Two-Dimensional Simulation Model of Sediment Removal and Flow in Rectangular Sedimentation Basin

A steady, two-dimensional numerical model was created to study the hydrodynamics of a rectangular sedimentation basin under turbulent conditions. The strip integral method was used to formulate the flow equations, using a forward marching scheme for solving the governing partial differential equations of continuity, momentum, advection–diffusion, turbulent kinetic energy, and its dissipation. In this way the flow equations were converted to a set of ordinary differential equations (ODEs) in terms of the key physical parameters. These parameters, along with a set of shape functions, describe flow variables including the velocity, the concentration of suspended sediments, and both the kinetic energy and its dissipation rate. Four Gaussian distributions were investigated, one corresponding to each flow parameter. In order to calculate the turbulent shear stresses, a two-equation turbulence model (i.e., k-ε model) was used. A fourth order Runge–Kutta method numerically integrates the set of ODEs. Simulation results were compared with experimental data, and close agreement (generally within 5–10%) was observed.     

C/SiC Gradient Oxidation Protective Coating on Graphite by Modified Reactive Melt Infiltration Method: Effects of Processing Parameters on Transition Interface Thickness and High-Temperature Anti-oxidation Behavior

Metallurgical and Materials Transactions A - In this research, it was found that the C/SiC transition interface thickness increases without a significant decrease in toughness by modifying the...     

Numerical simulation of froth formation in aerated slurry coupled with population balance modelling

A computational fluid dynamic (CFD) model has been developed to incorporate pulp and froth zones into one model. In the present research, froth was considered as a separate phase comprised of a mixture of gas, liquid and solids. Considering the froth phase as a separate phase, allowed the incorporation of pulp and froth zones into one model by tracking the formation and destruction of the froth phase due to mass exchange between the pulp and froth. Bubble break-up and coalescence were taken into account in the pulp zone, by employing user functions, written using FORTRAN. The effect of bubble coalescence process due to ?lm rupture was considered in the froth phase. The variation in the concentration of attached particles due to attachment and detachment processes were also taken into account. The CFD model predicted the height of froth layer, the concentration of different bubble sizes in both pulp and froth zones, and finally the multiphase ?ow phenomena in the slurry column. Froth height was found to increase with the increase of gas flow rate while increasing solid concentration decreased froth height.     

Cascaded waste‐heat recovery as a green technology for energy sustainability in power generation

In this work, the Cascaded waste‐heat recovery (WHR) is analyzed from the thermodynamic point of view. Typically, WHR is most effective with small gas turbines and old machines which have a relatively higher design mass flow per kW and higher exhaust temperatures than new designs. The working fluid used in the WHR technology is propane, which vaporizes and condenses at low temperatures. The temperature of the heat source, the outlet pressure of the two expanders, and the mass flow rate of the working fluid are assumed as working variables of the technology. The effect of these variables on the thermal efficiency and power output is evaluated. The obtained results are analyzed and discussed. The results of the calculation are also compared with similar published studies. The overall efficiency considering the gas turbine upstream ranges from about 35% up to 39%. The highest efficiency and power output of the WHR alone at 900 K heat source temperature, 800 kPa condenser pressure, and 100 kg/s mass‐flow rate are 30% and 18 MW, respectively, for two‐expander WHR, and 18% and 9 MW, respectively, for single expander WHR. Copyright © 2014 John Wiley & Sons, Ltd.     

Analysis of steelmaking kinetics from IMPHOS pilot plant data

It is widely accepted that understanding the kinetics of steelmaking is a complex task, and reliable and validated kinetics models are required for developing successful steelmaking process models. Therefore, as an initial attempt, this paper analyses the applicability of first order kinetics to explain the steelmaking reaction kinetics using the published data in the IMproving Phosphorus Refining research report. The process data for 20 heats in a 6?tonne pilot plant were analysed for the removal of carbon, silicon, manganese and phosphorus using first order kinetics with static and dynamic equilibrium conditions. It was observed that the removal behaviour of silicon closely followed a first order kinetics relationship, while that of carbon only approximately followed a first order kinetics relationship. The removal of manganese did not show a good degree of fit with first order kinetics using static equilibrium condition, but a clear improvement was observed when calculated using dynamic equilibrium condition. In contrast, the kinetics of phosphorus oxidation did not follow any first order relationship.     

Synthesis of Architectural-Cubic Porous Silicon by Electroless Stain Etching in V2O5 and HF Solution

Silicon - Porous silicon (PSi) samples were prepared via electroless stain etching of silicon substrates in HF using V2O5 as an oxidant and ethanol as a surfactant for different etching times. The...     

Optimizing the location of the gas injection well during gas assisted gravity drainage in a fractured carbonate reservoir using artificial intelligence

Gas assisted gravity drainage (GAGD) is a novel subdivision of gas injection method. In this method the injection wells are located in the upper bed of the oil zone, and the production wells are drilled at the bottom bed of the oil zone. Reservoir simulation is among the decision tools for investigating production rate and selecting the best scenarios for developing the oil and gas fields. Selecting the location of the injection wells for reaching the optimized pressure and production rate is one of the most significant challenges during the injection process. Recent experiences have shown that artificial intelligence (AI) is a reliable solution for taking the mentioned decision appropriately and in a least possible time. This study is attributed to the investigation of applying the artificial neural network (ANN) as an artificial intelligence method and a potent predictor for choosing the most proper location for injection in a GAGD process in a fractured carbonate reservoir. The results of this investigation clearly show the efficiency of the ANN as a powerful tool for optimizing the location of the injection wells in a GAGD process. The comparison between the results of ANN and black oil simulator indicated that the predictions obtained from the ANN is highly reliable. In fact the production flow rate and pressure can be obtained in every possible location of the injection well.     

Modeling the oxidative coupling of methane using artificial neural network and optimizing of its operational conditions using genetic algorithm

The effect of some operating conditions such as temperature, gas hourly space velocity (GHSV), CH₄/O₂ ratio and diluents gas (mol% N₂) on ethylene production by oxidative coupling of methane (OCM) in a fixed bed reactor at atmospheric pressure was studied over Mn/Na₂WO₄/SiO₂ catalyst. Based on the properties of neural networks, an artificial neural network was used for model developing from experimental data. To prevent network complexity and effective data input to the network, principal component analysis method was used and the number of output parameters was reduced from 4 to 2. A feed-forward back-propagation network was used for simulating the relations between process operating conditions and those aspects of catalytic performance including conversion of methane, C₂ products selectivity, C2 yielding and C₂H₄/C₂H₆ ratio. Levenberg-Marquardt method is presented to train the network. For the first output, an optimum network with 4-9-1 topology and for the second output, an optimum network with 4-6-1 topology was prepared. After simulating the process as well as using ANNs, the operating conditions were optimized and a genetic algorithm based on maximum yield of C₂ was used. The average error in comparing the experimental and simulated values for methane conversion, C₂ products selectivity, yield of C₂ and C₂H₄/C₂H₆ ratio, was estimated as 2.73%, 10.66%, 5.48% and 10.28%, respectively.