http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Amir H. Mohammadi (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
Amir Dashti,Farshid Zargari,Hossein Riasat Harami,Amir H. Mohammadi,Zahra Nikfarjam 한국화학공학회 2019 Korean Journal of Chemical Engineering Vol.36 No.10
Predicting the solubility of acid gases in ionic liquids (ILs), has lately appeared as advantageous for natural gas purifying, which is equipped by powerful models considering technical and economic aspects. Important issue in the assessment of ILs for potential utilization in gas sweetening process is estimating the H2S solubility at various temperatures and pressures Experimental measurements are costly and take considerable time and effort. As a result, proposing methods for predicting the behavior of this system over a wide range of conditions is vital. In this regard, molecular dynamics simulation (MD) technique as well as artificial intelligence knowledge of hybrid genetic algorithmadaptive neuro fuzzy inference system (GA-ANFIS) and an empirical polynomial regression (PR) model were employed to estimate the solubility of H2S in [bmim][PF6] IL. Pressure and temperature are considered as the independent input variables and H2S solubility as the dependent output variable. The results of this study reveal that the simple fourthorder PR model and GA-ANFIS have the highest accuracy. As a result of the simplicity and accuracy of PR model, it can be used without any prior knowledge about MD and artificial intelligence (AI). According to the accuracy and precision of model proved by the obtained result, the solubility of H2S in ILs has been estimated. The results show that the PR method is more trustworthy than other models.
-
Experimental measurement and modeling of saturated reservoir oil viscosity
Abdolhossein Hemmati-Sarapardeh,Amir H. Mohammadi,Ahmad Ramazani S. A.,Seyed-Mohammad-Javad Majidi,Behnam Mahmoudi 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.7
A novel mathematical-based approach is proposed to develop reliable models for prediction of saturatedcrude oil viscosity in a wide range of PVT properties. A new soft computing approach, namely least square supportvector machine modeling optimized with coupled simulated annealing optimization technique, is proposed. Six modelshave been developed to predict saturated oil viscosity, which are designed in such a way that could predict saturatedoil viscosity with every available PVT parameter. The constructed models are evaluated by carrying out extensive experimentalsaturated crude oil viscosity data from Iranian oil reservoirs, which were measured using a “Rolling Ballviscometer.” To evaluate the performance and accuracy of these models, statistical and graphical error analyses wereused simultaneously. The obtained results demonstrated that the proposed models are more robust, reliable and efficientthan existing techniques for prediction of saturated crude oil viscosity.
-
Modeling the permeability of heterogeneous oil reservoirs using a robust method
Arash Kamari,Farzaneh Moeini,Mohammad-Javad Shamsoddini-Moghadam,Seyed-Ali Hosseini,Amir H. Mohammadi,Abdolhossein Hemmati-Sarapardeh 한국지질과학협의회 2016 Geosciences Journal Vol.20 No.2
Permeability as a fundamental reservoir property plays a key role in reserve estimation, numerical reservoir simulation, reservoir engineering calculations, drilling planning, and mapping reservoir quality. In heterogeneous reservoir, due to complexity, natural heterogeneity, non-uniformity, and non-linearity in parameters, prediction of permeability is not straightforward. To ease this problem, a novel mathematical robust model has been proposed to predict the permeability in heterogeneous carbonate reservoirs. To this end, a fairly new soft computing method, namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization technique was utilized. Statistical and graphical error analyses have been employed separately to evaluate the accuracy and reliability of the proposed model. Furthermore, this model performance has been compared with a newly developed multilayer perceptron artificial neural network (MLP-ANN) model. The obtained results have shown the more robustness, efficiency and reliability of the proposed CSA-LSSVM model in comparison with the developed MLP-ANN model for the prediction of permeability in heterogeneous carbonate reservoirs. Estimations were found to be within acceptable agreement with the actual field data of permeability, with a root mean square error of approximately 0.42 for CSA-LSSVM model in testing phase, and a R-squared value of 0.98. Additionally, these error parameters for MLP-ANN are 0.68 and 0.89 in testing stage, respectively.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
