Optimization of an air drying process for Artemisia absinthium leaves using response surface and artificial neural network models

Karimi, Fatemeh; Rafiee, Shahin; Taheri‐Garavand, Amin; Karimi, Mohammad

doi:10.1016/j.jtice.2011.04.005

Cited by 86 publications

(60 citation statements)

References 30 publications

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“…Also they are used to determine the significant effect of each factor on the response factor by developing mathematical models and to find out the optimal conditions. RSM technique is used by many researchers ( [4], [18], [22], [33], [35], [37], [42], [44] & [51]) to optimize the drying effect of various dryers like fluidized bed dryers, on different agricultural products like Artemisia absinthium leaves, coriandrum sativum leaves, coroba slices, kefir powder, ganodermalucidium slices, green peas, olive leaves and soya bean, etc.…”

Section: Modelling and Optimization Techniquesmentioning

confidence: 99%

Recent developments in drying of food products

Valarmathi

Purushothaman

Sekar³

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Section: Modelling and Optimization Techniquesmentioning

confidence: 99%

Recent developments in drying of food products

Valarmathi

Purushothaman

Sekar³

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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“…Their optimal topology for predicting the energy and exergy in a fluidized bed dryer was determined by using response surface methodology integrated with a genetic algorithm in both static and recurrent modes . Response surface methodology was used in combination with ExA for determining the optimal process conditions for the drying of olive leaves (Erbay and Icier, 2009b) and herbal leaves (Karimi et al, 2012), and for identifying the main factors that affect the performance of thin layer drying of pomegranate arils (Nikbakht et al, 2013).…”

Section: Current Challenges and Future Trends In Designing Sustainablmentioning

confidence: 99%

The use of exergetic indicators in the food industry – A review

Zisopoulos

Rossier-Miranda²,

Goot

et al. 2015

Critical Reviews in Food Science and Nutrition

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Assessment of sustainability will become more relevant for the food industry in the years to come. Analysis based on exergy, including the use of exergetic indicators and Grassmann diagrams, is a useful tool for the quantitative and qualitative assessment of the efficiency of industrial food chains. In this paper, we review the methodology of exergy analysis and the exergetic indicators that are most appropriate for use in the food industry. The challenges of applying exergy analysis in industrial food chains and the specific features of food processes are also discussed.

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“…Freire et al [15] discuss various applications involving control and sensing techniques for spouted bed drying including adaptive and neural based algorithms. Hybrid neural network and first principle models, also known as grey-box models as opposed to black box where no model form is assumed or white box models that are purely theoretical, can likewise be found in the literature [16] [21]. Figure 1 shows a typical structure of a three layers neural network.…”

Section: Introductionmentioning

confidence: 99%

“…The computational capability of ANNs results from their ability to learn from examples through iteration, without requiring a prior knowledge of relationships between the variables under investigation [21] [22]. This technique is based on a trial and error fitting procedure until reaching a good agreement to the output parameters in a specified accuracy range [20] [21].…”

Section: Introductionmentioning

confidence: 99%

“…This technique is based on a trial and error fitting procedure until reaching a good agreement to the output parameters in a specified accuracy range [20] [21]. The network design may be adapted or modified to meet the specific requirements of each particular case by varying the number of neurons and neuron layers, which results in many different possible structures that allow the construction of very flexible models [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Neural Based Modeling Approach for Drying Kinetics Analysis of Mint Branches and Their Fractions (Leaves and Stems)

Rosanova¹,

Maia²,

Freire³

et al. 2017

ACES

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This work is aimed at investigating regular mint (Mentha × villosa) drying behavior and assessing how the heterogeneous composition of plants affects their drying kinetics. Drying kinetics and sorption isotherms were evaluated for whole branches and their fractions (leaves and stems). Stems and leaves were characterized by measurement of dimensions, apparent density and initial moisture content. The moisture sorption isotherms were obtained under temperatures of 30˚C, 40˚C and 50˚C for branches, stems and leaves and the data were fitted to the GAB model. Mint branches and their fractions were oven dried at temperatures from 40˚C to 70˚C and were obtained kinetic curves for each part. Water sorption patterns were similar for leaves and stems and the GAB model described well the sorption behavior of both materials. At a constant temperature, the drying rates were higher for leaves in comparison to stems and the differences increased as the temperature was raised. Therefore, depending on drying conditions, the moisture distribution in dried branches might be significantly different. Since the leaves constitute the major fraction in branches, the drying rates of branches were closer to those of leaves. The kinetic curves were fitted to a diffusion model based on an analytical solution of Fick's second diffusion law and to an empirical model based on artificial neural network (ANN). The results showed that the model based on the ANN predicted the drying kinetics of the different parts better than the diffusive model. A single network was built to describe the kinetic behavior of branches and fractions in the whole range of temperatures investigated. The diffusive model based on fitting effective diffusivity did not provide good predictions of moisture content, probably because neither the dependence of effective diffusivity on the moisture content nor the heterogenei- 155ty and shrinking of static beds were considered.

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Optimization of an air drying process for Artemisia absinthium leaves using response surface and artificial neural network models

Cited by 86 publications

References 30 publications

Recent developments in drying of food products

Recent developments in drying of food products

The use of exergetic indicators in the food industry – A review

A Neural Based Modeling Approach for Drying Kinetics Analysis of Mint Branches and Their Fractions (Leaves and Stems)

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