Accurate model based on artificial intelligence for prediction of carbon dioxide solubility in aqueous tetra‐<i>n</i>‐butylammonium bromide solutions

Hoseinpour, Seyed-Ahmad; Barati-Harooni, Ali; Nadali, Payam; Mohebbi, Armin; Najafi‐Marghmaleki, Adel; Tatar, Afshin; Bahadori, Alireza

doi:10.1002/cem.2956

Cited by 5 publications

(3 citation statements)

References 58 publications

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“…A RBF neural network has the ability to straightforwardly convert the data into a multi‐dimensional space and treat arbitrary scattered data . This methodology has a wide application in different fields and sciences such as chemical engineering, petroleum engineering, mathematical science, nanotechnology, etc . In contrast to MLP which may have two or more hidden layers, all RBF neural network models have only one hidden layer.…”

Section: Model Developmentmentioning

confidence: 99%

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Hemmati‐Sarapardeh¹,

Dabir²,

Ahmadi³

et al. 2018

Can J Chem Eng

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Asphaltene precipitation causes different problems in the oil industry. In this study, a large data bank was used to model asphaltene precipitation titration data as a function of temperature, type of solvent, and solvent to crude oil dilution ratio. Three multilayer perceptron (MLP) neural networks and three radial basis function (RBF) neural networks were developed to estimate asphaltene precipitation data. The MLP models were optimized with scaled conjugate gradient (SCG), Levenberg‐Marquardt (LM), and Bayesian regularization (BR) algorithms and the RBF models were optimized with the particle swarm optimization (PSO) algorithm, imperialist competitive algorithm (ICA), and genetic algorithm (GA). All of the proposed models show an acceptable degree of accuracy and have an average absolute percent relative error (AAPRE) less than 4 %. Afterwards, three of the best models including MLP‐LM, MLP‐BR, and RBF‐GA were combined and a committee machine intelligent system (CMIS) was designed, which offers a higher accuracy compared to the other intelligent models. Aside from these intelligent models, the group method of data handling (GMDH) was used to develop an explicit and simple expression for estimating asphaltene precipitation. The proposed CMIS and GMDH models were compared to models developed based on scaling theory and the results show that the proposed models in this study outperform pre‐existing models. The validity and accuracy of the CMIS and GMDH models were proven by statistical and graphical techniques. Finally, a sensitivity analysis suggested that the solvent to crude oil dilution ratio has the largest effect on asphaltene precipitation.

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Section: Model Developmentmentioning

confidence: 99%

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Hemmati‐Sarapardeh¹,

Dabir²,

Ahmadi³

et al. 2018

Can J Chem Eng

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“…Artificial neural networks (ANNs) have been employed for predicting draught requirement of tillage implements under sandy clay loam soil conditions [10], prognosticating the energy efficiency indices of driven wheels [11], and modeling the relationship between travel reduction-to-net traction ratio and tractive efficiency [12]. Although an ANN is a powerful tool for solving stochastic and complex problems and can produce highly accurate prediction models, more sophisticated (hybrid) soft computing techniques have also been employed [13]. An ANN-genetic algorithm (GA) has been used to model the power of agricultural tractors as a function of the wheel load, slip, and speed [14] and to model the dynamic characteristics of a tractor on sloping terrain [15].…”

Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Techniques for the Analysis of Tractive Properties of a Low-Power Agricultural Tractor under Various Soil Conditions

2020

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Considering the fuel consumption and soil compaction, optimization of the performance of tractors is crucial for modern agricultural practices. The tractive performance is influenced by many factors, making it difficult to be modeled. In this work, the traction force and tractive efficiency of a low-power tractor, as affected by soil coefficient, vertical load, horizontal deformation, soil compaction, and soil moisture, were studied. The optimal work of a tractor is a compromise between the maximum traction force and the maximum tractive efficiency. Optimizing these factors is complex and requires accurate models. To this end, the performances of soft computing approaches, including neural networks, genetic algorithms, and adaptive network fuzzy inference system, were evaluated. The optimal performance was realized by neural networks trained by backpropagation as well as backpropagation combined with a genetic algorithm, with a coefficient of determination of 0.955 for the traction force and 0.954 for the tractive efficiency. Based on models with the best accuracy, a sensitivity analysis was performed. The results showed that the traction performance is mainly influenced by the soil type; nevertheless, the vertical load and soil moisture also exhibited a relatively strong influence.

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“…The output of their prediction model was however focused onto a thermodynamic parameter, ie, the hydrate dissociation pressure. Bahadori and co‐workers applied several algorithms (neural networks, fuzzy inference systems, gene expression programming, etc.) to predict the solubility of CO 2 in semi‐clathrate hydrate‐forming solution of tetra‐ n ‐butylammonium bromide, showing that the proposed models were able to fit experimental solubility data as found in the literature.…”

Section: Introductionmentioning

confidence: 99%

Chemoinformatic design of amphiphilic molecules for methane hydrate inhibition

Profio

Canale

Marvulli

et al. 2018

Journal of Chemometrics

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Cationic surfactants and other low molecular weight compounds are known to inhibit nucleation and agglomeration of methane hydrates. In particular, tetralkylammonium salts are kinetic hydrate inhibitors; ie, they reduce the rate of hydrate formation. This work relates to the in‐silico determination of structural features of molecules modulating methane hydrate formation, as found experimentally, and the prediction of novel structures to be tested as candidate inhibitors. Experimental data for each molecule are the amount of absorbed methane. By inserting these numerical values into a chemoinformatic model, it was possible to find a mutual correlation between structural features and inhibition properties. A maximum amount of information is extracted from the structural features and experimental variables, and a model is generated to explain the relationship therebetween. Chemometric analysis was performed by using the software package Volsurf+ with the aim of finding a primary correlation between surfactant structures and their properties. Experimental parameters (pressure, temperature, and concentration) were further processed through an optimization procedure. A careful study of the chemometric analysis responses and the numerical descriptors of tested surfactants allowed to define the features of a good inhibitor, as far as the amount of absorbed gas is concerned. An external prediction is finally made to project external compounds, whose structures and critical micellar concentration are known, in a statistical model, to predict the inhibition properties of a particular molecule in advance of synthesis and testing. This method allowed to find novel amphiphilic molecules for testing as candidate inhibitors in flow‐assurance.

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Accurate model based on artificial intelligence for prediction of carbon dioxide solubility in aqueous tetra‐n‐butylammonium bromide solutions

Cited by 5 publications

References 58 publications

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Application of Soft Computing Techniques for the Analysis of Tractive Properties of a Low-Power Agricultural Tractor under Various Soil Conditions

Chemoinformatic design of amphiphilic molecules for methane hydrate inhibition

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