Fuzzy logic application to drying kinetics modelling

Váquiro, Henry Alexander; Bon, J.; Díez, José Luis

doi:10.3182/20080706-5-kr-1001.00372

Cited by 6 publications

(5 citation statements)

References 11 publications

(8 reference statements)

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“…On the other hand, the ANN model outperformed the ANFIS model in predicting the MR value. Similar results were obtained using the Takagi-Sugeno fuzzy model on mango slices to estimate the effective diffusivity (Vaquiro et al 2008). Ganjeh et al (2013) also reported that the combination of fuzzy logic and neural networks is a suitable and reliable method to model and predict the drying kinetics of onions and similar products.…”

Section: Performance and Prediction Using Anfis And Annsupporting

confidence: 63%

Modeling the Moisture Content and Drying Rate of Zucchini (Cucurbita pepo L.) in a Solar Hybrid Dryer Using ANN and ANFIS Methods

Bulus,

Moralar,

Celen

2023

Philipp Agric Scientist

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Estimating product drying kinetics is critical to obtain the best drying process without compromising product quality and necessitates the development of numerical drying models. This research aims to compare the prediction models developed using artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS), two popular machine learning approaches in the recent years. Zucchini slices were chosen as samples and dried in a solar-assisted microwave belt dryer at 0.245 m/min belt speed and microwave powers of 0.7, 1, and 1.4 kW. On the data set obtained by computing the moisture content and drying rate values, prediction models were developed using ANN and ANFIS approaches. These models were evaluated using the coefficient of determination, mean absolute percent error, and root mean square error data. The ANFIS-based prediction model outperformed the ANN model in terms of drying rate performance, but the ANN model outperformed the ANFIS model in terms of moisture content values. Results showed that both methods established can be utilized to estimate zucchini slices.

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Section: Performance and Prediction Using Anfis And Annsupporting

confidence: 63%

Modeling the Moisture Content and Drying Rate of Zucchini (Cucurbita pepo L.) in a Solar Hybrid Dryer Using ANN and ANFIS Methods

Bulus,

Moralar,

Celen

2023

Philipp Agric Scientist

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“…The goodness of the fit for each model to the experimental data was evaluated using the root mean square error (RMSE) [28] and the percentage of explained variance (VAR, %) [29], according to the following equations:…”

Section: Discussionmentioning

confidence: 99%

Diffusional analysis and textural properties of coated strawberries during osmotic dehydration treatment

Gamboa‐Santos

Vasco

Campañone

2021

JBR

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BACKGROUND: The use of polysaccharide based edible coatings as a barrier against the solute entry of high caloric constituents, such as sucrose, is a suitable strategy to develop pro-healthy osmo-dehydrated products. OBJECTIVE: This work aims to study the effect of a calcium alginate film over the drying kinetics of strawberries during an osmotic dehydration (OD) treatment at 60°Brix, 4 h, 40°C (sucrose). Also, textural parameters were evaluated. METHODS: It was determined the changes in some selected physical properties, such as: moisture, total solid contents, one-dimensional shrinkage and texture, at 30 min steps during OD treatment. Besides, water and solid diffusion coefficients ( D e W and D e TS , respectively) were calculated by using the analytical solution of Fick’s equation. RESULTS: Diffusion coefficients for water and solids showed significant differences due to coating presence as an evidence of the barrier effect. Coated strawberry samples presented several changes after 2 h of OD treatment at the assayed conditions for most of the parameters evaluated. CONCLUSIONS: Alginate coating at the tested conditions acts as a barrier to sucrose influx and to water outflux until 2 and 3 h of OD treatment, respectively. Besides, a good textural quality for osmotically-dehydrated strawberries was obtained.

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“…R, correlation coefficient; RMSE, root mean square error; SSE, sum of squares error. -López et al (2005), Vaquiro et al (2008), Perrot et al (2006) and other researchers have also mentioned high correlation between the obtained data from Mamdani fuzzy rules and the measured ones during drying processes of different products. …”

Section: Application Of Fuzzy Logic For Onion Dryingmentioning

confidence: 99%

“…Generally, it is a revolutionary method for some complex processes which suffer from lacking a comprehensive and effective modeling technique and require some faster approximating alternatives (Vaquiro et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Mathematical, Fuzzy Logic and Artificial Neural Network Modeling Techniques to Predict Drying Kinetics of Onion

Jafari

Ganje

Dehnad

et al. 2015

Journal of Food Processing and Preservation

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In this study, the influence of drying temperature (40, 50, 60C) and airflow velocity (2 and 3 m/s) on drying onion was evaluated by a custom designed fluidized bed dryer equipped with a heat pump dehumidifier. A comparative study was performed among nonlinear regression techniques, fuzzy logic and artificial neural networks to estimate the dynamic drying behavior of onion. Among nine mathematical models, approximation of diffusion with R 2 = 0.9999 and root mean square error = 0.004157 showed the best fit with experimental data. Fuzzy logic tool in MATLAB with Mamdani model in the form of If-Then rules along with triangular membership function used for simulation, interpolation and obtaining a theoretical increase in experimental moisture ratios were used. Feedforwardbackpropagation neural system with application of Levenberg-Marquardt training algorithm, hyperbolic tangent sigmoid transfer function, training cycle of 1,000 epoch and 2-5-1 topology was determined as the best neural model in terms of statistical indices. PRACTICAL APPLICATIONSForecasting kinetics of food drying by application of precise mathematical and dynamic modeling techniques as well as preparing particular patterns for describing such drying behaviors of diverse food products seem unavoidable for processing factories to optimize the quality of their dried food products and reduce operational costs. For this purpose, in this study, we applied different temperatures and airflow velocities to dry onion by a custom designed fluidized bed dryer equipped with a heat pump dehumidifier, predicting its drying behavior by regression, fuzzy logic and artificial neural network techniques and comparing accuracy of those models. Also, this paper compares prediction patterns of drying onion by fluidized bed drying with those of other food products dried by various drying methods. The results of this study will be helpful for all researchers and producers who want to know more about the nuances of impacts of different fluidized bed drying variables on the patterns of heat and mass transfer in fruits and vegetables.

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Fuzzy logic application to drying kinetics modelling

Cited by 6 publications

References 11 publications

Modeling the Moisture Content and Drying Rate of Zucchini (Cucurbita pepo L.) in a Solar Hybrid Dryer Using ANN and ANFIS Methods

Modeling the Moisture Content and Drying Rate of Zucchini (Cucurbita pepo L.) in a Solar Hybrid Dryer Using ANN and ANFIS Methods

Diffusional analysis and textural properties of coated strawberries during osmotic dehydration treatment

Mathematical, Fuzzy Logic and Artificial Neural Network Modeling Techniques to Predict Drying Kinetics of Onion

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