Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants

Esmaeili-Faraj, Seyyed Hamid; Vaferi, Behzad; Bolhasani, Akbar; Karamian, Soroush; Hosseini, Shahin; Rashedi, Reza

doi:10.1002/ceat.202000442

Cited by 15 publications

(4 citation statements)

References 36 publications

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“…It is possible to place several neurons in some successive layers to create different topologies of the ANN. The MLPNN [ 32 ], CFFNN [ 33 ], RNN [ 34 ], radial basis function neural networks, and general regression neural networks are the most well-known ANN types in this regard. Our literature review confirmed that the first three aforementioned models often provide acceptable accuracy for regression-based problems.…”

Section: Methodsmentioning

confidence: 99%

Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies

et al. 2022

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Biodegradable polymers have recently found significant applications in pharmaceutics processing and drug release/delivery. Composites based on poly (L-lactic acid) (PLLA) have been suggested to enhance the crystallization rate and relative crystallinity of pure PLLA polymers. Despite the large amount of experimental research that has taken place to date, the theoretical aspects of relative crystallinity have not been comprehensively investigated. Therefore, this research uses machine learning methods to estimate the relative crystallinity of biodegradable PLLA/PGA (polyglycolide) composites. Six different artificial intelligent classes were employed to estimate the relative crystallinity of PLLA/PGA polymer composites as a function of crystallization time, temperature, and PGA content. Cumulatively, 1510 machine learning topologies, including 200 multilayer perceptron neural networks, 200 cascade feedforward neural networks (CFFNN), 160 recurrent neural networks, 800 adaptive neuro-fuzzy inference systems, and 150 least-squares support vector regressions, were developed, and their prediction accuracy compared. The modeling results show that a single hidden layer CFFNN with 9 neurons is the most accurate method for estimating 431 experimentally measured datasets. This model predicts an experimental database with an average absolute percentage difference of 8.84%, root mean squared errors of 4.67%, and correlation coefficient (R2) of 0.999008. The modeling results and relevancy studies show that relative crystallinity increases based on the PGA content and crystallization time. Furthermore, the effect of temperature on relative crystallinity is too complex to be easily explained.

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Section: Methodsmentioning

confidence: 99%

Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies

et al. 2022

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“…This neuron-based machine learning method is the most widely-used tool as either estimator 65 , 66 or classifier 67 . The working process of the artificial neural network is handled by a combination of linear (LPart) and non-linear (NLPart) operations conducted by the neuron as follows 68 : w , b , and are weight and bias coefficients and activation function, respectively.…”

Section: Estimation Scenarios For Density Of Deep Eutectic Solventsmentioning

confidence: 99%

Estimating the density of deep eutectic solvents applying supervised machine learning techniques

Abdollahzadeh

Khosravi

Masjidi

et al. 2022

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Deep eutectic solvents (DES) are recently synthesized to cover limitations of conventional solvents. These green solvents have wide ranges of potential usages in real-life applications. Precise measuring or accurate estimating thermophysical properties of DESs is a prerequisite for their successful applications. Density is likely the most crucial affecting characteristic on the solvation ability of DESs. This study utilizes seven machine learning techniques to estimate the density of 149 deep eutectic solvents. The density is anticipated as a function of temperature, critical pressure and temperature, and acentric factor. The LSSVR (least-squares support vector regression) presents the highest accuracy among 1530 constructed intelligent estimators. The LSSVR predicts 1239 densities with the mean absolute percentage error (MAPE) of 0.26% and R2 = 0.99798. Comparing the LSSVR and four empirical correlations revealed that the earlier possesses the highest accuracy level. The prediction accuracy of the LSSVR (i.e., MAPE = 0. 26%) is 74.5% better than the best-obtained results by the empirical correlations (i.e., MAPE = 1.02%).

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“…A feedforward MLPNN has three layers of input, interiors, and output 44 – 46 . MLPNN benefits from a unique training approach known as the backpropagation, and the utilized activation functions in this method are non-linear 47 . The three most common types of activation functions are specified as follows 37 , 48 : …”

Section: Theoretical Backgroundmentioning

confidence: 99%

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Abdi

Hadipoor

Esmaeili-Faraj

et al. 2022

Sci Rep

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Absorption has always been an attractive process for removing hydrogen sulfide (H2S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H2S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H2S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H2S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H2S solubility in ILs and possesses R2, RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H2S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM+ and Tf2N-, i.e., [OMIM][Tf2N] ionic liquid, is the best choice for H2S capture among the investigated absorbents. The H2S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.

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Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants

Cited by 15 publications

References 36 publications

Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies

Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies

Estimating the density of deep eutectic solvents applying supervised machine learning techniques

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

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