Predicting ionic liquid based aqueous biphasic systems with artificial neural networks

Shahriari, Shahla; Shahriari, Sh.

doi:10.1016/j.molliq.2014.04.030

Cited by 7 publications

(5 citation statements)

References 45 publications

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“…The R 2 value of approximately 1 indicates the strong correlation between the experimental data, and the rheological model (Shahriari, Atashrouz, & Pazuki, 2018). The RMSD is used as a criterion to represent the deviation of the regression model results from the experimental data and the least values, suggesting the less deviation between the results of the rheological model from the experimental data (Shahriari & Shahriari, 2014). In this research, regarding the rheological models detailed in Table 5, it can be inferred that the Cross model can fit the experimental data with high accuracy (the R 2 value of the Cross model is the highest value [0.998] while RMSD is the lowest value [0.0116]).…”

Section: Resultsmentioning

confidence: 99%

“…The R 2 value of approximately 1 indicates the (Shahriari, Atashrouz, & Pazuki, 2018). The RMSD is used as a criterion to represent the deviation of the regression model results from the experimental data and the least values, suggesting the less deviation between the results of the rheological model from the experimental data (Shahriari & Shahriari, 2014). In this research, regarding the rheological models detailed in…”

Section: Rheological Modelsmentioning

confidence: 95%

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Effect of pressure homogenization and modified starch on the viscosity of ketchup: Experimental and modeling

Baeghbali

Shahriari

Pazuki

2021

J Food Process Engineering

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The simultaneous determination of the suitable level of homogenization pressure and modified starch is an important issue, which affects the quality of the industrial ketchup. In this study, ketchup was produced at different pressures of homogenization (0, 5, 8, 11, 14, 17, and 20 MPa) and different weight percentages of modified starch (0, 0.1, 0.4, 0.7, and 1 g/100 g). The viscosity of ketchup was measured at different temperatures. The results showed that the selected ketchup, which contains 1 g/100 g of modified starch and homogenized at a temperature of 25°C and a pressure of 14 MPa, has the optimum viscosity and consistency. After that, to evaluate the effect of homogenization on the physical properties of selected ketchup, additional tests such as the dynamic dispersion scale, the optical microscopy and the Fourier transform infrared spectrophotometer were done. The results indicated a smaller particle size, a more uniform, and a continuous structure of selected ketchup compared with the control sample. Consequently, several different rheological models were investigated for evaluating the rheological behavior of selected ketchup. The statistical measures indicated that the Cross model could predict the rheological behavior of selected ketchup well (R2 = 0.998). Practical Applications Viscosity and syneresis can have strong effects on ketchup quality. Also, reliable rheological data are necessary for designing and optimization of ketchup processing equipment such as pumps, piping, heat exchangers, evaporators, sterilizers, filters, and mixers. In this research, the simultaneous effect of the modified starch and pressure homogenization on the rheological data of ketchup was evaluated. The purpose of this study is to help the ketchup industry by determining the optimum level of homogenization pressure and the optimal dose of modified starch when used simultaneously. Paying attention to this subject is vital for loss prevention in the process and machine depreciation. Furthermore, the rheological behavior which is important during storage, processing, and the transport of ketchup in the industry is determined.

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Section: Resultsmentioning

confidence: 99%

Section: Rheological Modelsmentioning

confidence: 95%

Effect of pressure homogenization and modified starch on the viscosity of ketchup: Experimental and modeling

Baeghbali

Shahriari

Pazuki

2021

J Food Process Engineering

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“…The input and output of the neuron are specified with x and y , respectively; n stands for the number of the inputs traveling to the neuron; W ij denotes the weight that makes the connection between the neurons i and j ; and b j signifies the bias associated to neuron j .…”

Section: Modeling Phasementioning

confidence: 99%

“…At the beginning, the magnitudes of weight and bias are chosen randomly. An optimization algorithm is employed to effectively train the network through obtaining the optimal model parameters until the synaptic weights are adjusted and the network simulates the input/output mapping correctly .…”

Section: Modeling Phasementioning

confidence: 99%

Evolution of tar ball aggregates in Caspian Sea: Implications of connectionist tools linked with image analysis

Amin

Zendehboudi

Mohamadi

2018

Env Prog and Sustain Energy

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Intensive petroleum activities in the Caspian Sea lead to a considerable oil pollution in the various parts of this region. Tar balls stranded on the south coast of the Caspian Sea evidently cause (and accelerate) this serious environmental threat. In this study, a poloxamine copolymeric surfactant is used to agglomerate tar balls in a system containing the solvent, antisolvent, and Caspian Sea tar balls. This research work is conducted to determine the cumulative size distribution of tar balls in the presence of a poloxamine copolymer. The evolution of tar ball aggregates is experimentally and theoretically investigated and the steady‐state size distribution of the tar balls is estimated by Back Propagation Artificial Neural Network (ANN‐BP) and a new evolutionary algorithm, called Imperialist Competitive Algorithm (ICA). The average size of tar ball aggregates increases with time and reaches a maximum value. It then declines and eventually approaches a steady‐state condition so that an almost constant size for the particles is attained. The statistical analysis shows a good agreement between the predicted values (obtained by the neural network combined with ICA, known as ANN‐ICA) and the real data for the steady‐state size distribution of the aggregates. This research study can considerably help to further understand evolution of tar balls (that hit the beaches) and consequently to find effective and economical ways for tar balls removal from the beaches. © 2018 American Institute of Chemical Engineers Environ Prog, 37: 1901–1907, 2018

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“…They have also been used for aqueous biphasic systems based on polymers-polyethylene glycols (Kan and Lee 1996) and ethylene oxide propylene oxide copolymer (Leong et al 2018) with potassium phosphate. Similarly, Shahriari and Shahriari (2014) employed an artificial neural network with the batch backpropagation (BBP) learning algorithm for a three-layer feed-forward network to model the formation of the ABS based on 1-butyl-3-methylimidazolium trifluoromethanesulfonate ionic liquid with a broad range of salts. A good agreement between the experimental and predicted values was achieved, with the squared correlation coefficient (r 2 ) ranging from 0.9624 to 0.9978 for the testing data set.…”

Section: Introductionmentioning

confidence: 99%

Aqueous Biphasic Systems Based on Ethyl Lactate: Molecular Interactions and Modelling

2021

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Predicting ionic liquid based aqueous biphasic systems with artificial neural networks

Cited by 7 publications

References 45 publications

Effect of pressure homogenization and modified starch on the viscosity of ketchup: Experimental and modeling

Effect of pressure homogenization and modified starch on the viscosity of ketchup: Experimental and modeling

Evolution of tar ball aggregates in Caspian Sea: Implications of connectionist tools linked with image analysis

Aqueous Biphasic Systems Based on Ethyl Lactate: Molecular Interactions and Modelling

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