2018-05-27T21:14:41Z
http://gpj.ui.ac.ir/?_action=export&rf=summon&issue=3727
Gas Processing
2322-3251
2322-3251
2013
1
1
Formation Kinetics of Structure H Gas Hydrate
Mohsen
Vafaii Sefti
Behnaz
Parvizi
Farshad
Varaminian
This paper investigates the kinetics of structure H (sH) formation kinetics above and below the structure I (sI) formation equilibrium curve at temperatures of between 2Â°C and 6Â°C. Methane was used as a help gas and methylcyclohexane (MCH) was used as sH former. It was concluded that in the points above the sI formation equilibrium curve, at the first, the sI forms, and then converts to sH because of the low solubility of MCH in water. In the points under the sI equilibrium curve, at the first sH forms after a long induction time. The study also show the effect of the addition of hydroxymethylcellulose (in three concentrations of 0.05, 0.1 and 0.15 Wt%) on sH hydrate formation kinetics. With this additive, it was observed that the rate of sH formation increases.
sH
sI
Formation Kinetics
Gas Hydrate
2013
01
01
1
8
http://gpj.ui.ac.ir/article_20160_a19a3727658465066539b0ccf77cf6a4.pdf
Gas Processing
2322-3251
2322-3251
2013
1
1
Selection of the Best Efficient Method for Natural Gas Storage at High Capacities Using TOPSIS Method
Alireza
Sanaei
Seyed Hamidreza
Yousefi
Ehsan
Khamehchi
Nowadays one of the most important energy sources is natural gas. By depletion of oil reservoirs in the world, natural gasÂ will emerge as the future energy source for human life. One of the major concerns of gas suppliers is being able to supply this source of energy the entire year. This concern intensifies during more consuming seasons of the year when the demand for natural gas increases, resulting in a lot of problems such as pressure depletion in the pipelines. One of the most effective policies to prevent pressure depletion is gas storage in warm seasons of the year when public demand is low. In this paper three different methods of underground and surface gas storage at high capacities have been discussed which are as follows: depleted oil and gas reservoirs, liquefied gas storage, and gas hydrates storage. In this study, the NPV function for economical evaluation of these three natural gas storage methods was employed. Finally, after assessing the technical and economical aspects of these methods, the TOPSIS model was constructed and depleted oil and gas reservoirs storage selected as the best natural gas storage method at high capacities.
Natural Gas Storage
Net present value
TOPSIS Method
2013
01
01
9
18
http://gpj.ui.ac.ir/article_20161_e6cc8e5a259d810d54bb012071e8d24b.pdf
Gas Processing
2322-3251
2322-3251
2013
1
1
The Design of the Best Heat Integrated Separation Systems Using Harmony Search Algorithm
Gholam Reza
Salehi
Majid
Amidpour
Bahram
Ghorbani
Kazem
Hasanzadeh Lashkajani
The synthesis of heat integrated multi-component distillation systems is complex due to its huge search space for structural combination and optimization computation. To provide a systematic approach and tools for the synthesis design of distillation systems, a new method for modeling heat integrated columns is presented, and the operating cost objective function is minimized by improved harmony search algorithm (IHS). This paper studies a quick method for the synthesis of heat integrated distillation column sequences and IHS -based optimization strategy for the optimization of separation sequences with their heat integration.Â
Distillation Sequence
Heat Integration
Optimization
IHSA
2013
01
01
19
40
http://gpj.ui.ac.ir/article_20162_439ea12ae7b36ea933e2dc029c2115fe.pdf
Gas Processing
2322-3251
2322-3251
2013
1
1
Predicting the Hydrate Formation Temperature by a New Correlation and Neural Network
Hamidreza
Yousefi
Ebrahim
Shamohammadi
Ehsan
Khamehchi
Gas hydrates are a costly problem when they plug oil and gas pipelines. The best way to determine the HFT and pressure is to measure these conditions experimentally for every gas system. Since this is not practical in terms of time and money, correlations are the other alternative tools. There are a small number of correlations for specific gravity method to predict the hydrate formation. As the hydrate formation temperature is a function of pressure and gas gravity, an empirical correlation is presented for predicting the hydrate formation temperature. In order to obtain a new proposed correlation, 356 experimental data points have been collected from gas-gravity curves. This correlation is programmed and assessed with respect to its capabilities to match experimental data published in the literature under varying system conditions (i.e. temperature, pressure, and composition).The SPSS software has been employed for statistical analysis of the data. In order to establish a method to predict the hydrate formation temperature, a new neural network has also been developed with the BP(Back Propagation) method. This neural network model enables the user to accurately predict hydrate formation conditions for a given gas mixture, without having to do costly experimental measurements.
Hydrate Formation Temperature
AUT Correlation
Artificial Neural Network
2013
01
01
41
50
http://gpj.ui.ac.ir/article_20158_b01023a76a34375e55ef613e5f004495.pdf
Gas Processing
2322-3251
2322-3251
2013
1
1
The Design and Optimization of Distillation Column with Heat and Power Integrated Systems
Gholam Reza
Salehi
Bahram
Ghorbani
Kazem
Hasanzadeh Lashkajani
Majid
Amidpour
Based on two integration steps, an optimization framework is proposed in this work for the synthesis and design of complex distillation sequence. The first step is to employ heat integration in sequence and reduce the heat consumption and total annual cost of the process. The second one is to increase the exergetic efficiency of sequence by generating power in implemented expanders in sequence. The profit of power generation directly affects the operating cost of the process and decreases the total annual cost. In each step, the target is to minimize the objective function of total annual cost. A simulator is used to simulate the equipmentâs specification and formulate the objective function of cost. Results from employing these two integration steps for the considered case study show the advantages of such a complex distillation sequence with heat integration and power generation. The results represent a very high improvement for the sequence Indirect since the properties of the intake flow to the process are in a way that in this sequence not only do we have a high freedom for carrying out heat integration, but a large amount of power is also produced between the columns due to having high flow rate flows between the columns
Distillation
Sequence
Modeling
Integration
Optimization
Expander
2013
01
01
51
68
http://gpj.ui.ac.ir/article_20163_6587325a8883837189d6101ef0033303.pdf
Gas Processing
2322-3251
2322-3251
2013
1
1
The Indication of Two-Phase Flow Pattern and Slug Characteristics in a Pipeline Using CFD Method
Elahe
Bahramifar
Rahbar
Rahimi
Maryam
Mazarei Sotoodeh
Multiphase flows are commonly encountered in oil and gas industries. The transport of multiphase flow causes the formation of slug, the increase of pressure drop and the possibility of phase changes therefore, a set of simulation runs was performed to predict flow regimes in a horizontal pipeline, and the results were compared with the Baker chart. The effects of small downward inclinations of pipelines on the formation of slugs were also considered.Volume of Fluid model (VOF) has been used to predict the flow regimes in a horizontal pipeline within an 8-cm diameter and 7-m length pipe. In order to identify the critical parameters of slug flow (mean slug frequency, slug translational velocity, hold up and pressure drop), the simulations were carried out in a 7.63-cm diameter and 5-m length pipe mounted with three different inclination angles of 0 ,-0.5 and -0.8 . A good agreement between CFD model and experimental data has shown the advantage ofÂ Â VOF model for studying two-phase flows.
Inclination
Two-Phase Flow
Slug Flow
CFD
VOF
Baker Chart
Two
Phase Flow
2013
01
01
70
87
http://gpj.ui.ac.ir/article_20159_e0cd5b37c713a41c2751bb1b193b8776.pdf