*Drying Science and Technology*

*Assessment of Solar Dryer Performance for Drying Different Food Materials: A Comprehensive… DOI: http://dx.doi.org/10.5772/intechopen.112945*

#### *6.1.2 Carrot*

The researchers investigated drying sliced carrots utilizing hybrid infrared power, hot air drying techniques, and thermal energy storage. The carrots' diffusivity ranged from 2.01 <sup>10</sup><sup>10</sup> to 12.10 <sup>10</sup><sup>10</sup> <sup>m</sup><sup>2</sup> /s, while the specific energy used during drying ranged from 30.20 to 87.51 MJ/kg. Carrot shrinkage was measured to be between 23.49 and 51.25%. The Midilli-Kucuk model was discovered to be the best fit for the drying kinetics. The study indicated that the thermal energy storage arrangement produced promising results for future large-scale applications. These findings emphasized the possibility of adding thermal energy storage into the drying process of carrots, which would increase efficiency and quality in industrial-scale drying processes [35].

Whereas sliced carrots were dried using a passive indirect solar dryer, compared setups without (configuration 1) and with thermal energy storage (configuration 2). Configuration 1 had a drying rate of 0.5, while Configuration 2 had a greater drying rate of 0.59. In configuration 2, the sample dried faster, with the moisture content dropping from 9.13 to 0.478 on a dry basis. The average effective diffusivity for Configuration 1 was determined to be 6.7 <sup>10</sup><sup>9</sup> m2 /s and 7.24 <sup>10</sup><sup>9</sup> m2 /s for configuration 2. The two settings'specific energy consumption and moisture extraction rates were 3.5 and 0.28 kg/kWh for configuration 1 and 0.29 and 3.62 kg/kWh for configuration 2. Configuration 2 had a higher drying efficiency with an average of 10.25% compared to configuration 1. Based on the study's findings, it is considered that configuration 2, with the passive indirect sun dryer and thermal energy storage, is an acceptable recommendation for future large-scale applications. These findings showed the benefits of drying sliced carrots with a passive, indirect sun drier with thermal energy storage. Configuration 2 indicated higher drying efficiency, energy utilization, and a faster drying rate. This study gave valuable insights for optimizing the drying process of carrots using renewable energy sources, and it can guide future large-scale industrial applications [36].

#### *6.1.3 Potato*

A waste heat-based convection dryer was used to dry potato samples. The study evaluated the drying kinetics of potato samples and found the best model to describe the process. The drying process's activation energy was 47.19 kJ/mol. This parameter gives information about the drying process's temperature sensitivity by indicating the energy required to remove moisture from potato samples. According to the study results, the Midilli et al. model offered the best fit to characterize the drying kinetics of the potato samples. This model is often used to depict removing moisture during drying. Furthermore, the effective moisture diffusivity of the potato samples was determined to be 4.22 <sup>10</sup><sup>10</sup>–11.67 <sup>10</sup><sup>10</sup> <sup>m</sup><sup>2</sup> /s at temperatures ranging from 50 to 70°C. The effective moisture diffusivity measures moisture's ability to travel within potato samples during drying [37].

Siyabonga Gasa et al. used a solar-venturi dryer to model the drying process of sweet potato slices in naturally ventilated warm air. The drying characteristics were studied using a non-linear regression approach. The naturally ventilated solar-venturi dryer and a lemon juice pre-drying treatment were suitable for small to medium-scale drying of sweet potato slices in the study. The dryer arrangement allows for efficient drying in warm air while utilizing solar energy. The naturally-ventilated solar-venturi dryer's effective diffusivity (Deff) values ranged from 3.32 <sup>10</sup><sup>9</sup> to 6.31 <sup>10</sup><sup>9</sup> <sup>m</sup><sup>2</sup> /s.

On the other hand, the Deff values for the hot air oven dryer ranged from 1.02 <sup>10</sup><sup>8</sup> to 2.19 <sup>10</sup><sup>8</sup> <sup>m</sup><sup>2</sup> /s. According to these findings, the naturally ventilated solar-venturi dryer had lower effective diffusivity values than the hot air oven drier. This implied that the solar-venturi dryer setup provides a more regulated drying environment, enhancing drying efficiency and preserving sweet potato slices [41].

### *6.1.4 Ivy gourd*

While drying ivy gourd, the researchers compared natural and forced convection indirect-type solar dryers. Various metrics were used to compare the performance of the two types of dryers. The natural convection solar dryer had an average collector efficiency of 62.56%, whereas the forced convection solar dryer had a higher average collector efficiency of 77.2%. The researchers also calculated the average values of activation energy, mass transfer coefficient, heat transfer coefficient, and diffusion coefficient for ivy gourd drying. The activation energy values of 39.85 and 35.54 kJ/ mol were obtained, demonstrating the energy required for moisture elimination during drying. According to the evaluation results, the forced convection configuration produced the most significant results for drying ivy gourd. The increased drying performance was aided by better collector efficiency and favorable activation energy, mass transfer coefficient, heat transfer coefficient, and diffusion coefficient values [38].

Another study examined how various pre-treatments affected the qualitative characteristics, moisture diffusivity, and activation energy of solar-dried ivy gourd. Ascorbic acid, lemon juice, sugar solution, honey dip, and a control group were used as pre-treatments. The effective moisture diffusivity of dried ivy gourd samples varied depending on the pre-treatment. According to the findings, pre-treatments substantially impact solar-dried ivy gourd's moisture content and quality features. The findings show that pre-treatments can efficiently decrease moisture content and contribute to extended preservation periods. The lemon juice samples were found to be the best regarding moisture diffusivity and activation energy among the pretreatments tested, demonstrating their effectiveness in the drying process [13].

Elavarasan Elangovan et al. investigated the drying kinetics of ivy gourd using a solar dryer, explicitly contrasting passive and active mode solar dryers with traditional sun drying. The results revealed that for the passive mode solar drier, the safe moisture content, indicating the necessary moisture level for storage, was obtained in 9 hours, for the active mode solar dryer in 7 hours, and for sun drying in 11 hours. This suggests that, compared to typical sun drying, both solar dryers were more efficient in drying time. The rate of moisture evaporation from the ivy gourd is affected by air temperature, whereas relative humidity influences the moisture content of the surrounding air. Airspeed, or air movement, aids in the removal of moisture-laden air from the drying environment, allowing for faster drying. The study suggested optimizing and managing certain drying parameters might improve the drying process, resulting in improved drying kinetics. Altering and regulating the solar drier's air temperature, relative humidity, global radiation, and airspeed might produce more efficient and faster ivy gourd drying [42].

Elavarasan Elangovan et al. conducted an experimental investigation to determine the investigated convective and evaporative heat transfer coefficients during the drying process of ivy gourd using natural and forced convection solar dryers and open sun drying. For ivy gourd, the average evaporative heat transfer coefficient, representing the efficiency of moisture evaporation, ranged from 181.89 to 421.84 W/m2 °C.

#### *Assessment of Solar Dryer Performance for Drying Different Food Materials: A Comprehensive… DOI: http://dx.doi.org/10.5772/intechopen.112945*

The rate at which heat is delivered to the product and utilized for evaporation is indicated by this coefficient. Higher air velocity, as achieved by forced convection, resulted in a faster drying rate for the ivy gourd samples. Increased air velocity accelerates heat and mass transmission, resulting in faster drying. Furthermore, as drying air velocity rose, the mass transfer coefficient, representing the moisture transfer rate from the ivy gourd to the drying air, increased. This suggests that higher air velocity improves more efficient moisture removal from ivy gourd samples [25].

#### *6.1.5 Onion*

G.P. Sharma et al. dried onion slices using a thin-layer infrared radiation drying technique. The effective moisture diffusivity, which represents the rate of moisture movement within onion slices, ranged from 0.21 <sup>10</sup><sup>10</sup> to 1.57 <sup>10</sup><sup>10</sup> m2 /s. The drying process used forced convection, which involves the utilization of air circulation to improve heat and moisture transfer. This method allows for faster drying and increases the drying system's efficiency. Furthermore, the drying time was calculated concerning the amount of infrared power used during drying. The drying time was shortened by nearly 2.25 times when the infrared power was increased from 300 to 500 W. This suggests that the drying of the onion slices was expedited by increasing infrared power. A third-order polynomial relationship was discovered to correlate several elements influencing the specified drying process. This relationship aided in predicting drying behavior based on variables, including infrared power, drying duration, and moisture content [40].

Hidalgo et al. dried green onions using a direct sun drier aided by a photovoltaic module, focusing on natural and forced air convection operation. The effective diffusivity values for natural convection were 5.15 <sup>10</sup><sup>9</sup> <sup>m</sup><sup>2</sup> /s and 1.15 <sup>10</sup><sup>8</sup> <sup>m</sup><sup>2</sup> /s for forced convection. These values indicate the rate of moisture diffusion within the green onions during drying. The Page and Overhults models were selected as the bestfit models during the slower drying periods [39].

#### **6.2 Fruits**

This section discusses the performance of different solar dryers for drying various fruits, including Banana, Cucumber, Tomato, and Grapes. The summary of the same is presented in **Table 5**.

#### *6.2.1 Grapes*

Traditional grape drying methods have several disadvantages, such as mass losses and low quality. A joint German-Greek research program developed low-cost solar grape dryers to address these challenges. Solar dryers use the sun's energy to heat air, which is then circulated through the dryer to dry the grapes. This drying method has several advantages over traditional methods, including reduced drying time, improved quality, and prevention of mass losses [47].

The drying kinetics of two varieties of grapes grown on both shores of the Mediterranean Sea was the subject of another study. The drying kinetics were evaluated as a function of drying conditions, and the diffusion coefficient was determined. Two diffusion models were employed to determine the effective diffusivity: a simplified model based on Fick's law and a more complex model that accounted for the grapes' shrinkage. The study revealed that the drying kinetics of the two grape varieties were


**Table 5.** *Summary of performance of different dryers for drying various*

 *fruits.*

#### *Drying Science and Technology*

*Assessment of Solar Dryer Performance for Drying Different Food Materials: A Comprehensive… DOI: http://dx.doi.org/10.5772/intechopen.112945*

comparable, but the grapes cultivated on the southern side of the Mediterranean Sea had a higher effective diffusivity. The study also demonstrated that the drying conditions influenced the drying kinetics, with a faster drying rate at higher temperatures and reduced relative humidities [48].

Fadhel et al. compared the three solar processes to dry Sultanine grapes: natural convection solar drier, tunnel greenhouse, and open sun. The results showed that the solar tunnel greenhouse drying was the most efficient, followed by the natural convection solar drier and open sun. The solar tunnel greenhouse drying was also the most consistent, with the drying rate being relatively unaffected by changes in weather conditions [49].

#### *6.2.2 Tomato*

J. B. Hussein et al. dried tomato slices in thin-layers using hybrid, solar, and open sun drying methods. The effective moisture diffusivity values, which represent the moisture transfer rate within the tomato slices, were determined for each drying process. The effective moisture diffusivity values in the hybrid drying method ranged from 2.00 <sup>10</sup><sup>10</sup> to 5.84 <sup>10</sup><sup>10</sup> <sup>m</sup><sup>2</sup> /s, according to the results. The values for solar drying ranged from 1.37 <sup>10</sup><sup>10</sup> to 4.40 <sup>10</sup><sup>10</sup> <sup>m</sup><sup>2</sup> /s, whereas open sun drying ranged from 1.33 <sup>10</sup><sup>10</sup> to 4.01 <sup>10</sup><sup>10</sup> <sup>m</sup><sup>2</sup> /s. On a wet basis, the moisture content of the tomato slices was reduced by 94.22–10% following drying. The Page model was used to simulate the drying kinetics. The declining rate stage of drying is described by this model, in which the moisture removal rate lowers as the moisture content falls. The Page model was used to forecast drying behavior and estimate drying time for tomato slices [46].

H. Samimi, Akhijani et al. concentrated on hot air solar drying of tomato slices using forced convection. The moisture diffusivity, representing the moisture transfer rate within tomato slices, was examined at various air velocities and slice thicknesses. At an air velocity of 2 m/s and a slice thickness of 7 mm, the most significant moisture diffusivity value achieved was 6.98 <sup>10</sup><sup>9</sup> <sup>m</sup><sup>2</sup> /s. Higher air velocity and thicker slices facilitate faster moisture transfer during drying. At an air velocity of 0.5 m/s and a slice thickness of 3 mm, the minimum moisture diffusivity value achieved was 1.58 <sup>10</sup><sup>9</sup> <sup>m</sup><sup>2</sup> /s. This shows that slower moisture transfer is caused by decreased air velocity and thinner slices. The Page model was used to analyze the drying kinetics, and it provided the best fit for the experimental data [45]. P. Rajkumar et al. examined vacuum-assisted solar drying of tomato slices using a vacuum-assisted solar drier. Compared to open sun drying, the vacuum-assisted solar drying approach required less drying time for the slices. The Page model, which best fits the experimental data, was used to analyze the drying kinetics [50].

#### **6.3 Marine products**

This section discusses different solar dryers' performance for drying marine food products, including Fish, Shrimp, and Prawn. The summary of the same is presented in **Table 6**.

#### *6.3.1 Fish*

Pranav Mehta et al. dried fish using a mixed-mode tent-type solar drier. The Fish's moisture content was reduced from an initial value of 89% to a final value of 10%.


**Table 6.** *Summary of performance*

 *of different dryers for drying various marine food products.*

*Drying Science and Technology*

*Assessment of Solar Dryer Performance for Drying Different Food Materials: A Comprehensive… DOI: http://dx.doi.org/10.5772/intechopen.112945*

During the drying process, the effective moisture diffusivity was 1.53 <sup>10</sup><sup>7</sup> <sup>m</sup><sup>2</sup> /s. The drying kinetics were described using the Lewis model of drying. The Lewis model is widely used to study moisture transfer in porous materials during drying.

Furthermore, the study concluded that, under loaded conditions, recirculating the outlet's hot air after absorbing moisture is the most efficient energy utilization [52]. The researchers used the Page equation to predict the drying process of Fish. The drying rate of the fish samples was found to be fastest in the beginning and gradually decreased over time. The average effective diffusivity ranged from 7.158 <sup>10</sup><sup>8</sup> to 3.408 <sup>10</sup><sup>7</sup> <sup>m</sup><sup>2</sup> /s, demonstrating that moisture could diffuse throughout the Fish at different rates during the drying process. The moisture content of the fish samples was reduced significantly in the study, from 2.76 to 0.01 on a dry basis, suggesting the efficiency of microwave heating in drying the Fish. The Page equation was used to simulate the drying process using non-linear regression analysis, which offered a good fit for defining the drying characteristics of the fish samples [51].

#### *6.3.2 Shrimp and prawn*

D.S. Aniesrani Delefiya et al.'s studies provided valuable insights into the drying process of shrimp in an electric dryer. The initial moisture level of the shrimp samples ranged from 73 to 79% (wb) in the study on the drying characteristics of shrimp in an electric dryer, and the drying process aimed to reduce it to a final moisture content of 8–10% (wb). Many mathematical models were explored to understand and model the drying behavior of the shrimp. The Midilli model was chosen as the best-fit model for understanding the drying kinetics of shrimp [53]. The effect of several drying processes on the physical and qualitative parameters of dried shrimps was examined. The prawns were dried in an oven at 60, 70, and 80°C for 330–210 minutes and in a vacuum oven for 190–110 minutes. The usage of a vacuum pump decreased the drying time. The drying kinetics of prawns were investigated, and both techniques' appropriate moisture diffusion and activation energy were estimated.

The Alibas and Midilli and Kucuk models offered the best experimental data with a high coefficient of determination (R<sup>2</sup> ) for the oven and vacuum oven approaches. The final dried goods' color features, heavy metal levels, and protein analyzes were investigated. The rehydration ratio of dehydrated shrimps was also established. The study's findings revealed that the drying conditions influenced the color characteristics of the shrimps. Shrimp dried in ovens and Hoover ovens had higher brightness and yellowness scores but lower redness levels. The Pb, As, Cd, Hg, Cu, Zn, and Fe concentrations in dried prawns were below permissible levels [54].

#### **6.4 Other food substances**

In addition to the above-mentioned vegetables, fruits, and marine food products, the other food substances are also undergoing post-harvest loss predominantly. This section discusses the performance of different solar dryers for drying various essential food products, including Chili, Ginger, and Jaggery. The summary of the same is presented in **Table 7**.

#### *6.4.1 Chili*

Zakaria Hossain et al. developed a solar dryer for drying chilies. The initial moisture level of the chilies placed in the drier was 73%, which decreased to 14% during


*Drying Science and Technology*

**Table 7.**

*Summary of performance of different dryers for drying various other food products.* *Assessment of Solar Dryer Performance for Drying Different Food Materials: A Comprehensive… DOI: http://dx.doi.org/10.5772/intechopen.112945*

drying. One interesting observation was that the drying rate of the chiles on the upper tray was faster than on the lower tray. This disparity in drying rates can be attributable to various factors, including heat distribution, air circulation, and direct solar exposure. The dryer uses forced convection technology, which uses a fan or blower to increase movement and speed up drying. Forced convection improves heat transfer and moisture elimination from the chilies, resulting in faster drying [62]. Francis Kumi and Bram Parbi used an innovative approach to improve the heating system and drying performance in their study on a solar chimney dryer for chili peppers. They increased the efficiency of the solar drier by including sea pebbles in the collection base.

Using a basic regression analysis model, the researchers tested the solar chimney drier's drying performance. The chili peppers had an initial moisture content of 73.12% (w.b.) that decreased to 7.15% (w.b.) after drying. The inclusion of sea pebbles in the collection base was critical in improving the solar dryer's heating mechanism. The sea pebbles absorbed and held the sun's heat, contributing to higher temperatures within the drying chamber. As a result, the drying performance improved, and the moisture removal from the chili peppers was hastened [61]. A.K. Kamble et al. used a solar cabinet drier with a gravel bed and forced convection for drying chiles. The solar cabinet drier used forced convection to improve heat and moisture transfer by boosting airflow within the drying chamber. This forced convection technique helped the chiles dry more quickly.

Furthermore, a heat storage system was added to the drying process. This technique made heat available even after sunset, allowing the drying process to continue for an additional 4 hours. The heat storage system assisted in maintaining the required temperature within the dryer, ensuring successful drying even when solar radiation was limited [60].

#### *6.4.2 Ginger*

The performance of the solar greenhouse drier for drying Ginger was examined by Nimnuan et al. The purpose of the drying method was to bring the moisture content of the Ginger down from its initial value of 90% (wb) to a final value of 10% (wb). A thin-layer drying model was used to characterize drying kinetics. The effectiveness of the solar greenhouse drier in facilitating the drying process and attaining the target moisture content was evaluated in this study. The results provide insight into the solar greenhouse dryer's capability and efficacy for drying ginger [58]. Another study on the solar drying of Ginger reported an effective moisture diffusivity of 1.789 <sup>10</sup><sup>9</sup> <sup>m</sup><sup>2</sup> / s, which quantifies the rate at which moisture flows within the Ginger during drying. Various mathematical models were tested to understand the drying kinetics and model the drying process. The Page model was determined to be the best fit for characterizing the drying kinetics of Ginger in the solar dryer using natural convection among the models tested. It estimates the material's drying characteristics by considering the moisture content, drying duration, and drying rate [57].

#### *6.4.3 Jaggery*

Om Prakash et al. investigated the use of fuzzy logic to predict the rate of Jaggery's moisture evaporation in a controlled environment. MATLAB software generated the fuzzy logic model, which was then validated using experimental data. The results demonstrated that the fuzzy logic model could predict the moisture evaporation rate

with no more than 0.27% error. The model can be extended to various locations under varying weather conditions: ambient temperature, solar radiation, and relative humidity [63]. The objective of another study was to construct an adaptive-networkbased fuzzy inference system (ANFIS) model to predict the jaggery temperature, greenhouse air temperature, and moisture evaporation during the natural convection drying of jaggery within a greenhouse. For complete drying, distinct experiments were conducted for 0.75 kg and 2.0 kg jaggery pieces measuring 0.03 0.03 0.01 m<sup>3</sup> . The jaggery was desiccated in a roofed, even-span greenhouse with a 1.20 0.78 m<sup>2</sup> floor area. MATLAB software was used to construct the ANFIS model for calculating jaggery temperature, greenhouse air temperature, and moisture evaporation. The model was also utilized to predict the greenhouse's thermal performance based on solar intensity and ambient temperature. Analytical and experimental results for jaggery drying were in excellent agreement following experimental validation of the model [64].

Using an artificial neural network (ANN), Om Prakash et al. predicted the hourly mass of jaggery during drying in a greenhouse dehydrator with natural convection. Jaggery was dehydrated until its mass fluctuated constantly. The input parameters for the ANN model were solar radiation, ambient temperature, and relative humidity. The outcomes of the ANN model were validated using experimental data on the dehydration of jaggery mass. The statistical parameters root mean square error (RMSE) and correlation coefficient (R2 ) was utilized to determine the difference between the values predicted by the ANN model and those observed in the experimental investigation [65].

Kumar et al. developed a thermal model that could forecast the jaggery temperature, greenhouse air temperature, and moisture evaporated (jaggery mass during drying) during natural convection drying of jaggery. The jaggery was dried in a roof-type even-span greenhouse with a 1.20 0.78 m<sup>2</sup> floor area. In MATLAB software, a computer program was developed to calculate the jaggery temperature, greenhouse air temperature, and moisture evaporated. The program was also utilized to forecast the greenhouse's thermal performance based on sun intensity and ambient temperature. The program was experimentally evaluated, and the findings revealed that the analytical and experimental results for jaggery drying agreed well [66].
