Evaluation of exergy performance and onion drying properties in a multi-stage semi-industrial continuous dryer: Artificial neural networks (ANNs) and ANFIS models

Kaveh, Mohammad; Chayjan, Reza Amiri; Golpour, Iman; Poncet, Sébastien; Seirafi, Farhad; Khezri, Behrooz

doi:10.1016/j.fbp.2021.02.010

Cited by 41 publications

(34 citation statements)

References 69 publications

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“…By increasing t, all the terms will tend to zero except the first one. The effective moisture diffusion coefficient (D e f f ) can be obtained from the slope (k) of Ln (MR) vs. t using Equation ( 4) [20].…”

Section: Drying Kineticsmentioning

confidence: 99%

“…Based on Darvishi et al [6] and Jafari et al [33], the energy efficiency in the MW dryer can be calculated by Equation (20).…”

Section: Energy Efficiency and Energy Lossmentioning

confidence: 99%

“…This method is a combination of the fuzzy structures with ANN to identify the systems and predict time series. This model has several advantages, among which the ability to simulate nonlinear systems, high precision, and shorter time of model development can be mentioned [20].…”

Section: Introductionmentioning

confidence: 99%

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Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms

et al. 2021

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The study targeted towards drying of cantaloupe slices with various thicknesses in a microwave dryer. The experiments were carried out at three microwave powers of 180, 360, and 540 W and three thicknesses of 2, 4, and 6 mm for cantaloupe drying, and the weight variations were determined. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were exploited to investigate energy and exergy indices of cantaloupe drying using various afore-mentioned input parameters. The results indicated that a rise in microwave power and a decline in sample thickness can significantly decrease the specific energy consumption (SEC), energy loss, exergy loss, and improvement potential (probability level of 5%). The mean SEC, energy efficiency, energy loss, thermal efficiency, dryer efficiency, exergy efficiency, exergy loss, improvement potential, and sustainability index ranged in 10.48–25.92 MJ/kg water, 16.11–47.24%, 2.65–11.24 MJ/kg water, 7.02–36.46%, 12.36–42.70%, 11.25–38.89%, 3–12.2 MJ/kg water, 1.88–10.83 MJ/kg water, and 1.12–1.63, respectively. Based on the results, the use of higher microwave powers for drying thinner samples can improve the thermodynamic performance of the process. The ANFIS model offers a more accurate forecast of energy and exergy indices of cantaloupe drying compare to ANN model.

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Section: Drying Kineticsmentioning

confidence: 99%

“…Based on Darvishi et al [6] and Jafari et al [33], the energy efficiency in the MW dryer can be calculated by Equation (20).…”

Section: Energy Efficiency and Energy Lossmentioning

confidence: 99%

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Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms

et al. 2021

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“…Therefore, the MC must be reduced to about 11–13% using an appropriate drying process for a proper safe storage, for milling operation and following safe storing in the form of milled rice (Golpour et al, 2015; Tirawanichakul, Prachayawarakorn, Varanyanond, & Soponronnarit, 2009). The process of drying, as one of the most important operations in the food and agricultural processing industries, is within the most often utilized methods to preserve food products and improve their quality (Golpour et al, 2017; Golpour, Kaveh, Amiri Chayjan, & Guiné, 2020a, 2020b; Kaveh, Amiri Chayjan, et al, 2021; Kaveh, Golpour, et al, 2021; Guiné, Golpour, Barroca, & Kaveh, 2020). Convective drying is a very complex process that involves simultaneously unsteady heat and mass transfer.…”

Section: Introductionmentioning

confidence: 99%

“…The effective moisture diffusivity accounts for the influence of numerous input variables such as the air temperature and velocity on the mass transport occurring during drying (Chayjan Amiri, Salari, Abedi, & Sabziparvar, 2013). The minimum energy required to begin the drying operation is designated by the activation energy (Kaveh, Amiri Chayjan, et al, 2021; Kaveh, Golpour, et al, 2021). Overall, the knowledge about physical and thermal characteristics of agricultural products, for instance, the heat and mass transfer, is of prime importance to design and model the ideal convective thin layer dryer (Aghbashlo, Kianmehr, & Samimi‐Akhijahani, 2008).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the heat and mass transfer effective coefficients during the convective drying process of paddy (Oryza sativa L.)

Golpour

Guiné

Poncet

et al. 2021

J Food Process Engineering

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This work investigates important heat and mass transfer parameters of a variety of paddy named Fajr, during a thin layer convective drying. The experiments on drying are conducted at air temperatures in the domain of 30–80°C with air velocities between 0.54 and 3.27 m s−1. Mathematical equations are developed to adjust the experimental data obtained for the thin layer drying. The drying results show that the Henderson and Pabis' model satisfactorily fit all the experimental data for the convective drying behavior of paddy. The effective diffusivity of moisture transfer, calculated by the Fick's second law of diffusion, varies from 1.109 × 10−11 to 5.858 × 10−10 m2 s−1 and the highest values of the Dincer, Biot, Reynolds, and Nusselt numbers obtained are 2.383 × 105; 0.2026; 1.833 × 107; and 8,434, respectively. The obtained results demonstrate that the coefficient of mass transfer (hm) for paddy lies between 5.118 × 10−8 and 4.807 × 10−6 m s−1, while the heat transfer coefficient (hc) ranges from 811 to 2,474 W m−2 K−1. Furthermore, the values of the energies of activation obtained for moisture diffusion (Ed) and convective mass transfer (Ec) are in the range of 50.538–61.825 kJ mol−1 and 72.325–87.386 kJ mol−1, respectively. Therefore, knowledge regarding to the determination process of heat and mass transfer characteristics can help to distinguish the suitable operating conditions for saving the maximum value of energy. Practical Applications Some grains have a short shelf life that limits their commercialization as fresh products and increase postharvest losses. Drying is an interesting alternative to the development of new products of grains like paddy with added value. This research work involved the estimation of heat and mass transfer properties during convective drying by using two calculation methodologies, allowing estimating the diffusivity and the mass and heat transfer coefficients for paddy. Also, the activation energy for moisture diffusion and for convective mass transfer was determined, assuming that the temperature dependence of the diffusion coefficient and the mass transfer coefficient follows an Arrhenius type relationship. Therefore, also the study presents a simple method to greatly enhance the shelf life of wet paddy by convective drying and it can be applied for the better preservation of this product.

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Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

Jimoh,

Hashim,

Shamsudin

et al. 2024

J Sci Food Agric

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BackgroundFive computational intelligence approaches, namely: gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT), and support vector machine (SVM) were used to describe the evolution of moisture during the dehydration process of glutinous rice. The hyperparameters of the models were optimized with three strategies i.e., Bayesian optimization, grid search and random search. To understand the parameters that facilitate intelligence model adaptation to the dehydration process, global sensitivity analysis (GSA) was used to compute the impact of the input variables on the model output.ResultThe result shows that the optimum computational intelligence techniques include the 3–9‐1 topology trained with Bayesian regulation function for ANN, gaussian kernel function for SVM, matern covariance function combined with zero mean function for the GPR, boosting method for EoT, and 4 minimum leaf size for DT models. The GPR has the highest performance with R2 of 100% and 99.71% during calibration and testing of the model respectively. The GSA reveals that all the models significantly rely on the variation in time as the main factor that affects the model outputs.ConclusionTherefore, the computational intelligence models especially the GPR can be applied for an effective description of moisture evolution during small‐scale and industrial dehydration of glutinous rice.This article is protected by copyright. All rights reserved.

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Evaluation of exergy performance and onion drying properties in a multi-stage semi-industrial continuous dryer: Artificial neural networks (ANNs) and ANFIS models

Cited by 41 publications

References 69 publications

Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms

Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms

Evaluating the heat and mass transfer effective coefficients during the convective drying process of paddy (Oryza sativa L.)

Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

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