Modeling interfacial tension in N2/n-alkane systems using corresponding state theory: Application to gas injection processes

Ameli, Forough; Hemmati-Sarapardeh, Abdolhossein; Schaffie, Mahin; Husein, Maen M.; Shamshirband, Shahaboddin

doi:10.1016/j.fuel.2018.02.067

Cited by 50 publications

(25 citation statements)

References 92 publications

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“…Generally, the Levenberg-Marquardt (LM) algorithm [40,41] is implemented to optimize weights and biases of the network. In most cases ANN-LM models does not involve local minimums and even with an inappropriate initial guess the global minimum is obtained [42]. In this regard we used this algorithm for optimization of the ANN model.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…In addition to error functions which were considered in the previous section, determining of applicability domain has also a great importance to evaluate the model performance. The Leverage approach as a popular method has been used in a number of researches for the purpose of outlier detection and to identify applicability domain of the models [20,27,42,[58][59][60].…”

Section: Investigating Applicability Domain Of the Proposed Modelsmentioning

confidence: 99%

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Mathematical modeling of ethylene polymerization over advanced multisite catalysts: an artificial intelligence approach

et al. 2020

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Recent developments in multisite catalysts based on metallocenes and post-metallocenes attracted the attention of researchers and industrial petrochemical companies due to the production of high-performance polymeric materials which generally are not achievable based on Ziegler-Natta catalysts. In this study, with the aim of predicting the average molecular weight of produced polyethylene and activity of ethylene polymerization using multisite catalysts, robust precise models based on artificial neural networks are developed. The average error for the prediction of the average molecular weight and activity are 3.76% and 5.89%, respectively. The Leverage method was used to check the reliability of the proposed model and the quality of experimental data which have been used for model development. The results showed that just a few data points are outside of the applicability domain of the developed models, confirming that both developed models and their predictions are statistically correct. Comparison of the artificial neural network models with other artificial intelligence approaches including support vector machine and group method of data handling type neural networks illustrates the better performance and robustness of the proposed models. The results of this study promise that neural networks can be used as reliable models with reasonable accuracy to estimate the performance of ethylene polymerization over this type of new metallocene/post-metallocene multisite catalysts.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Investigating Applicability Domain Of the Proposed Modelsmentioning

confidence: 99%

Mathematical modeling of ethylene polymerization over advanced multisite catalysts: an artificial intelligence approach

et al. 2020

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“…A miscellaneous predicting prototypical conjoining neural networks with fuzzy pattern-recognition was developed in view of the fuzziness in the perception of analogous basins, in which initiation tasks in the ANN model are improved with categorization (Ameli, Hemmati-Sarapardeh, Schaffie, Husein, & Shamshirband, 2018;Chau, 2017;Mosavi & Rabczuk, 2017b;Mosavi, Rabczuk, & Varkonyi-Koczy, 2017). Such a fuzzy pattern appreciation initiation task was presented addicted to a hybrid neural network (HNN) model to represent uncertainties of the river flow problematic (Chen, Chau, & Busari, 2015).…”

Section: Extreme Learning Machinementioning

confidence: 99%

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

Ghazvinei

Darvishi

Mosavi

et al. 2018

Engineering Applications of Computational Fluid Mechanics

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Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production datadriven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R 2 ) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results. ARTICLE HISTORY

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“…This estimation can be made from data or correlations of the interfacial tension of alkanes contained in the crude oil and the injected gases. 1 , 12 , 19 , 20 …”

Section: Introductionmentioning

confidence: 99%

Pressure–Surface Tension–Temperature Equation of State for n-Alkanes

Mulero

Cachadiña

Cardona

et al. 2022

Ind. Eng. Chem. Res.

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Herein, the geometric similitude concept is applied to propose a cubic equation that relates surface tension, saturation pressure, and temperature for n -alkanes. The input properties for each fluid are the molecular mass, pressure, temperature, and compressibility factor at the critical point. The model is applied to temperatures below 0.93· T c (critical point temperature). A total of 2429 surface tension values have been selected for 32 n -alkanes. The parameters of the model have been obtained with a fit of the surface tension values for 19 pure n -alkanes that are randomly chosen. Then, it is tested for the other 13 pure n -alkanes and used to predict the surface tension for 11 binary and 4 ternary mixtures. These predictions are compared with the reported experimental data. For pure n -alkanes, the overall absolute average deviation is 2.4%, including the correlation and testing sets. No additional adjustable coefficients are used for mixtures, yielding an overall absolute average deviation of 2.98% for the binary systems and 7.97% for the ternary ones. The results show that the model is accurate enough for predictions and that the highest deviations are due to the lack of agreement in the values of surface tension of pure fluids obtained from different sources.

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Modeling interfacial tension in N2/n-alkane systems using corresponding state theory: Application to gas injection processes

Cited by 50 publications

References 92 publications

Mathematical modeling of ethylene polymerization over advanced multisite catalysts: an artificial intelligence approach

Mathematical modeling of ethylene polymerization over advanced multisite catalysts: an artificial intelligence approach

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

Pressure–Surface Tension–Temperature Equation of State for n-Alkanes

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