Modelling and optimization of energy consumption in essential oil extraction processes

Meziane, Ilhem Ait Amer; Mäızi, Nadia; Abatzoglou, Nicolas; Benyoussef, El-Hadi

doi:10.1016/j.fbp.2019.11.018

Cited by 30 publications

(6 citation statements)

References 66 publications

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“…With new changes occurring worldwide to ensure sustainable development, most biobased processes are suffering significant transformation to achieve better extraction process efficiencies and reduce its environmental impact. [28][29][30] Further studies will be conducted to examine the Fig. 2 Graphical summary of the RSM results for the optimization of the two main variables involved (X 1 and X 2 ) in the extraction of ergosterol from Pleurotus eryngii and Pleurotus ostreatus mushroom species for the three response value formats assessed (Y 1 , yield in %; Y 2 , mg E per g R; and Y 3 mg E per 100 g M dw).…”

Section: The Optimum Conditions Of Hae Process Experimental Verificatmentioning

confidence: 99%

Optimization of ergosterol extraction from Pleurotus mushrooms using response surface methodology

et al. 2020

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Section: The Optimum Conditions Of Hae Process Experimental Verificatmentioning

confidence: 99%

Optimization of ergosterol extraction from Pleurotus mushrooms using response surface methodology

et al. 2020

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“…The modeling approach adopted in this research allowed, in addition to estimating energy consumption, its changes during the extraction process. The results showed that there was a good agreement between the predicted and the experimental values during the extraction process (Ait Amer Meziane et al, 2020). Although mathematical models have many advantages in identifying and predicting the behavior of parameters in different conditions, generally, they are not very accurate in predicting complex behaviors (Zandi et al, 2022).…”

Section: Introductionmentioning

confidence: 84%

“…The success of mathematical models in the kinetic modeling of the extraction of D‐limonene (Khandare et al, 2021) and musk willow essential oil (Zandi et al, 2022) via distillation ultrasound assisted hydro‐distillation, extraction of Yarrow essential oil by hydro‐distillation (Karami et al, 2022b) and extraction of sage EO by microwave assisted hydro‐distillation (Radivojac et al, 2020) were reported by researchers. Ait Amer Meziane et al (2020) modeled and optimized energy consumption during the process of EO extraction using conventional and microwave methods. The modeling approach adopted in this research allowed, in addition to estimating energy consumption, its changes during the extraction process.…”

Section: Introductionmentioning

confidence: 99%

Modeling of microwave‐ and ultrasound‐ohmic‐assisted hydro‐distillation extraction of the Pimpinella anisum essential oil

Jafari

Zandi

Ganjloo

2023

J Food Process Engineering

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First‐order, second‐order, sigmoid, and absorption models were used to predict the extraction yield (EY) of anise essential oil (AEO). Sigmoid model was able to predict the EY with R2 0.9852 and 0.9869 respectively for ohmic‐assisted hydro‐distillation methods with ultrasound and microwave pretreatments. It was found that among the different prediction models, adaptive neuro‐fuzzy inference system (ANFIS) has the highest accuracy for EY estimating. So that a network with the help of Mamdani's maximum‐minimum method and triangular membership function was able to predict the EY in both pretreatments with R2 above 0.99. The results of artificial neural network (ANN) modeling showed that the multilayer perceptron network with feed‐forward back propagation structure, Levenberg–Marquardt training algorithm, and with configurations 3‐16‐7‐1 and 3‐14‐5‐1 was able to have the best performance in EY predicting of ultrasound and microwave pretreatments, respectively. The results emphasize the successful use of models, especially dynamic models, in predicting the kinetics of essential oil extraction.Practical applicationsModeling helps to understand the basic process and is suitable for better control of the process and increasing its efficiency. Mathematical models, as a relatively simple method, lead to a better understanding of the mechanism and factors affecting extraction processes. Although mathematical models have many advantages in identifying and predicting the behavior of parameters in different conditions, generally, they are not very accurate in predicting complex behaviors. Dynamic modeling including artificial neural networks (ANNs) and fuzzy logic has recently received much attention due to its ability to learn complex processes and predict nonlinear relationships. The results emphasize the successful use of models, especially dynamic models, in predicting the kinetics of essential oil extraction.

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“…Quadratic polynomials were used to interpolate the experimental data of both the dielectric constant and loss factor of cypress biomass versus the dry-basis moisture content. Equations (6) and 7show the obtained polynomials for both magnitudes:…”

Section: Permittivity Dependence On Dry-basis Moisture Contentmentioning

confidence: 99%

Permittivity Measurements for Cypress and Rockrose Biomass Versus Temperature, Density, and Moisture Content

Pérez-Campos

Fayos-Fernández

Guerrero

et al. 2020

Sensors

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Permittivity of materials is of utmost importance for microwave applicators’ design and to predict high-frequency dielectric heating of materials. In the case of aromatic plant biomass, however, there are few data that help researchers design microwave applicators for microwave-assisted extraction. In this work, the permittivity of cypress and rockrose biomass samples were measured versus temperature, density, and moisture content. A resonant technique based on a coaxial bi-reentrant microwave cavity was employed to obtain the complex permittivity of biomass samples as a function of those magnitudes around the 2.45 GHz ISM frequency. The obtained measurements show that large variations for permittivity values can be found with moisture content and density changes for both cypress and rockrose biomass. Temperature also has effects in a lesser degree, although it has an important influence on the cypress biomass loss factor. Polynomial expressions fitting the experimental data were provided in order to facilitate the estimation of intermediate values, which were not explicitly arranged in this work. As a general trend, the permittivity of cypress and rockrose biomass increases with increasing values of moisture content and density, whereas the biomass loss factor increases when temperature rises.

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Modelling and optimization of energy consumption in essential oil extraction processes

Cited by 30 publications

References 66 publications

Optimization of ergosterol extraction from Pleurotus mushrooms using response surface methodology

Optimization of ergosterol extraction from Pleurotus mushrooms using response surface methodology

Modeling of microwave‐ and ultrasound‐ohmic‐assisted hydro‐distillation extraction of the Pimpinella anisum essential oil

Permittivity Measurements for Cypress and Rockrose Biomass Versus Temperature, Density, and Moisture Content

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