Artificial neural network modelling of supercritical fluid CO2 extraction of polyunsaturated fatty acids from common carp (Cyprinus carpio L.) viscera

Kuvendžiev, Stefan; Lisichkov, Kiril; Zeković, Zoran; Marinkovski, Mirko

doi:10.1016/j.supflu.2014.06.007

Cited by 35 publications

(17 citation statements)

References 27 publications

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“…Higher pressure presented a positive influence on the yield of PUFA, and the temperature, in low pressure conditions, provides decreasing in the PUFA yield [30].…”

Section: Cobia Liver Oilmentioning

confidence: 89%

Supercritical Extraction of Cobia (Rachycentron canadum) Liver Oil as a New Source of Squalene

Santos¹,

Takahashi²,

Verde³

et al. 2016

fph

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Cobia is a fish cultivated worldwide and there are few studies on the potential of their waste, such as the liver, it's rich in oil and active compounds. The objective of this study was to extract cobia liver oil (CLO) using supercritical CO 2 under moderate temperature conditions (50, 60 and 70 °C, 250 bar) and characterize the oil (physico-chemical analysis, fatty acids profile and squalene content). The results showed that CLO is rich in MUFA and presented squalene, suggesting that this matrix can be applied in the production of high nutritional value oil.

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“…Higher pressure presented a positive influence on the yield of PUFA, and the temperature, in low pressure conditions, provides decreasing in the PUFA yield [30].…”

Section: Cobia Liver Oilmentioning

confidence: 89%

Supercritical Extraction of Cobia (Rachycentron canadum) Liver Oil as a New Source of Squalene

Santos¹,

Takahashi²,

Verde³

et al. 2016

fph

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“…As basic characteristics, networks have adaptive learning, self-organizing capacity and a robust structure with parallel distribution (layers). They are efficient in learning and generalization, and in addition to being tolerant to outliers, they are also able to model different variables and their non-linear relationships, as well as enabling quantitative and qualitative variable modeling (Kuvendziev et al 2014, Haykin 2001).…”

Section: Introductionmentioning

confidence: 99%

Using artificial neural networks in estimating wood resistance

Miguel¹,

Melo

Serenini

et al. 2018

Maderas, Cienc. tecnol.

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The purpose of this research was to evaluate the potential of Artificial Neural Networks in estimating the properties of wood resistance. In order to do so, a hybrid of eucalyptus (Eucalyptus urograndis) planted in the Northern Region of the State of Mato Grosso was selected and ten trees were collected. Then, four samples of each tree were removed, totaling 40 samples, which were later subjected to non-destructive testing of apparent density, ultrasonic wave propagation velocity, dynamic modulus of elasticity obtained by ultrasound, and Janka hardness. These properties were used as estimators of resistance and compressive strength parallel to fibers, and hardness. Multilayer Perceptron networks were also employed, training 100 of them for each of the evaluated parameters. The obtained results indicated that the use of Artificial Neural Networks is an efficient tool for predicting wood resistance.

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“…ANNs are powerful mathematical tools which are capable of providing a reliable model of a complex and nonlinear system between inputs and outputs through mimicking the biological neural network behavior. To date, ANNs have seen enormous applications in various engineering fields [22][23][24][25][26][27][28][29][30][31][32][33][34]. This paper presents the use of an ANN to accurately estimate CO 2 -brine IFT based on experimental data acquired from previous literature reports.…”

Section: Introductionmentioning

confidence: 99%