• International Journal of Technology (IJTech)
  • Vol 11, No 3 (2020)

Kinetic Study of Air Bubbles-Cetyltrimethylammonium Bromide (CTAB) Surfactant for Recovering Microalgae Biomass in a Foam Flotation Column

Muayad A. Shihab, Mohammed A. Dhahir, Hamad K. Mohammed

Corresponding email: muayad.abed@tu.edu.iq


Cite this article as:
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. International Journal of Technology, Volume 11(3), pp. 440-449

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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
Email to Corresponding Author

Abstract
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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 R2 (>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  (mg/ml) is the concentration of valuable particles in the collection zone,  (min-1) is the flotation rate constant,  (min) is the flotation time, and  is the flotation kinetic order.

The recovery of particles in the top product (foamate) (R ) at any time is defined as:


where Ci (mg/ml) is the initial concentration of particles in the bubbly liquid zone. The ultimate recovery (i.e. the maximum recovery, R?) after infinite time is calculated by Equation 2 at  (i.e. the particle concentration in the collection zone at t?  ) as follows:


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:


To account for the floatability distribution of particles in the collection zone, distributed rate constants are often utilized instead of a single rate constant (Yianatos, 2007). The floatability of particles can be simply defined as the tendency of particles to float or the fraction of floating particles (Runge et al., 2003; Corona-Arroyo et al., 2018), and it is a function of the particle characteristics that affect the flotation rate constant, such as particle size and shape, liberation properties of the particles, hydrophobicity, surface energy, liquid surface tension, and pH (Leroy et al., 2011; Guerrero-Pérez et al., 2017; Xia, 2017; Corona-Arroyo et al., 2018). Particle floatability is not affected by the operational conditions of the flotation process, and this notion was introduced to extend the applicability of flotation kinetic models to account for the heterogeneity of particles (Bu et al., 2016a). However, this may not apply to the flotation of microalgae cells, since they have, to a certain extent, a narrow size distribution and similar shapes and surface properties. Nevertheless, some microalgae aggregations resulting from the presence of surfactant have been observed under microscope, with possible simple variations in surface energy and hydrophobicity among microalgae cells during the growth period, which might result in different flotation rates; Equation 4 is therefore written as follows:

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.

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