Prediction of Air Specific Heat Ratios at Elevated Pressures Using a Novel Modeling Approach

An accurate and efficient model based on least‐squares support vector machines (LSSVM) is developed for the determination of air specific heat ratios at elevated pressures. Additionally, the coupled simulated annealing optimization strategy is used to calculate the optimal values of the LSSVM parameters. A large dataset of air specific heat ratios as a function of temperature and pressure for about 170 samples is used to develop and validate the model. The leverage approach (Williams plot) is used to determine the applicability domain of the model and to detect probably erroneous data points. Comparison of the obtained results with a previously published correlation as well as an intelligent method demonstrates that the performance of the presented model is more satisfactory than that of other methods.     

Simple equations to correlate theoretical stages and operating reflux in fractionators

Natural gas is an important source of primary energy. Virtually all gas processing plants producing natural gas liquids require at least one fractionator to produce a liquid product which can meet sales specifications. Fractionation is one of the pivotal unit operations in refineries, gas processing and other industries utilized to separate mixtures into individual products. However, it is capital and energy intensive and, with decreasing relative volatility, the size and energy requirements of a column tend to increase. The primary parameters involved in the design of fractionators are the number of stages and the reflux ratio. The aim of this study is to develop easy-to-use equations, which are simpler than current available models involving a large number of parameters and requiring more complicated and longer computations, for an appropriate prediction the operating reflux ratio for a given number of stages. Alternatively, for a given reflux ratio, number of stages can be determined. The accuracy of the proposed equations was tested and found to be in excellent agreement with the reported data for the wide range of conditions, wherein the average absolute deviation percent of proposed equations being 1.5%. These simple-to-use equations can be of immense practical value for the engineers. In particular, process engineers would find the proposed approach to be user friendly involving no complex expressions with transparent calculations.     

Prediction of physical properties of hydrocarbon compounds using empirical correlations

Accurately describing the physical properties of hydrocarbon fractions is important to proper analysis and design of chemical, oil, and gas processing systems. Physical properties prediction of wide boiling range hydrocarbons is necessitated by the difficulty and expense of obtaining experimental data. A variety of mathematical relationships have been developed for this purpose, which differ greatly in approach and form. Though a theoretical justification for the developed correlations is advanced by some of the sources, the wide formula variety is evidence of their empirical nature. Therefore, care should be taken to examine which approach provides the most accurate system representation. The aim of this study was a better understanding of the methods predicting hydrocarbon compounds' physical properties that will impact unit operation, process simulation, and design.     

Simple methodology for sizing of absorbers for TEG (triethylene glycol) gas dehydration systems

Natural gas is an important source of primary energy and it is saturated with water vapor under normal production conditions. In the design of natural gas dehydration systems, correct estimation of absorption column size is crucial. Once the lean TEG (Triethylene glycol) concentration has been established, the circulation rate of TEG and number of trays (height of packing) must be determined. The current methods to correlate the TEG circulation rate, TEG purity, water removal efficiency, number of equilibrium stages (or height of packing) and the diameter of contactor employs rigorous calculation techniques involving more complicated and longer computations. The aim of this study is therefore to develop a simple-to-use method, by employing basic algebraic equations to correlate water removal efficiency as a function of TEG circulation rate and TEG purity for appropriate sizing of the absorber at wide range of operating conditions of TEG dehydration systems. Estimates from simplified approach were found to be quite reliable and accurate, as evidenced by the comparisons with literature data where the average absolute deviation percent from reported data in the literature shown to be around 0.05%.     

Rapid Prediction of CO2 Solubility in Aqueous Solutions of DEA and MDEA

In the present work, a simple‐to‐use correlation is developed to predict the solubility of CO₂ in aqueous solutions of DEA and MDEA as a function of the reduced partial pressure and temperature. Using the interaction parameters generated, the model is applied to correlate the CO₂ loading in different amine solutions. The results from the proposed correlation have been compared with the reported experimental data and it was found that there is a good agreement between the observed data and the model predictions over a wide range of operating conditions in aqueous solutions of both diethanolamine (DEA) and methyldiethanolamine (MDEA).     

Simple method for estimation of unsteady state conduction heat flow with variable surface temperature in slabs and spheres

When a solid is exposed to a hot gas or liquid, the resistance to heat transfer in the fluid is usually significant, and the surface temperature changes with time. In this work, an attempt has been made to formulate a simple-to-use method for one-dimensional heat flow with variable surface temperatures in slabs and spheres as a function of the Fourier number and the Biot number in order to arrive at the temperature distribution in the solid and the average solid temperature changes with time. The average absolute deviation between the reported data and the proposed correlations are found to be 1.8%.     

Estimation of breakthrough time for water coning in fractured systems: Experimental study and connectionist modeling

Water coning in petroleum reservoirs leads to lower well productivity and higher operational costs. Adequate knowledge of coning phenomena and breakthrough time is essential to overcome this issue. A series of experiments using fractured porous media models were conducted to investigate the effects of production process and pore structure characteristics on water coning. In addition, a hybrid artificial neural network (ANN) with particle swarm optimization (PSO) algorithm was applied to predict breakthrough time of water coning as a function of production rate and physical model properties. Data from the literature combined with experimental data generated in this study were used to develop and verify the ANN‐PSO model. A good correlation was found between the predicted and real data sets having an absolute maximum error percentage less than 9%. The developed ANN‐PSO model is able to estimate breakthrough time and critical production rate with higher accuracy compared to the conventional or back propagation (BP) ANN (ANN‐BP) and common correlations. The presence of vertical fractures was found to accelerate considerably the water coning phenomena during oil production. Results of this study using combined data suggest the potential application of ANN‐PSO in predicting the water breakthrough time and critical production rate that are critical in designing and evaluating production strategies for naturally fractured reservoirs. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1905–1919, 2014     

Using geometrical routing for overlay networking in MMOGs

At a first glance, transmitting update information to a geographic region in the virtual space seems to be an attractive primitive in Massively Multiplayer Online Gaming (MMOG) applications where players are constantly moving and need to send updates to their neighbors who are in the same region of the virtual space. The system would become more scalable if entities did not need to keep track of each other or send messages directly to one another. Rather, an entity could just send a message to a specific region in the virtual space (its area of effect), as opposed to sending packets to specific IP addresses, significantly reducing tracking and routing overhead. Fundamentally speaking, update message exchange is mostly based on users’ visibility range, which is mainly affected by proximity; i.e., avatars are interested in nodes within a specific distance around them. Therefore MMOG applications require a routing scheme that can deliver messages to specified locations in the virtual space. Such location based routing motivates the use of geographical routing, which has been introduced and successfully used in the context of wireless networks; however, in its current form it is not well suited for MMOGs which run on wired networks. In this article, we propose a scalable MMOG networking architecture based on hierarchical multi-grid geographical routing that is well suited for MMOG networks. We present our concept and design of hierarchical geometrical routing based on locality sensitive hashing, demonstrate its performance, and discuss both the strengths and shortcomings of our approach.     

Prediction of water removal rate in a natural gas dehydration system using radial basis function neural network

Natural gas commonly contains water as a contaminant that can condense to water or form gas hydrates, which causes a range of problems during gas production, transportation, and processing. Therefore, the removal of gas moisture is of great importance. A common and popular method for removing water contamination from natural gas is using solid dehydrators. Calcium chloride is a nonregenerative desiccant to dehydrate natural gas. With continual water adsorption, CaCl₂ changes to consecutively higher states of hydration, finally producing a CaCl₂ brine solution. This method does not require heating or moving parts. In addition, it does not react with H₂S or CO₂. These features make this method a popular one for drying natural gas. Nevertheless, precise and simple methods are needed to predict the water content of natural gas dried by calcium chloride dehydrator units. In this study, an intelligent method, called the radial basis function neural network, was incorporated to predict the gas moisture dehydrated by calcium chloride in dehydration units. Modeling was performed under different conditions of a fresh recharge and before recharging. The overall correlation factor of 0.9999 for both the fresh charge and before charging conditions showed that the outputs of the proposed models were in agreement with the experimental data. In addition, the developed models were compared with the previously proposed intelligent models and classic correlations. The comparison showed that the developed model is superior to the previously proposed models and correlations regarding the accuracy of prediction.