|Suci Madhania||-Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia -Chemical Engineering Department, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo,|
|Tantular Nurtono||Chemical Engineering Department, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia|
|Sugeng Winardi||Chemical Engineering Department, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia|
|Yuswan Muharam||Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
|Widodo Wahyu Purwanto||Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
Detailed information on the flow field in the operation of a mixing unit is necessary for the optimal design of the reactor. The flow field characteristic is an essential factor in obtaining an optimal stirred vessel design. The efficiency of the stirred vessel system depends on, for example, the stirred vessel geometry, the flow induced by the impeller, the working fluid properties and the operating condition. The aim of this study is to exhibit the time-dependent flow field of the mixing process inside a stirred vessel for different propeller rotational speeds using computational fluid dynamics methods. The working fluid in question is molasses and water, which is a miscible liquid. The stirred vessel is a conical-bottomed cylindrical vessel (D = 0.28 m and H = 0.395 m) equipped with a three-blade propeller (d = 0.036 m). The transient calculation was conducted using ANSYS Fluent version 18.2. The Mixture multiphase flow model coupled with the Reynolds-averaged Navier-Stokes Standard k-? (SKE) turbulence model was applied to capture the information on the time-dependent flow fields at various propeller rotational speeds inside the stirred vessel. The flow generated by the propeller was compared at 1000 rpm, 1300 rpm and 1500 rpm. The Multiple Reference Frame method was used to solve the moving domain and stationary domain multiple frames case. The results revealed the local velocity, flow pattern, molasses volume fraction value, density gradient distribution, power number and flow number. The profile of all the variables determines the optimal operating conditions for the degree of mixing required.
Computational fluid dynamics; Multiphase flow; Propeller; Stirred vessel; Turbulent flow
The blending of water and molasses with different properties has related applications in the production of bioethanol. The water-molasses mixture includes the miscible liquid system. Homogenization of two mutually dissolvable liquids is achieved through molecular diffusion and convection, but stirring can speed up the homogeneous condition reaches. The stirred vessel is one type of mixing equipment used in an industrial process. The flow field information inside the stirred vessel is necessary for the optimal design of the reactor. The efficiency of the stirred vessel system depends on, for example, the stirred vessel geometry and the flow induced by the impeller. The flow generated by the impeller is influenced by various factors including the shapes and number of impeller (Muharam & Kurniawan, 2016), the impeller size, the impeller type (Zhao et al., 2011; Sossa-Echeverria & Taghipour, 2015), the location and layout of the impeller (Rahimi, 2005) and rotational speed.
The experimental study is the primary method used to describe the flow characteristic in a stirred vessel. Direct measuring techniques can disturb the flow field, while indirect methods may not be appropriate as they are often too expensive and take a long time to carry out. However, advancements in computer technology and mathematical modeling leads researchers to prefer to use computational fluid dynamics (CFD) over an experimental study. CFD has become a capable device for describing fluid flow and has also been successfully used to predict the mixing behavior of the miscible liquid system, e.g. in the mixing of ethanol and glycerol (Al-Qaessi & Abu-Farah, 2014), the homogenisation of two mutually dissolvable fluids with different properties (Derksen, 2011), the water-ethanol system (Orsi et al., 2013) and the blending of two miscible liquids with different densities and viscosities (Montante et al., 2016).
The flow conditions in stirred tanks are mostly turbulent due to the presence of the impeller; therefore, the selection of the turbulence model should be appropriately considered as a way of resolving the effect of turbulence inside the system unit that operates at a high Reynolds number flow (Daryus et al., 2016). Commonly encountered forms of turbulence model include the Direct Numerical Simulation (DNS), Large Eddy Simulation (LES) and Reynolds-Averaged Navier-Stokes (RANS) models. Time-resolved and full-length-scale Navier-Stokes equations without any models or assumptions are solved directly by DNS. In LES, large eddies are resolved directly, while the effects of small eddies are modeled using subgrid-scale stress. However, the equations in RANS calculate the average of large eddies and some assumptions should be applied. Meanwhile, in real conditions, the tremendous computational costs of the LES and DNS still pose a significant barrier and there is a greater focus on mean flow characteristics as opposed to detailed turbulence. Therefore, the RANS model is commonly used. The RANS model comprises the standard k-? (SKE) model (Launder & Spalding, 1974), the renormalization k-? (RNG) model (Yakhot & Orszag, 1986) and the realizable k-? (RKE) model (Shih et al., 1995).
In CFD, a stirred tank system is included in the multi-reference frame category due to the presence of the impeller, which is a moving part. There are several approaches to handling multi-reference frame cases, among others the Sliding-Mesh (SM) and the Multiple Reference Frame (MRF) method. The SM is an unsteady approach to treat the moving and stationary frame interaction, while MRF is a steady state condition (Luo et al., 1994).
Power number (Np) and Flow number (NQ) are important factors for characterising the impeller inside the stirred vessel. Zadghaffari et al. (2009) used the power number in his studies on mechanical agitation inside the stirred vessel. The power number can be obtained using both the torque applied in the impeller and by integrating the turbulence energy dissipation rate over the tank volume (Ge et al., 2014). However, based on Singh et al. (2011), torque-based prediction is more accurate than prediction based on the turbulence energy dissipation rate.
Up to now, the main progress of CFD study on the mixing process inside a stirred vessel has been achieved in the context of a top-entry installed propeller configuration. However, relatively few studies have been undertaken with regard to a side-entry configuration, e.g. CFD simulation on the mixing of crude oil in flat-bottomed cylindrical storage tanks (Dakhel & Rahimi, 2004), the influence of propeller layout on the mixing of the crude oil system inside a stirred tank (Rahimi, 2005), the intensity of solid-liquid mixing inside a stirred tank with various impeller layouts (Wu, 2011), and the mixing of pseudoplastic solutions (Sossa-Echeverria & Taghipour, 2015).
This study aims to present detailed information on the time-dependent flow fields generated by a three-blade propeller at three different rotational speeds for the water-molasses mixing process in a side-entry configuration stirred tank using the ANSYS Fluent CFD method. This research is a continuation of previous research that has reviewed the mixing effectiveness of different propeller installments (Madhania et al., 2017) and the mixing phenomena associated with different computational solution strategies (Madhania et al., 2018).
The time-dependent flow field of two mutually dissolved liquids in a stirred vessel was predicted using ANSYS Fluent 18.2 version at three different propeller rotational speeds: 1000, 1300 and 1500 rpm. The RANS SKE was used as the turbulence model, and the MRF method was used for counting the moving-stationary frame interaction. The well-documented time-dependent flow fields of local velocity, flow pattern, molasses volume fraction value, density gradient, power number and flow number were elucidated. The flow pattern formed a non-symmetric double-loop circulation pattern near the propeller. The propeller vicinity velocity showed a maximum value of 3.83 ms-1. The predicted molasses volume fraction value, density gradient and power number all fell as a function of time at the different propeller rotational speeds under the mixing process, which was in contrast to the flow number, which was the opposite of the other variables. As the rotational speed increased, so the gradient further decreased. The profile of all the variables can be applied to determine the optimal operating conditions for mixing water and molasses with respect to the degree of mixing required.
The United States Agency for International Development (USAID) supports the publication of this research/article through the Sustainable Higher Education Research Alliance (SHERA) Program for Universitas Indonesia’s Scientific Modeling, Application, Research and Training for City-centered Innovation and Technology (SMART CITY) Project, Grant #AID-497-A-1600004, Sub Grant #IIE-00000078-UI-1, contract number 0142/UN2.R3.SC/HKP.05.01/2018.
Al-Qaessi, F., Abu-Farah, L., 2014. Prediction of Mixing Time for Miscible Liquids by CFD Simulation in Semi-batch and Batch Reactors. Engineering Applications of Computational Fluid Mechanics, Volume 3(1), pp. 135–146
Ansys-Fluent, 2017. Fluent 18.2 Documentation Fluent Theory Guide, Canonsburg. PA, USA, ANSYS, Inc.
Dakhel, A.A., Rahimi M., 2004. CFD Simulation of Homogenization in Large-scale Crude Oil Storage Tanks. Journal of Petroleum Science and Engineering, Volume 43(3-4), pp. 151–161
Daryus, A., Siswantara, A.I., Darmawan, S., Gunadi, G.G.R., Camalia, R., 2016. CFD Simulation of Turbulent Flows in Proto X-3 Bioenergy Micro Gas Turbine Combustor using STD k-e and RNG k-e Model for Green Building Application. International Journal of Technology, Volume 7(2), pp. 204–211
Derksen, J.J., 2011. Blending of Miscible Liquids with Different Densities Starting from a Stratified State. Computers & Fluids, Volume 50(1), pp. 35–45
Ge, C.Y., Wang, J.J., Gu, X.P., Feng L.F., 2014. CFD Simulation and PIV Measurement of the Flow Field Generated by Modified Pitched Blade Turbine Impellers. Chemical Engineering Research and Design, Volume 92(6), pp. 1027–1036
Launder, B.E., Spalding, D.B., 1974. The Numerical Computation of Turbulent Flows. Computer Methods in Applied Mechanics and Engineering, Volume 3(2), pp. 269–289
Luo, J.Y., Issa, R.I., Gosman, A.D., 1994. Prediction of Impeller Induced Flows in Mixing Vessels using Multiple Frames of Reference. Institution of Chemical Engineers Symposium Series, Volume 136, pp. 549–556
Madhania, S., Cahyani, A.B., Nurtono, T., Muharam, Y., Winardi, S., Purwanto, W.W., 2018. CFD Study of Mixing Miscible Liquid with High Viscosity Difference in a Stirred Tank. In: IOP Conference Series: Materials Science and Engineering, Volume 316
Madhania, S., Nurtono, T., Cahyani, A.B., Carolina, Muharam, Y., Winardi, S., Purwanto, W.W., 2017. Mixing Behaviour of Miscible Liquid-liquid Multiphase Flow in Stirred Tank with Different Marine Propeller Installment by Computational Fluid Dynamics Method. Chemical Engineering Transactions, Volume 56, pp. 1057–1062
Marshall, E.M., Bakker, A., 2004. Computational Fluid Mixing. Handbook of Industrial Mixing, Paul, E.L., Atiemo-Obeng, V.A., Kresta, S.M., (eds.), John Wiley & Sons, Inc., Hoboken, New Jersey, USA
Montante, G., Coroneo, M., Paglianti, A., 2016. Blending of Miscible Liquids with Different Densities and Viscosities in Static Mixers. Chemical Engineering Science, Volume 141, pp. 250–260
Muharam, Y., Kurniawan, A., 2016. Computational Fluid Dynamic Application in Scale-up of a Stirred-batch Reactor for Degumming Crude Palm Oil. International Journal of Technology, Volume 7(8), pp. 1344–1351
Orsi, G., Roudgar, M., Brunazzi, E., Galletti, C., Mauri, R., 2013. Water–Ethanol Mixing in T-shaped Microdevices. Chemical Engineering Science, Volume 95, pp. 174–183
Rahimi, M., 2005. The Effect of Impellers Layout on Mixing Time in a Large-scale Crude Oil Storage Tank. Journal of Petroleum Science and Engineering, Volume 46(3), pp. 161–170
Roache, P.J., 1994. A Method for Uniform Reporting of Grid Refinement Studies. J. Fluids Eng, Volume 116(3), pp. 405–413
Roache, P.J., 1997. Quantification of Uncertainty in Computational Fluid Dynamics. Annu. Rev. Fluid Mech., Volume 29(1), pp. 123–160
Patankar, S.V., 1980. Numerical Heat Transfer and Fluid Flow. Washington, DC: Hemisphere Publishing Corporation
Shih, T.H., Liou, W.W., Shabbir, A., Yang, Z., Zhu, J., 1995. A New k-? Eddy Viscosity Model for High Reynolds Number Turbulent Flows. Computers & Fluids, Volume 24(3), pp. 227–238
Singh, H., Fletcher D.F., Nijdam J.J., 2011. An Assessment of Different Turbulence Models for Predicting Flow in a Baffled Tank Stirred with a Rushton Turbine. Chemical Engineering Science, Volume 66(23), pp. 5976–5988
Sossa-Echeverria, J., Taghipour, F., 2015. Computational Simulation of Mixing Flow of Shear Thinning Non-Newtonian Fluids with Various Impellers in a Stirred Tank. Chemical Engineering and Processing: Process Intensification, Volume 93, pp. 66–78
Van Doormaal, J.P., Raithby, G.D., 1984. Enhancements of the Simple Method for Predicting Incompressible Fluid Flows. Numerical Heat Transfer, Volume 7(2), pp. 147–163
Wu, B., 2011. CFD Investigation of Turbulence Models for Mechanical Agitation of Non-Newtonian Fluids in Anaerobic Digesters. Water Research, Volume 45(5), pp. 2082–2094
Yakhot, V., Orszag, S.A., 1986. Renormalization Group Analysis of Turbulence. I. Basic Theory. Journal of Scientific Computing, Volume 1(1), pp. 3–51
Zadghaffari, R., Moghaddas, J.S., Revstedt, J., 2009. A Mixing Study in a Double-Rushton Stirred Tank. Computers & Chemical Engineering, Volume 33(7), pp. 1240–1246
Zhao, J., Gao, Z., Bao Y., 2011. Effects of the Blade Shape on the Trailing Vortices in Liquid Flow Generated by Disc Turbines. Chinese Journal of Chemical Engineering, Volume 19(2), pp. 232–242