Sintesis ZnO:Al Sebagai Bahan Transparent Conducting Oxide (TCO) dengan Metode Spray Pyrolysis

ZnO:Al particles are widely used as semiconductor material in various fields of technology, such as transparent conducting oxide (TCO). Synthesis of ZnO:Al particles using spray pyrolysis method has many advantages. The generated particles are relatively homogenous size distribution, spherical, and easily adjusted the particle size in range nano-submicrometer. Here, we studied the effect of doping concentration (1-4 at.%), operation temperature (500-900°C) and carrier gas flow rate (2-4 L/min) on the characteristics of the generated particles including morphology, crystallinity, and transparancy. In order to analyse the morphology, crystallinity, and transparancy of the generated particles, we used Scanning Electron Microscope (SEM), X-ray Diffraction (XRD), dan UV-Vis spectrophotometer, respectively. The optimum condition for the highest crystallinity and transparancy was obtained by partices synthesized using doping concentration of 2 at.%, furnace of 900°C and carrier gas flow rate of 2 liter/minute Keywords: Al doped ZnO, transparent conducting oxide, particle morphology, crystalline size, transparanc

Computational Study of the Time-dependent Flow Field of a Water-Molasses Mixture Inside a Stirred Vessel

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

Turbulence Modeling in Side-Entry Stirred Tank Mixing Time Determination

Mixing is one of the critical processes in the industry. The stirred tank is one of the operating units commonly used in the mixing process. Several factors greatly influence the efficiency of the stirred tank, including the stirred-tank design, operating conditions, and working fluid properties. The side-entry stirred tank is widely applied in industry, among others; the processing of crude oil in the refinery industry, water-molasses mixing in the bioethanol industry, pulp stock chest in the pulp and paper industry, and anaerobic digester for biogas reactors. Mixing time is one of the critical parameters used in the design of the stirred tank. This research will model mixing time in a flat bottomed-cylindrical side-entry stirred tank with dimensions D = 40 cm and T = 40 cm using CFD ANSYS 18.2 by applying the Standard κ − ε (SKE) and Realizable κ − ε (RKE) turbulence models. The stirrer used is a three-blade marine propeller d = 4 cm which is an axial type impeller. The phenomenon of mixing in the side-entry stirred tank, qualitatively described through computational prediction results in the form of flow profiles and tracer density change contours locally. Moreover, quantitatively indicated by mixing time validated using experimental data carried out by the conductometry method. The computational prediction shows that the mixing time modeled using the SKE turbulence model shows a similarity level of 68.16%, while the RKE turbulence model shows 31.94%