Amir H. Mohammadi

Optimisation of the thermodynamic performance of the Stirling engine

In this communication, the thermodynamic performance of an ideal Stirling cycle engine has been investigated. In this regard, the first law of thermodynamics has been employed to determine state of total heat addition, network output, and thermal efficiency with changes in dead volume percentage and regenerator effectiveness. Second law analysis is applied to obtain the trends for the total entropy generation of the cycle. Moreover, the entropy generation of each element involving the Stirling cycle processes is measured. Three objective functions including the output power per rate of mass of the ideal gas working fluid (wnet) and the thermal efficiency (ηt) have been considered simultaneously for maximisation, and the ratio of total entropy generation to rate of mass of the ideal gas working fluid of the Stirling engine is minimised at the same time. Multi-objective evolutionary algorithms based on the NSGA-II algorithm have been employed, while effectiveness of the regenerator, effectiveness of low- and high-temperature heat exchangers, effectiveness of high-temperature heat exchanger, temperatures of the hot side and cold side, and dead volume ratio are considered as decision variables. After the definition of the Pareto optimal frontier, the final optimal solution has been selected using different decision-making methods such as the fuzzy Bellman–Zadeh, LINMAP and TOPSIS.

Compositional Model for Estimating Asphaltene Precipitation Conditions in Live Reservoir Oil Systems

Asphaltenes form the most polar fractions in crude oil, which decrease considerably the rock permeability and the oil recovery and in total can cause operational problems. Hence, it is important to estimate the asphaltene precipitation as a function of operating conditions, crude oil composition, and characterization. In this article, a reliable and robust model, namely, the least squares support vector machine, is applied to predict the onset pressures of asphaltene precipitation in live oil systems as well as oil saturation conditions. To pursue our objective, we used literature-reported onset and saturation (bubble point) pressures data of various live oils from different regions, but mostly from the Middle East, with different amounts of asphaltenes. The results indicate that the proposed strategy provides reasonably satisfactory predictive results. Additionally, the obtained results demonstrate not only the validation of the proposed method but also pose an interesting alternative to the classic methods of estimating asphaltene precipitation due to the low number of adjustable parameters used in our model.

Experimental measurement and modeling of saturated reservoir oil viscosity

A novel mathematical-based approach is proposed to develop reliable models for prediction of saturated crude oil viscosity in a wide range of PVT properties. A new soft computing approach, namely least square support vector machine modeling optimized with coupled simulated annealing optimization technique, is proposed. Six models have been developed to predict saturated oil viscosity, which are designed in such a way that could predict saturated oil viscosity with every available PVT parameter. The constructed models are evaluated by carrying out extensive experimental saturated crude oil viscosity data from Iranian oil reservoirs, which were measured using a “Rolling Ball viscometer.” To evaluate the performance and accuracy of these models, statistical and graphical error analyses were used simultaneously. The obtained results demonstrated that the proposed models are more robust, reliable and efficient than existing techniques for prediction of saturated crude oil viscosity.

A hybrid particle swarm optimization-genetic algorithm for optimal location of svc devices in power system planning

The particle swarm optimization (PSO) was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the other hand, genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to initial population. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient. Optimal location of SVC using this hybrid PSO-GA algorithm is found. We have also found the optimal place of SVC using GA and PSO separately and compared the results. It has been shown that the new algorithm is more effective and efficient. An IEEE 68 bus test system is used for simulation.

Efficient screening of enhanced oil recovery methods and predictive economic analysis

Abstract Oil demand for economic development around the world is rapidly increasing. Moreover, oil production rates are getting a peak in mature reservoirs and tending to decline in the near future, which has led to considerable researches on enhanced oil recovery (EOR) methods. Therefore, an efficient technical and economical screening to appropriate selection of EOR methods can make savings in time and cost. The purpose of this communication is to present a method to select an efficient EOR process and investigate its economic parameters. A database of reservoir parameters of rock and fluid properties along with successful EOR techniques has been collected and analyzed. First, an artificial neural network (ANN) was developed to classify the EOR methods technically. Then, an economical EOR screening model was designed, and then, future cash flows on the use of EOR methods were predicted. The results show that the ANN system can select proper EOR methods and classify them. Moreover, the obtained results indicate that the economic analysis performed in this study is efficient and useful to predict future cash flows.

SUBSURFACE CARBON DIOXIDE STORAGE THROUGH CLATHRATE HYDRATE FORMATION

Rising atmospheric emissions as a result of fossil fuel consumption is a major concern for the developed and developing countries, considering the role it plays in the greenhouse effect and hence global climate change. Various schemes for underground CO2 storage (viz. geologic disposal into coal seams, depleted oil/gas reservoirs, salt caverns, and deep oceans) have already been reported in the literature. Subsurface CO2 storage through clathrate hydrate formation is a novel option for the reduction of atmospheric carbon content and permanent underground CO2 disposal over geological periods. Depths of CO2 injection, respective pressure-temperature conditions, water salinity etc. are all important factors for successful CO2 sequestration. Furthermore if CO2 is injected/stored in methane hydrate reservoirs it could be possible to produce low-carbon methane energy, thereby offsetting the cost of CO2 transportation and disposal. In this communication, we present the results of experiments carried out to understand the mechanisms of CH4 displacement in hydrate structure by injected CO2 and the formation of simple CO2 or mixed CH4-CO2 hydrates, thereby simulating the conditions of CO2 injection into CH4 hydrate reservoirs. We used two sets of experimental rigs specifically designed for studying gas hydrates in porous media. They are the Medium Pressure Glass Micromodel (80 bar) for visual observation of gas hydrate formation / dissociation and distribution in porous media, and the Ultrasonic Rig (400 bar) for studying CO2 sequestration in CH4 hydrates in synthetic porous media.

Development of Predictive Techniques for Estimating Liquid Water-Hydrate Equilibrium of Water-Hydrocarbon System

In this communication, we review recent studies by these authors for modeling the 𝐿𝑊-H equilibrium. With the aim of estimating the solubility of pure hydrocarbon hydrate former in pure water in equilibrium with gas hydrates, a thermodynamic model is introduced based on equality of water fugacity in the liquid water and hydrate phases. The solid solution theory of Van der Waals-Platteeuw is employed for calculating the fugacity of water in the hydrate phase. The Henrys law approach and the activity coefficient method are used to calculate the fugacities of the hydrocarbon hydrate former and water in the liquid water phase, respectively. The results of this model are successfully compared with some selected experimental data from the literature. A mathematical model based on feed-forward artificial neural network algorithm is then introduced to estimate the solubility of pure hydrocarbon hydrate former in pure water being in equilibrium with gas hydrates. Independent experimental data (not employed in training and testing steps) are used to examine the reliability of this algorithm successfully.

A Novel Algorithm for Optimal Location of FACTS Devices in Power System Planning

The particle swarm optimization (PSO) has been shown to converge rapidly during the initial stages of a global search, but around global optimum, the search pro~ess becomes very slow. On the other hand, the genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to initial population. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient and can find the optimal solution more accurately and with less computational time. Optimal location of SVC using this hybrid PSO-GA algorithm is found. We have also found the optimal place of SVC using GA and PSO separately and have compared the results. It has been shown that the new algorithm is more effective and efficient. An IEEE 68 bus test system is used for simulation.

Evaluating the unloading gradient pressure in continuous gas-lift systems during petroleum production operations

Evaluating the performance, applicability, and field testing of various artificial lift methods, in particular continued gas-lift, can be time consuming and costly. To overcome these drawbacks, it is needed to propose a reliable model to estimate gas-lift applicability in advance of the installation under specific well operational conditions such as tubing size and design oil rate. In this study, the robust least square modification of support vector machine (LSSVM) methodology is implemented to propose a computer program, by which the unloading pressure gradient region can be determined in various design oil production rates and also tubing sizes. The developed LSSVM model results indicate 1.084% average absolute relative deviation from the corresponding unloading pressure gradient literature values, and squared correlation coefficient of 0.9994.

Online Solving Of Economic Dispatch Problem Using Neural Network Approach And Comparing It With Classical Method

In this study, two methods for solving economic dispatch problems, namely Hopfield neural network and lambda iteration method are compared. Three sample of power system with 3, 6 and 20 units have been considered. The time required for CPU, for solving economic dispatch of these two systems has been calculated. It has been shown that for on-line economic dispatch, Hopfield neural network is more efficient and the time required for convergence is considerably smaller compared to classical methods