Proceedings of the 6^{th} International Mineral
Processing Congress. Cannes, France

Kusrini, E., Putra, N., Siswahyu, A., Tristatini, D., Prihandini,
W.W., Alhamid, M.I., Yulizar, Y., Usman, A., 2019. Effects of Sequence
Preparation of Titanium Dioxide–water Nanofluid using Cetyltrimethylammonium
Bromide Surfactant and Tio_{2} Nanoparticles for Enhancement of Thermal
Conductivity. *International Journal of Technology*, Volume 10(7), pp.
1453–1464

Laamanen, C.A., Ross, G.M., Scott, J.A., 2016. Flotation Harvesting of
Microalgae. *Renewable and Sustainable Energy Reviews*, Volume 58, pp. 75–86

Leroy, S., Dislaire, G., Bastin, D., Pirard, E., 2011. Optical
Analysis of Particle Size and Chromite Liberation from Pulp Samples of a UG2
Ore Regrinding Circuit.* **Minerals
Engineering*, Volume 24(12),
pp. 1340–1347

Miettinen, T., Ralston, J., Fornasiero, D., 2010. The Limits of Fine
Particle Flotation. *Minerals Engineering*, Volume 23(5), pp. 420–437

Rizaldi, M.I., Rahman, A., Deendarlianto, A., Prihantini, N.B.,
Nasruddin, N., 2019. Generation of Microbubbles through Single Loop and Double
Loop Fluid Oscillator for Photobioreactor Aeration. *International Journal of
Technology*, Volume 10(7), pp. 1446–1452

Runge, K.C., Franzidis, J.P., Manlapig, E., 2003. A Study of the Flotation Characteristics of
Different Mineralogical Classes in Different Streams of an Industrial Circuit.
*In:* Proceedings of the XXII International Mineral Processing Congress.
Cape Town, South Africa, pp. 962–972

Stevenson, P., Li, X., 2014. *Foam
Fractionation: Principles and Process Design* (1^{st} ed). Taylor & Francis Group, CRC Press, Boca
Raton

Sukarni, S., Sumarli, S., Nauri, I.M., Prasetiyo, A., Puspitasari, P.,
2019. Thermogravimetric Analysis on Combustion Behavior of Marine Microalgae
Spirulina Platensis Induced by MgCO_{3} and Al_{2}O_{3}
Additives. *International Journal of Technology*, Volume 10(6), pp. 1174–1183

Sutherland, K.L., 1948. Physical Chemistry of Flotation. XI. Kinetics
of the Flotation Process. *The Journal of
Physical and Colloid Chemistry*,
Volume 52(2), pp. 394–425

Xia, W., 2017. Role of Particle Shape in the Floatability of Mineral
Particle: An Overview of Recent Advances. *Powder
Technology*, Volume 317, pp.
104–116

Yianatos, J.B., 2007. Fluid Flow and Kinetic Modelling in Flotation
Related Processes. *Journal of **Chemical
Engineering Research and Design*,
Volume 85(12), pp. 1591–1603