Microwave vacuum drying of dragon fruit slice: Artificial neural network modelling, genetic algorithm optimization, and kinetics study

Raj, G. V. S. Bhagya; Dash, Kshirod Kumar

doi:10.1016/j.compag.2020.105814

Cited by 43 publications

(23 citation statements)

References 62 publications

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“…Moreover, microwave energy penetrates the cantaloupe samples and generates heat by inducing polarity in the water molecules, hence improving drying and uniformity of heat and water distribution in the sample [28]. Similar results have been reported for drying rice paddy [33], chamomile [30], dragon fruit slicess [45], and apple [52]. Different letters for the same segment represent statistically significant differences at a confidence level of 95%.…”

Section: Dryer Efficiency and Thermal Efficiencysupporting

confidence: 70%

“…As observable in Table 2, a rise in the Mw power enhanced the value of D eff which can be assigned to the increment of the heat energy which declined the viscosity of the water present in the samples and hence increased its activity and accelerated the evaporation [29]. Sharabiani et al [39] and Raj and Dash [45] also reported similar results in drying apple and dragon fruit slices using an MW dryer. Moreover, the statistical analysis indicated that at a given MW power, the sample thickness significantly affected the effective diffusion coefficient (p < 0.05); however, the increase in the thickness from 2 to 6 mm elevated the diffusion coefficient, which can be attributed to the surface hardness of the samples as surface hardening occurs more rapidly in thinner samples while the evaporation rate of thinner samples is far higher [46].…”

Section: Effective Moisture Diffusion Coefficientmentioning

confidence: 64%

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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: Dryer Efficiency and Thermal Efficiencysupporting

confidence: 70%

Section: Effective Moisture Diffusion Coefficientmentioning

confidence: 64%

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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“…Nowadays, artificial neural network (ANN) is one of the famous approaches that can be applied as a potential alternative to mathematical thin‐layer models in the drying kinetic. The major superiorities of ANN are the easy description of nonlinear and complex systems, parallel processing capability, and ability to work with incomplete knowledge (Bai, Xiao, Ma, & Zhou, 2018; Bhagya Raj & Dash, 2020; Liu et al, 2019). Also, the newest comprehensive discussions about the capability and robustness of ANN for modeling the drying process were done by Khan, Sablani, Joardder, and Karim (2020) and Chen et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Real‐time evaluation of artificial neural network‐developed model of banana slice kinetics in microwave‐hot air dryer

Sabzevari

Behroozi‐Khazaei

Darvishi

2021

J Food Process Engineering

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The required microwave power input and moisture content prediction in microwave‐hot air dryer are challenging. In this study, two artificial neural networks (ANN) were investigated for modeling of moisture content of banana slices and required microwave power input. The experiments were done in five levels of microwave power density (MPD) (4, 5, 6, 7, and 8 W/g) with fixed mode and two levels (6 and 8 W/g) with variable mode at 40°C hot air temperature. The initial MPD, mode, and time were used as input variables in both ANN models. The ANN with 3–5–5–1 structure had best performance for MC modeling with 0.0148 of RMSE and 0.9998 of R for testing data. Also, the ANN with 3–3–1 structure could predict the microwave power with 14.013 of RMSE and 0.9929 of R for testing data. Real‐time verification of developed models showed that developed ANN models could predict microwave power and MC with fixed MPD with good reliability and confidence for online drying control but did not reasonably satisfy for variable MPD. Also, the appearance quality of the banana slices was monitored by machine vision. The results showed that the percentage of banana charring at the fixed MPDs of 6, 7, and 8 is equal to 12%, 24%, and 29%, respectively, whereas no burns were observed at the other power density levels. Practical applications The main benefit of microwave drying is volumetric and high‐efficiency heating. But the temperature of the product at the end of drying process increased and brunt the product. Using the variable microwave power program is one of the famous methods for temperature control to prevent scrunching and burning the product. But powerful models for predicting the moisture content and microwave power during the drying process are not available. In this study, the drying kinetic of banana slices and input microwave power in the hybrid microwave‐hot air dryer under constant and variable microwave power was investigated and real‐time evaluating of the developed models for dryer control was examined. The results expressed the real‐time evaluation of the developed model had great accuracy and reliability and can be used for microwave dryer control.

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“…H. undatus , also known as pitaya, belongs to the Cactaceae family, characteristically oval or oblong, and has white pulp with tiny black seeds (Gunasena, Pushpakumara, & Kariyawasam, 2020). The pulp of dragon fruit is sweet in taste, a rich source of vitamin C and contains minerals such as magnesium, potassium, and calcium that boost immunity (Bhagya Raj & Dash, 2020; Cai, Sun, & Corke, 2003). The fruit pulp can be used to fortify food to improve the nutritional value (Maigoda, Sulaeman, Setiawan, & Wibawan, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…The fruit pulp can be used to fortify food to improve the nutritional value (Maigoda, Sulaeman, Setiawan, & Wibawan, 2016). The pulp of the dragon fruit deteriorates rapidly due to high moisture (89% moisture wb) and is considered a highly perishable commodity (Bhagya Raj & Dash, 2020; Jalgaonkar et al). Insufficient storage facilities and poor handling are accountable for the spoilage of one‐third of agricultural produces like fresh fruits and vegetables throughout the world (Hasan Masud, Karim, Ananno, & Ahmed, 2020).…”

Section: Introductionmentioning

confidence: 99%

Heat transfer analysis of convective and microwave drying of dragon fruit

Raj

Dash

2021

J Food Process Engineering

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The purpose of this study was to develop a predictive three‐dimensional analytical model for predicting the temperature profile during microwave and convective drying of dragon fruit. A combined electromagnetic (Maxwell's equation) and heat transfer model was used for modeling of microwave drying. This heat transfer modeling is applicable to describe the thermal dissipation during the microwave and convective drying of agricultural produces. In this process, the dragon fruit cube of 15 mm was dried at a microwave power of 200, 400, and 600 W during the microwave drying process and hot air temperature of 60°C during the convective drying process. During microwave drying, the core or center temperature was maximum compared with the temperature at the surface of the dragon fruit cube. The predicted temperature at the center of the dragon fruit cube exposed to the microwave power of 200, 400, and 600 W for 60 s of drying time was 34.67, 44.34, and 54.02°C, respectively. In convective drying, the temperature at the edges was higher than the temperature at the center point of the dragon fruit cube. During convective drying, the fruit sample attained the maximum temperature of 60°C after being exposed to hot air for 8 min. The RMSE and χ2 values between experimental and model projected values were less than 0.988 and 0.029 in microwave drying and less than 0.891 and 0.018 in convective drying, indicating that the model projected values were in good agreement with the experimental values. Practical Applications The importance of thermal processes in deciding the safety and quality of food products is emergent. This model can predict food product temperature distribution during the hot air drying and microwave drying of Dragon fruit cubes. The economy of the drying process is often influenced by the nature and operation of these processes and hence modeling of the drying process is a crucial factor for the industry. These results can be used to quantify heat and moisture distribution, as well as to monitor the drying process of fruits and vegetables, saving energy and time.

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Microwave vacuum drying of dragon fruit slice: Artificial neural network modelling, genetic algorithm optimization, and kinetics study

Cited by 43 publications

References 62 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

Real‐time evaluation of artificial neural network‐developed model of banana slice kinetics in microwave‐hot air dryer

Heat transfer analysis of convective and microwave drying of dragon fruit

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