Energetic and exergetic performance analysis and modeling of drying kinetics of kiwi slices

Qualitative, energy and environmental aspects of microwave drying of pre-treated apple slices

In the present research, response parameters such as specific energy consumption (SEC), thermal efficiency (TE), energy efficiency (EF), drying time (DT), greenhouse gas (GHG) emission (such as CO2 and NOx), and quality features (color variation and shrinkage) were modeled by response surface methodology (RSM) for apple slices dried in a microwave dryer under ultrasonication (30 ℃-10 min) and blanching (80 °C-2 min) pretreatments. Also, RSM was applied to optimize two independent parameters including microwave power and sample thickness in the levels 100, 200, and 300 W and 2, 4, and 6 mm, respectively. The results indicated the significant influence (P < 0.01) of the independent parameters on the response parameters. The vales of SEC, DT, GHG emission, shrinkage, and color difference were linearly decreased with the declining sample thickness and increasing microwave power, while the energy and thermal efficiencies were increased by a quadratic equation. The use of ultrasonication and blanching pretreatments decreased the SEC, GHG emissions, and DT; while improving the quality of the samples as compared to the non-treated slices. The optimization results showed the optimal drying times (31.55, 82.19, and 50.55 min), SEC (3.42, 10.07, and 4.37 MJ/kg), CO2 with natural gas (1539.75, 1518.75, and 4585 g), CO2 with gas oil (3662.53, 2099.25, 2721.25 g), NOx with natural gas (10.094, 9.956, and 12.906 g), and NOx with gas oil (12.934, 12.758, and 16.538 g) at a microwave power of 300 W and sample thickness of 2 mm with desirability of 0.921, 0.935, and 0.916 for control samples, ultrasonicated, and blanched, respectively.

Enhancing Hot Air Drying Efficiency through Electrostatic Field-Ultrasonic Coupling Pretreatment

The drying of compact and biologically active materials presents significant challenges. In this study, we propose using electrostatic field-ultrasonic coupling pretreatment to enhance the drying efficiency of ginkgo fruits. We designed and constructed an experimental device to investigate the effects of ultrasonic power, pretreatment time, hot air drying temperature, and electrostatic field voltage on the moisture content of the fruits. We used the response surface methodology to identify optimal process conditions and further explored the kinetic model for the moisture content of the fruits under the pretreatment. The results showed that the optimal process parameters for electrostatic-ultrasound pretreatment and the drying of ginkgo fruits were: an electrostatic field voltage of 11.252 kV, an ultrasound power of 590.074 W, a pretreatment time of 32.799 min, and a hot air drying temperature of 85 °C. Under the optimized process conditions, the correlation between the moisture content of ginkgo fruits and the two-term drying kinetics model was the highest. After electrostatic-ultrasound coupling pretreatment, the drying rate of ginkgo fruits was significantly improved during hot air drying.

Factors affecting energy efficiency of microwave drying of foods: an updated understanding

Microwave drying (MWD) is an efficient dielectric drying method in food, with advantages such as volumetric heating, fast drying, safety, and good product quality. As a key indicator of a dryer's market value, energy efficiency is of concern to sellers and dryer manufacturers. This paper systematically reviewed the quantification methods and influencing factors of energy efficiency of microwave drying in food application from different perspectives. Mechanisms and possible improvements of these factors are highlighted. Future trends in improving the energy efficiency of MWD are proposed. Energy consumption of MWD depends on a variety of factors such as equipment structure, drying conditions (microwave power, frequency, temperature, and air velocity), material properties, and combined/hybrid drying technologies. The drying system can be effectively improved if these parameters are adjusted appropriately and taking the processing cost into consideration. Although a good product can be obtained by pretreatment or combined/hybrid drying method, it may consume more energy. Future research should develop artificial intelligence, renewable energy, and computational fluid dynamics technology to pave the way for large-scale application of MWD and reduce energy consumption.

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

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.

Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling

Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) were studied. In the analysis of the performance of our approach on moisture ratio (MR) of apple slices, artificial neural networks (ANNs) was used to provide with a background for further discussion and evaluation. In order to evaluate the models mentioned in the literature, the Midilli et al. model was proper for dehydrating of apple slices in both MD and CD. The MD drying technology enhanced the drying rate when compared with CD drying significantly. Effective diffusivity (D_eff) of moisture in CD drying (1.95 × 10^-7-4.09 × 10^-7 m²/s) was found to be lower than that observed in MD (2.94 × 10^-7-8.21 × 10^-7 m²/s). The activation energy (Ea) values of CD drying and MD drying were 122.28-125 kJ/mol and 14.01-15.03 W/g respectively. The MD had the lowest specific energy consumption (SEC) as compared to CD drying methods. According to ANN results, the best R² values for prediction of MR in CD and MD were 0.9993 and 0.9991, respectively.

Thermodynamic and Quality Performance Studies for Drying Kiwi in Hybrid Hot Air-Infrared Drying with Ultrasound Pretreatment

The present study examined the effect of ultrasonic pretreatment at three time the levels of 10, 20 and 30 min on some thermodynamic (effective moisture diffusivity coefficient(Deff), drying time, specific energy consumption (SEC), energy efficiency, drying efficiency, and thermal efficiency) and physical (color and shrinkage) properties of kiwifruit under hybrid hot air-infrared(HAI) dryer at different temperatures (50, 60 and 70 °C) and different thicknesses (4, 6 and 8 mm). A total of 11 mathematical models were applied to represent the moisture ratio (MR) during the drying of kiwifruit. The fitting of MR mathematical models to experimental data demonstrated that the logistic model can satisfactorily describe the MR curve of dried kiwifruit with a correlation coefficient (R2) of 0.9997, root mean square error (RMSE) of 0.0177 and chi-square (χ2) of 0.0007. The observed Deff of dried samples ranged from 3.09 × 10−10 to 2.26 × 10−9 m2/s. The lowest SEC, color changes and shrinkage were obtained as 36.57 kWh/kg, 13.29 and 25.25%, respectively. The highest drying efficiency, energy efficiency, and thermal efficiency were determined as 11.09%, 7.69% and 10.58%, respectively. The results revealed that increasing the temperature and ultrasonic pretreatment time and decreasing the sample thickness led to a significant increase (p < 0.05) in drying efficiency, thermal efficiency, and energy efficiency, while drying time, SEC and shrinkage significantly decreased (p < 0.05).

Energy and Exergy Analyses of a Combined Infrared Radiation-Counterflow Circulation (IRCC) Corn Dryer

Energy consumption performance evaluation of an industrial grain dryer is an essential step to check its current status and to put forward suggestions for more effective operation. The present work proposed a combined IRCC dryer with drying capacity of 4.2 t/h that uses a novel drying technology. Moreover, the existing energy–exergy methodology was applied to evaluate the performance of the dryer on the basis of energy efficiency, heat loss characteristics, energy recovery, exergy flow and exegetic efficiency. The results demonstrated that the average drying rate of the present drying system was 1.1 gwater/gwet matter h. The energy efficiency of the whole drying system varied from 2.16% to 35.21% during the drying process. The overall recovered radiant energy and the average radiant exergy rate were 674,339.3 kJ and 3.54 kW, respectively. However, the average heat-loss rate of 3145.26 MJ/h indicated that measures should be put in place to improve its performance. Concerning the exergy aspect, the average exergy rate for dehydration was 462 kW and the exergy efficiency of the whole drying system ranged from 5.16% to 38.21%. Additionally, the exergy analysis of the components indicated that the combustion chamber should be primarily optimized among the whole drying system. The main conclusions of the present work may provide theoretical basis for the optimum design of the industrial drying process from the viewpoint of energetics.

Exergoeconomic Analysis of Corn Drying in a Novel Industrial Drying System

The improvement of the design and operation of energy conversion systems is a theme of global concern. As an energy intensive operation, industrial agricultural product drying has also attracted significant attention in recent years. Taking a novel industrial corn drying system with drying capacity of 5.5 t/h as a study case, based on existing exergoeconomic and exergetic analysis methodology, the present work investigated the exergetic and economic performance of the drying system and identified its energy use deficiencies. The results showed that the average drying rate for corn drying in the system is 1.98 g_water/g_{dry matter} h. The average exergy rate for dehydrating the moisture from the corn kernel is 345.22 kW and the exergy efficiency of the drying chamber ranges from 14.81% to 40.10%. The average cost of producing 1 GJ exergy for removing water from wet corn kernels is USD 25.971, while the average cost of removing 1 kg water is USD 0.159. These results might help to further understand the drying process from the exergoeconomic perspective and aid formulation of a scientific index for agricultural product industrial drying. Additionally, the results also indicated that, from an energy perspective, the combustion chamber should be firstly optimized, while the drying chamber should be given priority from the exergoeconomics perspective. The main results would be helpful for further optimizing the drying process from both energetic and economic perspectives and provide new thinking about agricultural product industrial drying from the perspective of exergoeconomics.

Nutritional quality of heat-sensitive food materials in intermittent microwave convective drying

Background

The retention of health promoting components in nutrient-rich dried food is significantly affected by the dehydration method. Theoretical and experimental investigations reported in the literature have demonstrated that intermittent microwave convective drying (IMCD) can effectively improve the drying performance. However, the impact of this advanced drying method on the quality food has not been adequately investigated.

Design

A programmable NN-SD691S Panasonic inverter microwave oven (1100 W, 2450 MHz) was employed for the experiments. The microwave power level was set at 100 W and ran for 20 seconds at different power ratios and the constant hot air conditions was set to a temperature of 60°C and 0.86 m/s air velocity.

Objective

In this study, natural bioactive compounds (ascorbic acid and total polyphenol), water activity, colour and microstructure modifications which can occur in IMCD were investigated, taking kiwifruit as a sample.

Results and Discussion

The microwave (MW) power ratio (PR) had significant impact on different quality attributes of dried samples. The results demonstrate that applying optimum level MW power and intermittency could be an appropriate strategy to significantly improve the preservation of nutrient contents, microstructure and colour of the dried sample. The IMCD at PR 1:4 was found to be the ideal drying condition with the highest ascorbic acid retention (3.944 mg/g DM), lowest colour change (ΔERGB = 43.86) and a porous microstructure. However, the total polyphenol content was better maintained (3.701 mg GAE/g DM) at higher microwave density (PR 1:3). All samples attained a desirable level of water activity which is unsusceptible for microorganism growth and reproduction.

Conclusion

Overall, IMCD significantly improved the drying performance and product quality compared to traditional convective drying.

Modeling Thin Layer Drying Kinetics, Moisture Diffusivity and Activation Energy of Hazelnuts during Microwave-Convective Drying

The effects of microwave-convective drying as an efficient drying method, on drying kinetics of hazelnuts were studied. Drying experiments were conducted at three temperature (40, 50 and 60°C) and microwave power (0, 450 and 900 W) levels. The moisture ratio and the temperature of the hazelnuts were recorded during the drying. The results showed that microwave power had a more dominant effect than drying air temperature. Mathematical modeling was performed in order to predict the moisture changes during drying process. It was concluded that two term and Midilli et al. models were the best models to predict the drying kinetics of hazelnut in different conditions. The effective moisture diffusivities varied from 3.80327×10‒8to 1.71233×10‒6m2/s and had an increasing polynomial relationship with temperature and microwave power. The activation energy was also between 15.61675 and 41.0053 kJ/mol with a second-order relationship with microwave power.

Modeling Drying Properties of Pistachio Nuts, Squash and Cantaloupe Seeds under Fixed and Fluidized Bed Using Data-Driven Models and Artificial Neural Networks

This paper presents the application of feed forward and cascade forward neural networks to model the non-linear behavior of pistachio nut, squash and cantaloupe seeds during drying process. The performance of the feed forward and cascade forward ANNs was compared with those of nonlinear and linear regression models using statistical indices, namely mean square error ( M S E $MSE$ ), mean absolute error ( M A E $MAE$ ), standard deviation of mean absolute error (SDMAE ) and the correlation coefficient ( R 2 ${R^2}$ ). The best neural network feed forward back-propagation topology for the prediction of effective moisture diffusivity and energy consumption were 3-3-4-2 with the training algorithm of Levenberg-Marquardt (LM). This structure is capable to predict effective moisture diffusivity and specific energy consumption with R 2 ${R^2}$ = 0.9677 and 0.9716, respectively and mean-square error ( M S E $MSE$ ) of 0.00014. Also the highest R 2 ${R^2}$ values to predict the drying rate and moisture ratio were 0.9872 and 0.9944 respectively.