Kartik Chandra Ghanta

Slurry flow modelling by CFD

An attempt has been made in the present study to develop a generalized slurry flow model using CFD and utilize the model to predict concentration profile. The purpose of the CFD model is to gain better insight into the solid liquid slurry flow in pipelines. Initially a three-dimensional model problem was developed to understand the influence of the particle drag coefficient on the solid concentration profile. The preliminary simulations highlighted the need for correct modelling of the inter phase drag force. The various drag correlations available in the literature were incorporated into a two-fluid model (Euler-Euler) along with the standard k-e turbulence model with mixture properties to simulate the turbulent solid-liquid flow in a pipeline. The computational model was mapped on to a commercial CFD solver FLUENT6.2 (of Fluent Inc., USA). To push the envelope of applicability of the simulation, recent data from Kaushal (2005) (with solid concentration up to 50%) was selected to validate the three dimensional simulations. The experimental data consisted of water-glass bead slurry at 125 and 440-micron particle with different flow velocity (from 1 to 5 m/s) and overall concentration up to 10 to 50% by volume. The predicted pressure drop and concentration profile were validated by experimental data and showed excellent agreement. Interesting findings came out from the parametric study of velocity and concentration profiles. The computational model and results discussed in this work would be useful for extending the applications of CFD models for simulating large slurry pipelines.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Sequestration of carbon dioxide and production of biomolecules using cyanobacteria

A cyanobacterial strain, Synechococcus sp. NIT18, has been applied to sequester CO2 using sodium carbonate as inorganic carbon source due to its efficiency of CO2 bioconversion and high biomass production. The biomass obtained is used for the extraction of biomolecules - protein, carbohydrate and lipid. The main objective of the study is to maximize the biomass and biomolecules production with CO2 sequestration using cyanobacterial strain cultivated under different concentrations of CO2 (5-20%), pH (7-11) and inoculum size (5-12.5%) within a statistical framework. Maximum sequestration of CO2 and maximum productivities of protein, carbohydrate and lipid are 71.02%, 4.9 mg/L/day, 6.7 mg/L/day and 1.6 mg/L/day respectively, at initial CO2 concentration: 10%, pH: 9 and inoculum size: 12.5%. Since flue gas contains 10-15% CO2 and the present strain is able to sequester CO2 in this range, the strain could be considered as a useful tool for CO2 mitigation for greener world.

Phycoremediation of cyanide from coke–oven wastewater using cyanobacterial consortium

Abstract Owing to the presence of several toxic pollutants such as cyanide, phenol, ammonium, coke–oven wastewater is being considered as hazardous stream and needs to be treated properly. In the present study, cyanobacterial consortium of Dinophysis acuminata and Dinophysis caudata, collected from East Kolkata Wetland, was used for the treatment of both synthetic cyanide solution and real coke–oven wastewater. The growth kinetics was studied considering nitrate as substrate. Since consortium showed growth in cyanide solution, a model was proposed considering both nitrate and cyanide as substrates. The simulated data match quite well with experimental ones. Two coke–oven wastewater samples were collected—untreated one from equalization tank and another from secondary clarifier effluent and treated with consortium separately. Lipid was extracted from biomass of native cyanobacterial consortium, biomass treated with raw coke–oven wastewater and biomass treated with secondary clarifier effluents. Fatty acid methyl ester of such lipid samples was analyzed using gas chromatograph.