New numerical model for solubility of light alkanes in triethylene glycol

Alireza Bahadori 한국화학공학회 2007 Korean Journal of Chemical Engineering Vol.24 No.3

range of experimental data results, estimates the amount of CH4, C2H6 and C3H8 absorbed per volume of triethyleneglycol (TEG) circulated vs. the partial pressure of light alkanes and the absorber temperature. This article shows thatthe proposed numerical approach is more accurate than routine equation of states in predicting the solubility of lighthydrocarbons in TEG. This article also provides comparisons between the results of the proposed model with experi-

Applying SVM framework for modeling of CO₂ solubility in oil during CO₂ flooding

Rostami, Alireza,Arabloo, Milad,Lee, Moonyong,Bahadori, Alireza Elsevier 2018 Fuel Vol.214 No.-

<P><B>Abstract</B></P> <P>CO<SUB>2</SUB> solubility is one of the most important parameters that affects CO<SUB>2</SUB> flooding, because gas dissolution into crude oil results in oil swelling, viscosity reduction, IFT reduction, oil mobilization, and oil recovery improvement. Therefore, a better understanding of CO<SUB>2</SUB> solubility mechanisms and its influence on physical properties of crude oil are essential to any effective CO<SUB>2</SUB> flooding process. In this study, Least-Square Support Vector Machine (LSSVM) as a newly established soft computing algorithm is applied for developing a new correlative model for CO<SUB>2</SUB> solubility in both dead and live oil systems. CO<SUB>2</SUB> solubility in dead oil is basically affected by the oil saturation pressure (P<SUB>s</SUB>), oil specific gravity (<I>γ</I>), oil molecular weight (MW), and reservoir temperature (T). Moreover, the impact of bubble point pressure is considered in constructing the LSSVM model for the live oil. A number of statistical quality measures are utilized to assess and demonstrate the superior capability of the newly developed LSSVM model in comparison with the previous empirically derived correlations. The average absolute relative deviation (AARD) and coefficient of determination (R<SUP>2</SUP>) of 2.2783% and 0.9933 for the dead oil system, and 1.7432% and 0.9958 for the live oil system, respectively, verify the acceptable accuracy and efficient performance of the proposed LSSVM model over a wide range of dataset used in this study within the range of the used databank. However, the impact of CO<SUB>2</SUB> liquefaction pressure is ignored, the LSSVM model gives the best result. In conclusion, it is worth mentioning that the proposed LSSVM model can serve as an accurate correlative tool for fast and effective estimation of CO<SUB>2</SUB> solubility in both dead and live crude oils.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A new soft computing model is established for CO<SUB>2</SUB> solubility prediction in both dead and live oil systems. </LI> <LI> Several statistical parameters are utilized to demonstrate the superiority of the suggested SVM model. </LI> <LI> The performance of the developed model is compared with other literature correlations. </LI> <LI> The proposed model can serve as accurate tool for effective estimation of CO<SUB>2</SUB>-crude oil solubility. </LI> <LI> The impact of all input variables on the target values based on different techniques is determined. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

A novel modeling approach to optimize oxygen–steam ratios in coal gasification process

Arabloo, Milad,Bahadori, Alireza,Ghiasi, Mohammad M.,Lee, Moonyong,Abbas, Ali,Zendehboudi, Sohrab Elsevier 2015 Fuel Vol.153 No.-

<P><B>Abstract</B></P> <P>Coal gasification operation appears to be an essential element in the advanced energy systems, where the reaction between oxygen, steam and coal results in production of syngas (e.g., a mixture of carbon monoxide and hydrogen) under elevated pressure and temperature conditions. An efficient design for gasification process is expected if proper oxygen/steam rations are selected such that a thermal balance is established between the exothermic and endothermic reactions, leading to yield maximization of desired products in most cases. In this article, a rigorous modeling approach using support vector machine (SVM) algorithm is developed to estimate optimum oxygen–steam ratios required to balance the released heat and heat requirement in coal gasification process. An acceptable match between modeling outputs and real data is noticed so that the average absolute error is lower than 1.0%.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Support Vector Machine Algorithm is used to estimate oxygen–steam ratios in coal gasification process. </LI> <LI> The coupled simulated annealing optimization tool obtains the optimal model parameters. </LI> <LI> The model has been developed and tested using 100 series of the data. </LI> <LI> Excellent agreement between the results of model and reported data is observed. </LI> </UL> </P>

Liquid–liquid equilibrium data and correlation for quaternary systems of acetic acid + water + methyl acetate + p-xylene at 313.2 K

Gregorius Rionugroho Harvianto,김서은,강기준,Alireza Bahadori,이문용 한국공업화학회 2016 Journal of Industrial and Engineering Chemistry Vol.35 No.-

The experimental liquid–liquid equilibrium (LLE) data of the quaternary (acetic acid + water + pxylene+ methyl acetate) system was investigated at 313.2 K and atmospheric pressure. This research isaimed to examine the potential of the mixture of methyl acetate and p-xylene, which available as theremaining substances in terephthalic acid production, to be prospective extracting solvent for the aceticacid dehydration. LLE phase diagrams at different ratio of p-xylene to methyl acetate were presented forthis quaternary system. The results showed that an enlargement of the LLE two-phase region occurredwith increasing p-xylene to methyl acetate mass ratio in the initial solvent phase. The distributioncoefficient and selectivity for the extraction of acetic acid were also obtained to evaluate the capability ofsolvent. LLE data were sufficiently correlated by Othmer-Tobias and Bachman equations. Theexperimental results were used to obtain binary interaction parameters as correlated by the nonrandomtwo liquid (NRTL) and universal quasi-chemical theory (UNIQUAC) equation models. The rootmean square deviations (RMSD) values as low as 0.0119 and 0.0128 were calculated for NRTL andUNIQUAC, respectively; indicating excellent results for both models were suitable for the determinationof LLE data of this quaternary system.

Monotonic behavior of C and L shaped angle shear connectors within steel-concrete composite beams: an experimental investigation

Mahdi Shariati,Farzad Tahmasbi,Peyman Mehrabi,Alireza Bahadori,Ali Toghroli 국제구조공학회 2020 Steel and Composite Structures, An International J Vol.35 No.2

Shear connectors are essential elements in the design of steel-concrete composite systems. These connectors are utilized to prevent the occurrence of potential slips at the interface of steel and concrete. The two types of shear connectors which have been recently employed in construction projects are C- and L-shaped connectors. In the current study, the behavior of C and L-shaped angle shear connectors is investigated experimentally. For this purpose, eight push-out tests were composed and subjected to monotonic loading. The load-slip curves and failure modes have been determined. Also, the shear strength of the connectors has been compared with previously developed relationships. Two failure modes of shear connectors were observed: 1) concrete crushing–splitting and 2) shear connector fracture. It was found that the L-shaped connectors have less shear strength compared to C-shaped connectors, and decreasing the angle leg size increases the shear strength of the C-shaped connectors, but decreases the relative ductility and strength of L-shaped connectors.

Optimization of modified single mixed refrigerant process of natural gas liquefaction using multivariate Coggin’s algorithm combined with process knowledge

Pham, Tram Ngoc,Khan, Mohd Shariq,Minh, Le Quang,Husmil, Yuli Amalia,Bahadori, Alireza,Lee, Sanggyu,Lee, Moonyong Elsevier 2016 Journal of natural gas science and engineering Vol.33 No.-

<P><B>Abstract</B></P> <P>The optimization of a mixed refrigerant liquefaction process is a challenge because of its non-linear characteristics with stringent multiple process constraints. This study proposes a novel hybrid approach for the optimization of a newly developed, modified single mixed refrigerant process of natural gas liquefaction targeted for offshore applications. This contribution focuses on interpreting the geometric pattern of a plot of the temperature difference between the hot and cold composite curves in a cryogenic heat exchanger to understand the profound effects of the flow rates of the individual refrigerant components and the operating pressure on the liquefaction efficiency. From this, an effective method to generate a proper initial approach temperature profile was developed to ensure robust convergence of the main optimization step. An enhanced coordinate descent methodology was implemented in the main optimization procedure to accelerate the optimization of the modified single mixed refrigerant liquefaction process. The proposed knowledge-inspired hybrid optimization approach showed a robust convergence on determining the optimal design condition. The total energy requirement for natural gas liquefaction cycle was reduced by 21.9% compared to the base case. The proposed methodology can be extended directly to solve optimization problems for other mixed refrigerant based natural gas liquefaction processes.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Analysis of the TDCC plot of the MSMR process. </LI> <LI> Effects of decision variables on the performance of NG liquefaction. </LI> <LI> An enhanced coordinate descent methodology to accelerate the optimization. </LI> <LI> A good initial point and search sequence to ensure robust convergence. </LI> <LI> The compression energy for NG liquefaction was reduced significantly. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures

Mahdi Shariati,Mohammad Saeed Mafipour,Peyman Mehrabi,Yousef Zandi,Davoud Dehghani,Alireza Bahadori,Ali Shariati,Nguyen Thoi Trung,Musab N.A. Salih,Shek Poi-Ngian 국제구조공학회 2019 Steel and Composite Structures, An International J Vol.33 No.3

This study is aimed to predict the behaviour of channel shear connectors in composite floor systems at different temperatures. For this purpose, a soft computing approach is adopted. Two novel intelligence methods, including an Extreme Learning Machine (ELM) and a Genetic Programming (GP), are developed. In order to generate the required data for the intelligence methods, several push-out tests were conducted on various channel connectors at different temperatures. The dimension of the channel connectors, temperature, and slip are considered as the inputs of the models, and the strength of the connector is predicted as the output. Next, the performance of the ELM and GP is evaluated by developing an Artificial Neural Network (ANN). Finally, the performance of the ELM, GP, and ANN is compared with each other. Results show that ELM is capable of achieving superior performance indices in comparison with GP and ANN in the case of load prediction. Also, it is found that ELM is not only a very fast algorithm but also a more reliable model.

Correlation Between Nitrogen Dioxide as an Air Pollution Indicator and Breast Cancer: a Systematic Review and Meta-Analysis

Keramatinia, Aliasghar,Hassanipour, Soheil,Nazarzadeh, Milad,Wurtz, Morten,Monfared, Ayad Bahadori,Khayyamzadeh, Maryam,Bidel, Zeinab,Mhrvar, Narges,Mosavi-Jarrahi, Alireza Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.1

Background: The aim of this systematic review was to study the relationship between exposure to nitrogen dioxide ($NO_2$) in the ambient air and breast cancer incidence. Materials and Methods: A systematic review was performed based on the MOOSE guideline for review of observational studies. We searched five online databases (PubMed, Science Direct, Google Scholar, EBSCO, and Scopus) from their conception to June 2014. A pooled estimate of the correlation between $NO_2$ exposure and breast cancer incidence was calculated using Pearson's correlation coefficient. Results: A total of 654 titles were retrieved in the initial search of the databases. Further refinement and screening of the retrieved studies produced a total of five studies from four countries. The studies included three ecological studies (aggregate level) and two individual based studies (one prospective cohort and the other one a case-control study). The ecological studies were pooled and the meta-analysis of correlation coefficient without z transformation showed a pooled estimate of r = 0.89 with 95% CI of 0.84 to 0.95. Using z transformation, the pooled r was 1.38 with 95%CI of 1.11 to 1.59. No significant heterogeneity between studies was observed. Following a sensitivity analysis and the removal of each study from pooled analysis we did not see any significant change in the pooled estimate. Conclusions: It was concluded that there is a tendency toward a weak association between exposure to $NO_2$ in ambient air and breast cancer at the individual level and a significant association at the aggregate level.

A neural network-based local rainfall prediction system using meteorological data on the Internet: A case study using data from the Japan Meteorological Agency

Kashiwao, Tomoaki,Nakayama, Koichi,Ando, Shin,Ikeda, Kenji,Lee, Moonyong,Bahadori, Alireza Elsevier 2017 Applied soft computing Vol.56 No.-

<P>In this study, we develop and test a local rainfall (precipitation) prediction system based on artificial neural networks (ANNs). Our system can automatically obtain meteorological data used for rainfall prediction from the Internet. Meteorological data from equipment installed at a local point is also shared among users in our system. The final goal of the study was the practical use of 'big data' on the Internet as well as the sharing of data among users for accurate rainfall prediction. We predicted local rainfall in regions of Japan using data from the Japan Meteorological Agency (JMA). As neural network (NN) models for the system, we used a multi-layer perceptron (MLP) with a hybrid algorithm composed of back-propagation (BP) and random optimization (RO) methods, and radial basis function network (RBFN) with a least squares method (LSM), and compared the prediction performance of the two models. Precipitation (total amount of rainfall above 0.5 mm between 12: 00 and 24: 00 JST (Japan standard time)) at Matsuyama, Sapporo, and Naha in 2012 was predicted by NNs using meteorological data for each city from 2011. The volume of precipitation was also predicted (total amount above 1.0 mm between 17: 00 and 24: 00 JST) at 16 points in Japan and compared with predictions by the JMA in order to verify the universality of the proposed system. The experimental results showed that precipitation in Japan can be predicted by the proposed method, and that the prediction performance of the MLP model was superior to that of the RBFN model for the rainfall prediction problem. However, the results were not better than those generated by the JMA. Finally, heavy rainfall (above 10 mm/h) in summer (Jun.-Sep.) afternoons (12: 00-24: 00 JST) in Tokyo in 2011 and 2012 was predicted using data for Tokyo between 2000 and 2010. The results showed that the volume of precipitation could be accurately predicted and the caching rate of heavy rainfall was high. This suggests that the proposed system can predict unexpected local heavy rainfalls as 'guerrilla rainstorms.' (C) 2017 Elsevier B.V. All rights reserved.</P>

Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and <i>N</i>-methylpyrrolidone using intelligent models

Tatar, Afshin,Barati, Ali,Yarahmadi, Ali,Najafi, Adel,Lee, Moonyong,Bahadori, Alireza Elsevier 2016 International journal of greenhouse gas control Vol.47 No.-

<P><B>Abstract</B></P> <P>Clear knowledge about the solubility of acid gases such as CO<SUB>2</SUB> in different solvents at different states is very important, especially for carbon capture from flue gases. This study highlights the application of artificial intelligence in prediction of carbon dioxide solubility in a mix solvent of methyldiethanolamine and <I>N</I>-methylpyrrolidone at wide range of temperature and pressure.</P> <P>The input data of the models were temperature, pressure, and saturation pressure and the output parameter was the solubility of CO<SUB>2</SUB>. Different intelligent approaches such as MLP-ANN, GA-RBF, CSA-LSSVM, Hybrid-ANFIS, PSO-ANFIS, and CMIS were developed and the reliability of models was investigated through different graphical and statistical methods. Result showed that the developed models are accurate and predictive for estimation of experimental solubility data. However, the CMIS approach exhibited better results compared to other intelligent approaches. Results of this study showed that intelligent based algorithms are powerful alternatives for time-consuming and difficult experimental processes of solubility measurement.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Different models are utilized to predict carbon dioxide solubility in mix solvent of methyldiethanolamine and <I>N</I>-methylpyrrolidone. </LI> <LI> The CMIS approach exhibited better results compared to other intelligent approaches. </LI> <LI> The reliability of models was investigated through different graphical and statistical methods. </LI> <LI> The models show excellent agreement with experimental data. </LI> </UL> </P>