Prediction of energy and exergy of mushroom slices drying in hot air impingement dryer by artificial neural network

Liu, Zi-Liang; Bai, Junwen; Wang, Shuxi; Meng, Jian-Sheng; Wang, Hui; Yu, Xian-Long; Gao, Zhen-Jiang; Xiao, Hong‐Wei

doi:10.1080/07373937.2019.1607873

Cited by 55 publications

(38 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ex =̇E Liu et al (2019) TA B L E 3 Formulas used for determining Exergy loss and Exergy loss of convective dryer…”

Section: Equation Equation Numbermentioning

confidence: 99%

“…Artificial neural networks and ANFIS have been successful in estimations in natural processes. Liu et al (2019) have surveyed ANNs application to predict E u , EUR, exergy loss, and exergy efficiency mushroom slices in HA dryer. Compared to linear regression models, they do not necessitate the placement of prediction values around the mean and thus reflect the real data variability (Jahanbakhshi & Salehi, 2019;Kaveh, Jahanbakhshi, Abbaspour-Gilandeh, Taghinezhad, & Moghimi, 2018;Movagharnejad & Nikzad, 2007;Shekarchizadeh, Tikani, & Kadivar, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS

Abbaspour‐Gilandeh

Jahanbakhshi

Kaveh

2019

Food Science & Nutrition

View full text Add to dashboard Cite

This study aimed to predict the drying kinetics, energy utilization (E u ), energy utilization ratio (EUR), exergy loss, and exergy efficiency of quince slice in a hot air (HA) dryer using artificial neural networks and ANFIS. The experiments were performed at air temperatures of 50, 60, and 70°C and air velocities of 0.6, 1.2, and 1.8 m/s. The thermal parameters were determined using thermodynamic relations. Increasing air temperature and air velocity increased the effective moisture diffusivity (D eff ), E u , EUR, exergy efficiency, and exergy loss. The value of the D eff was varied from 4.19 × 10 -10 to 1.18 × 10 -9 m 2 /s. The highest value E u , EUR, and exergy loss and exergy efficiency were calculated 0.0694 kJ/s, 0.882, 0.044 kJ/s, and 0.879, respectively. Midilli et al. model, ANNs, and ANFIS model, with a determination coefficient (R 2 ) of .9992, .9993, and .9997, provided the best performance for predicting the moisture ratio of quince fruit. Also, the ANFIS model, in comparison with the artificial neural networks model, was better able to predict E u , EUR, exergy efficiency, and exergy loss, with R 2 of .9989, .9988, .9986, and .9978, respectively. K E Y W O R D S adaptive neuro-fuzzy inference system, artificial neural networks, drying, quince, thermodynamic parameters | 595 ABBASPOUR-GILANDEH Et AL.focusing on energy and exergy analysis is very important (Lingayat, Chandramohan, & Raju, 2018;Yogendrasasidhar & Setty, 2018).Şevik, Aktaş, Dolgun, Arslan, and Tuncer (2019) analyzed the exergy and energy in the process of drying mint and apple slices in a solar and solar-infrared. The results indicated that the loss of exergy and exergy efficiency increases by increasing the air temperature. Exergy efficiency for mint in solar dryer and solar-infrared was 69.35% and 59. 07%, respectively. Akpinar, Midilli, and Bicer (2006) analyzed the energy and exergy in pumpkin. They reported that the pumpkin dried within the time range of 5.66-12 hr with a loss of exergy from 0 to 1.165 kJ/s. The maximum exergy of the system input was 2.198 kJ/s. Also, with increased exergy loss, the energy used in the solar dryer increased. Karthikeyan and Murugavelh (2018) studied the energy and exergy required to dry turmeric in a mixed-mode forced convection solar tunnel dryer and concluded that the loss of exergy and energy utilization ratio and its efficiency was increased with increasing temperature.

show abstract

“…Ex =̇E Liu et al (2019) TA B L E 3 Formulas used for determining Exergy loss and Exergy loss of convective dryer…”

Section: Equation Equation Numbermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS

Abbaspour‐Gilandeh

Jahanbakhshi

Kaveh

2019

Food Science & Nutrition

View full text Add to dashboard Cite

show abstract

“…A seasonal character of production and a high moisture content of fruits make them suitable for processing. Drying can be used to reduce the moisture content, inhibit the growth of microorganisms, and prolong the shelf life of fruits (Liu et al 2019a). Due to the waxy surface layer which is a barrier to heat and mass transfer processes, hot air drying may take up to 160 hours and negatively affect the physical properties of dried cranberries ).…”

Section: A Styrofoam Containermentioning

confidence: 99%

Combined Hot Air and Microwave-Vacuum Drying of Cranberries: Effects of Pretreatments and Pulsed Vacuum Osmotic Dehydration on Drying Kinetics and Physicochemical Properties

Liu

Staniszewska

Zielińska

et al. 2020

Food Bioprocess Technol

Self Cite

View full text Add to dashboard Cite

The aim of the study was to evaluate the effect of sonication (S), microwave-vacuum (MWV), convective freezing (F), cryogenic freezing (N), and their combinations, as well as pulsed vacuum osmotic dehydration (PVOD) on the drying kinetics, bioactive compounds, texture and color of whole cranberries during combined hot air convective drying, and microwave-vacuum drying (HACD+MWVD). Drying of berries took from 20 to 493 min. Drying rate was enhanced by 23% and drying time of non-osmotically dehydrated fruits was shortened by 33% using F treatment, while MWV decreased moisture content before drying by 68% and shortened the drying time of PVOD berries by 96%. Generally, total phenolic (TP) content increased during processing, total flavonoids (TF), and total monomeric anthocyanins (TMA) contents decreased, while the values of ferric reducing antioxidant power (FRAP) of dried fruits depended on the initial pretreatment. F and HACD+MWVD yielded fruits of the highest L* (33.8 ± 0.7), a* (25.2 ± 1.0), and b* (7.3 ± 0.6), inflated oval shape, and a small amount of wrinkles on the surface. PVOD and HACD+MWVD resulted in flat and wrinkled fruits.

show abstract

“…They have been used to model the drying of fruits and vegetables (Chaurasia et al, ; Erenturk & Erenturk, ). ANNs have been used to model and predict the energy and exergy of mushroom during air impingement drying (Liu et al, ). Neural Network‐based extreme learning machine was used to model the effect of high‐humidity hot air impingement blanching and the different drying parameters like air temperature and air velocity on the quality of broccoli florets and drying characteristics (Liu et al, ).…”

Section: Introductionmentioning

confidence: 99%

Foam mat drying of papaya using microwaves: Machine learning modeling

Qadri

Osama

Srivastava

2020

J Food Process Engineering

View full text Add to dashboard Cite

The aim of this article is to study the microwave-assisted foam mat drying of papaya to form papaya powder. The process of foam mat drying of papaya using microwaves was modeled by machine learning approaches like artificial neural network (ANN), support vector regression (SVR), and Gaussian process regression (GPR). Effect of microwave power (480-640 W), inlet air temperature (40-50 C), and thickness of foam (2-4 mm) on the rate of drying were studied. The performance of the models was evaluated on the basis of different performance matrices including root mean square error (RMSE), coefficient of determination (R 2 ), model predictive error, and Chi-square (χ 2 ). The microwave heating of the papaya foam reduced the drying time manifold. All three machine learning approaches were able to predict the drying process efficiently.SVR showed the best performance (R 2 = 0.96; RMSE = 0.03) followed by GPR (R 2 = 0.92; RMSE = 0.04) and ANN (R 2 = 0.91; RMSE = 0.04). SVR-based model was simulated to predict the effect of power, temperature, and thickness on drying rate.Machine learning approaches can be efficiently used for modeling and microwaveassisted foam mat drying. SVR-based model proves to be a good alternative of ANN.Practical Applications: Foam mat drying is the method of dehydrating for heatsensitive and viscous materials which cannot be dried by other conventional methods. It is cost-effective, simple, and provides high product quality. The use of microwave-assisted drying decreases the drying time manifold. Modeling of the drying process is significant for its scale-up to industrial scale. Machine learning techniques have the capability of learning the hidden factors involved in the process and thus provide better predictions as compared with statistical regression methods. In this study, three machine learning methods (artificial neural network [ANN], support vector regression [SVR], and Gaussian process regression [GPR]) were compared for their efficiency in modeling the foam-mat drying of papaya pulp using microwaves.SVR showed the best performance as compared with ANN and GPR.

show abstract

Prediction of energy and exergy of mushroom slices drying in hot air impingement dryer by artificial neural network

Cited by 55 publications

References 48 publications

Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS

Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS

Combined Hot Air and Microwave-Vacuum Drying of Cranberries: Effects of Pretreatments and Pulsed Vacuum Osmotic Dehydration on Drying Kinetics and Physicochemical Properties

Foam mat drying of papaya using microwaves: Machine learning modeling

Contact Info

Product

Resources

About