Development and Techno-Economic Analysis of a Tracked Indirect Forced Solar Dryer Integrated Photovoltaic System for Drying Tomatoes

Abstract

Fresh tomato fruits (TFs) contain a high moisture content of 90–94%, which makes storage and transportation over long distances difficult. Lately, numerous investigators have employed diverse solar dryers (SDs) in conjunction with stationary solar collectors (SCs) to dry tomatoes; however, the effectiveness of this technique is limited due to the sun’s constant motion throughout the day. Consequently, the current study set out to create an SD that is outfitted with an autonomous sun tracking system and an internet of things (IoT)-based photovoltaic system connected to an SC to continually track the sun and increase the quantity of energy absorbed. Furthermore, we investigated some operating parameters that impact the SD’s performance, taking into account three tomato slice thicknesses (STs) (4.0, 6.0, and 8.0 mm) and three air velocities (1.0, 1.5, and 2.0 m/s). The obtained data demonstrated a notable rise in the efficiency of the SD integrated with the automatic SC tracker throughout the course of the day when compared to the fixed SC, where the latter’s efficiency improved by 21.6%, indicating a strong degree of agreement. The results demonstrated a notable 20–25% reduction in drying time and a 4.9 °C increase in air temperature within the SC integrated with an automatic solar collector tracker (ASCT) at 2:00 p.m., as compared to the SC integrated with a fixed SC. The results of this study also demonstrated that there were no appreciable variations in the air speeds used to dry the tomatoes; however, the thickness of the tomato slices (TSs) had a significant impact; using 4 mm thick tomato slices resulted in a 50% reduction in drying time. Furthermore, the highest efficiency of the PV system was discovered to be 17.45%. Although the two solar dryers have very similar payback times, there are more dried tomatoes available in the markets.

1. Introduction

On a global scale, tomato fruits (TFs) (Lycopersi conesculentum) are the first most widely grown vegetable in the world, with an annual yield of 186.11 million metric tons [1]. Egypt is the fifth-largest producer in the world, where the yearly production volume is approximately 6.25 million metric tons, and it plays an important role in producing good healthy foods and fast foods, where it is a good source of many important nutritional elements [1,2,3,4,5,6]. A TF’s chemical composition includes the following: 0.6–6.6% dry matter, 0.95–1.0% protein, 4.0–5.0% sugar, 0.2–0.3% fat, 0.8–0.9% cellulose, 0.6 ash, 0.5% organic acids, 19–35 mg/kg vitamin C, 0.2–2 mg/kg carotene, 0.3–1.6 mg/kg thiamine, and 1.5–6 mg/kg riboflavin [4,5,7,8,9]. Food processors and consumers alike will greatly appreciate the existing demand for hygienically processed solar-dried tomatoes of good quality with desirable characteristic color [10]. Open sun drying (OSD) is a widely used technique for food preservation. Most tropical and subtropical countries use this method to dry vegetables and fruits, where the fresh materials are spread on the ground, but there are many disadvantages and problems, such as the low quality of the final products due to contamination of the dried product by various sources such as dust, rain, and rodents, as well as long drying times [2,3,4,5,6].

Enhancing the sustainability of TF value chains hinges on boosting production, minimizing losses, and mitigating associated environmental impacts. The drying procedure, while elevating energy requirements, concurrently lowers the climate change footprint. Furthermore, it has the potential to minimize product losses and shelf life, thereby supporting the value chains of locally supplied organic TF in a sustainable manner. Integration with renewable energy sources could further enhance this effect [11,12,13].

The use of a solar dryer (SD) as a plentiful, sustainable, and renewable energy source has drawn interest from academics all around the world to engage in solar energy application research. Solar drying has the potential to meet the growing demand for affordable, healthful food products in developing nations, while also addressing the need for sustainable income. SDs used in the dehydration of agricultural products are highly advantageous in terms of energy conservation. These devices conserve energy and save considerable time, occupy minimal space, enhance product quality, and elevate the overall quality of life [14,15,16].

As opposed to an OSD, an SD aids in controlling all environmental factors related to the drying process, such as air velocity (AV), air temperature, and relative humidity (RH), to preserve the dried goods’ quality and protect the product from the various contamination sources previously mentioned [17]. There are many types of SDs that can be classified based on a variety of factors, such as the air circulation mechanism (i.e., natural circulation and forced circulation), drying type (i.e., direct, indirect, and mixed-mode), operation type (i.e., batch or continuous), and material to be dried, etc. [4]. After researching, Shimpy et al. [18] found that for high-moisture-content products, forced convection SDs offer greater appeal owing to their enhanced performance, accelerated drying rate (DR), and reduced drying time. SDs possess the capability to halve conventional drying expenses and boost returns by up to 30%. ElGamal et al. [19] mentioned that to achieve a high system efficiency, it is essential to use an automated system, specifically a solar tracker, which, when the sun travels across the sky, continuously lines the SD up with the ideal position. Samimi-Akhijahani et al. [20] reported that solar tracking systems could be a promising approach not only for their role in accelerating the solar drying process and reducing the drying time but also for applying advanced technologies in industrial applications. Several researchers have designed, developed, and evaluated many types of SDs so far. Since the location of an experiment affects the intensity of the solar radiation (SR), most designs are region-specific. In order to enhance DRs after sunset, Ghoniem and Gamea [21] designed and evaluated two direct SDs where a solar heat storage unit is integrated with one of the drying chambers (DCh). They found that in an SD equipped with a heat storage unit, the overnight internal temperature was consistently about 5 °C higher than in a standard SD. The developed SD showed a significant increase in the DR, resulting in a significant reduction of 33.3 to 36% in the total time required to reach the desired final moisture content (MC). Djebli et al. [22] studied the DR of TF using a large-scale mixed SD. The authors employed four distinct STs: 1.0, 1.5, 2.0, and 0.5 cm. The initial MC was between 92.5 and 93.6%. RH decreased between 18 and 25%. The drying process lasted from 5 h to 21.25 h. It was found that the DR was higher for the thinnest ST. Bakry et al. [23] studied the drying of TFs under three drying systems (OSD, SD, and oven drying). Five STs of TFs were used (half, quarter, 7 mm, 5 mm, and 3 mm), including two different treatments (with and without ascorbic acid 2%). The authors discovered that slices of 7.0 mm had the greatest TF DR value treated with 2% citric acid by an SD. Ebadi et al. [24] evaluated the performance of hybrid SDs, where three STs (4.0, 6.0, and 8.0 mm), three airflow rates (0.01, 0.025, and 0.4 m³/s), and three ATs (55, 65, and 75 °C) were used. The results indicated that the drying time was reduced to 83 min for an ST of 4.0 mm with a 0.04 m³/s airflow rate and 75 °C. Chouikhi et al. [25] designed and evaluated an indirect-forced convection SD integrated with a PV system and SC for drying TFs. The results showed that the average efficiency of the SC, SD, and PV systems was 30.9, 15.2, and 8.7%, respectively. Suherman et al. [26] found that the DR is primarily influenced by AV, AT, (SR), product type, and initial MC. Kumar et al. [27] developed a finite-element model for drying tomato flakes under a passive greenhouse dryer. They stated the potential for optimizing greenhouse dryers and promoting sustainable, cost-effective drying practices in the food industry. Ahmad et al. [28] studied the drying kinetics and performance analysis of a thermal storage-based hybrid greenhouse dryer for the uniform drying of tomato flakes. Ahmad et al. [29] evaluated the performance of a solar greenhouse dryer at different bed conditions under a passive mode.

There are many drawbacks related to the fixed solar systems, where the effectiveness of solar systems relies on the amount of radiation received from the sun. Thus, if there is a mechanical apparatus that tracks the movement of the sun and is capable of adjusting the orientation of the solar panels to ensure that the sun’s rays are perpendicular to the surface, it would optimize the amount of radiation absorbed by the solar system [30]. Also, there have been many attempts to increase the thermal efficiency (TE) of the SC. ElGamal [19] developed an SD-integrated ASCT for drying apple fruit, and they reported that the TE of the SD reached 45% compared to the traditional SD. Bhowmik et al. [31] improved the TE of a flat-plate SC using solar reflectors. The result showed that the TE was increased by about 10% compared to the traditional system. Zheng et al. [32] numerically investigated a compound parabolic concentrator SC, and the results showed that the TE of the SC was 60.5%. Zou et al. [33] tested a small parabolic-trough SC, where the TE of the SC reached about 67%. A heat-pipe evacuated tube with an SC was created by Chamsa-ard et al. [34], and the findings revealed that the collector’s TE was 78%. Ref. [35] found that the TE of the SC reached 76% when using a circular glass tube. Wei et al. [36] evaluated a flat-plate heat SC with a heat pipe where the TE of the SC reached 66%. The operation of an individual spiral-shaped SC tube and the number of riser tubes linked to headers in a standard SC were compared by Verma et al. [37], where the result showed that the TE increased by 21.94% under a forced mode compared with the traditional SC. Ramachandran et al. [38] compared the performance of a flat-plate SC and a Scheffler solar concentrator, and the results indicated that the TE was increased by 6% when using a chaffier concentrator. Das et al. [39] compared the performance of an SD integrated with an ASCT with the traditional system, and they reported that the TE was increased by up to 75.7% by using the tracking system. A biaxial solar tracker that is mirror-reflected was devised and assessed by Ismail et al. [40]. According to their study, the AT was 15% higher on average than that of the fixed panel. There are also many other attempts being made by many researchers to maximize the use of solar energy in solar dryers, and the monitoring of surface heat is significantly important Hence, these are some references concerning surface heat flux online measurements [41,42,43,44].

An economic analysis of an SD integrated with an ASCT was performed to determine the commercial sustainability from the perspective of commercial viability. The Egyptian financial environment served as the basis for estimating the economic performance metrics. The economic performance parameters were annualized cost of drying, pay-back period, and net present value as the key performance indicator parameters based on the findings of Mohammed and Al Dulaimi [45], ELkhadraoui et al. [46], and Singh and Gaur [47].

Given the limitations imposed by climate change, energy solutions must provide minimal greenhouse gas (GHG) emissions while being cost-effective. Employing photovoltaic (PV) systems is a practical and effective approach to mitigating the release of GHG emissions. PV applications, including tiny PV systems in rural off-grid areas, large-scale PV power plants, and commercial PV rooftop systems, provide financial advantages in terms of minimizing GHG emissions [48,49,50,51,52,53].

An important concept in the world of information technology is the internet of things (IoT). The IoT is a technological advancement that involves the transformation of physical objects into intelligent virtual entities, shaping the future of technology. The objective of this concept is to combine all aspects of our surroundings into a unified infrastructure, giving us the ability to not only manage our environment, but also get up-to-date information about its condition [54]. Many researchers have used IoT technology in many agricultural fields, such as the analysis of soil nutrients [55], drying of agricultural products [56], management of smart farming and irrigation systems [57], management of poultry systems [58], design of farm machinery [59,60,61,62], management of post-harvesting technology [63], and monitoring of water quality [64].

Thus, the current study aimed to

• Design and conduct performance assessments of a PV-integrated SD based on IoT technology and an automatic solar collector tracker (ASCT) for drying the most popular TF variety in Luxor Governorate, Luxor, Egypt, and decreasing the MC to a safe level for storage and handling.
• Study some important parameters related to DR and drying time, such as the type of SD, SR intensity, AV, and hot AT.
• Perform an economic analysis of both investigated SDs.

2. Materials and Methods

To reach these goals, a hot air-indirect-forced SD system with a PV system and an ASCT was created and set up in a workshop in Luxor, Egypt. The performance of the SD system with an ASCT was compared to that of the SC system with a fixed SC (FSC) under the same operating conditions. We designed the SDs to function with a broad spectrum of AV and AT. In the current study, the SD integrated with an ASCT and the SD integrated with an FSC are shown in Figure 1.

2.1. Design Equations of the SD

2.1.1. Amount of Moisture to Be Removed ($M_{\mathrm{w}}$)

TF contains a high MC percentage that must be decreased to a safe level. The amount of moisture was estimated using Equation (1), as stated by Etim et al. [66].

$$M_w={\displaystyle \frac{W_w\times \left[M_i-M_f\right]}{\left[1-M_f\right]}}\times 100$$

(1)

where $W_w$ is the fresh TF, and $M_i-M_f$ are the initial and final moisture contents, respectively.

2.1.2. Heat Quantity

Using Equation (2), the amount of heat needed to move the TF slices’ AT within the DCh (Q₁, J) to the hot AT was calculated, as mentioned by Dissa et al. [67], where the C_p of TF is 3.5174 ± 68.5 kJ/kg.K.

$$Q_1=W_w\times C_p\times \Delta T$$

(2)

where $C_p$ is the specific heat of TF, and ΔT is the change in air temperature inside the drying room.

The heat quantity required for evaporating the MC from the TSs (Q₂, J) was calculated using Equation (3), as stated by Eke and Simonyan [68]:

$$Q_2=M_w\times L_v$$

(3)

where $L_v$ is the water’s heat of vaporization (2257 J/kg).

The total heat quantity required for removing the MC from the TF slices (Q_T, J) was calculated using Equation (4), as mentioned in Eke and Simonyan [68]:

$$Q_T=Q_1+Q_2$$

(4)

2.1.3. The SC’s Surface Area

The SC’s surface area (A_c) required to receive SR can be calculated by Equation (5), as reported in Babar et al. [69].

$$A_c={\displaystyle \frac{Q_{abs}}{\eta \times I_t\times t_d}}$$

(5)

where $Q_{abs}$ is the total heat quantity absorbed by the solar collector, W; $\eta$ is the efficiency of the glass cover, %; $I_t$ is the total solar radiation, W/m²; $t_d$ is the total sunshine hours available on a particular day.

2.1.4. Determination of Angle of Inclination (Tilt Angle)

Eke [70] reported that the tilt angle (β) of an SC was found within latitude ($\varnothing$) 25.6890° N using Equation (6). To prevent the dryer from being too tall and to make it easier to monitor the drying process, an inclination angle of 28° was set during the design phase. The design and SD size calculations were performed using the assumptions and parameters listed in

Table 1. The design calculation and SD.

Items	Conditions and Assumptions
Drying location	25.6890° N, and 32.6975° E
Orientation	Southwards direction
Tilt angle	28°
Product	TF (Lycopersi conesculentum)
Loading capacity	5 kg
Initial MC, %	92 ± 2%
Total drying time	10 h
SC dimensions (length × width × height), cm	100 × 50 × 15 cm
DCh dimensions (length × width × height), cm	44 × 44 × 63 cm
SD dimensions (length × width × height), cm	44 × 46 × 163 cm
Tray dimensions (Length × Width), cm	44 × 44 cm

.

$$\beta =2.66°+Lat \varnothing$$

(6)

2.2. Description of the SDs

The constructed SDs comprise an SC, a DCh, drying trays, two axial flow suction fans, control systems, and a measuring unit. The suction fans, measuring electronic circuit, and control circuit were operated by a PV system. Figure 2 shows the main components of both SDs, while Figure 3 illustrates the detailed drawings of the three basic views and overall dimensions of both SDs.

The SD can be described as follows:

2.2.1. DCh and Trays

A wooden box measuring 44 cm in length, 44 cm in width, 63 cm in height, and 2 cm in thickness of walls served as the DCh. The DCh’s top point was where the suction fan was mounted for forced circulation of air inside the DCh, so it draws ambient air to pass through the SC and then into the DCh, passing through TSs on drying trays. Two trays can be stacked on the DCh with a 20 cm gap between them. The dimension of each tray is 44 cm × 44 cm.

2.2.2. Description of the SC

Fixed SC

As seen in Figure 4, the timber SC frame had dimensions of 100 cm in length, 50 cm in width, and 15 cm in depth. A galvanized metal sheet with a thickness of 3 mm was used as the absorber material. The SC was covered with a glass sheet of 3 mm thickness.

The SC was oriented in a southward direction and inclined horizontally at an angle of 28°. The ambient air entered through three holes on the front side of the SC. The SC was connected to the DCh by flexible tubes 100 cm in length, 5 cm in diameter, and 2 mm thick, which was made of polyethylene and insulated by glass wool 3 mm thick. As seen in Figure 3, the SC was set up on a metal frame of 104 cm in length, 60 cm in width, and 71 cm in height.

Automatic SC Tracker (ASCT)

The ASCT has the same dimensions and is constructed from the same raw materials previously mentioned about the FSC, but the ASCT has a rotating pivot axis for tracking the solar rays during the day and maximizing the SC efficiency. But it has additional parts, such as the smart monitoring unit and solar tracking circuit, which are described in detail below.

2.2.3. Design of Smart Monitoring Unit and Solar Tracking Circuit

Figure 5 illustrates the working flow diagram and the main parts of the ASCT. The ASCT consists of the following components: laptop, linear actuator, LDR sensor (5 mm), Arduino Uno board, battery charger, converter, battery, and PV system (75 W); the specifications of the main electronic components are listed and described in

Table 2. List of components and specification of the main electronic components of the ASCT *.

No.	Quantity	Component	Accuracy
1.	1	Arduino Uno (7–12 Vdc), China	---
2.	2	LDR sensor, China	±1 Lux
3.	1	Relay kit (4—channel), Generic, China	---
4.	1	Linear DC motor (actuator) (10 W, 0.4–0.8 A–36 V, 24-inch, model No: HARL-3624+), Germany	---
5.	1	Converter (input: 110–220 V AC; output: 36 V DC 10 A), DAJUNGUO, China	---
6.	2	Linear resistor (10 kΩ), China	±0.5%

* All electronic parts were purchased from local markets in Egypt.

.

Figure 4, Figure 5 and Figure 6 show the electric circuit of the main components of the ASCT, which were drawn using the Fritzing program. Table 2 lists the components and specifications of the ASCT.

2.2.4. Operating Algorithm of the ASCT

The SC integrated with an ASCT was developed for tracking the sun’s movement automatically, as shown in Figure 3. When the system is operating, the controller initializes the linear DC motor and both LDR sensors, and then it reads the analog signals from both LDR sensors (Eastern LDR_E and Western LDR_W), as shown in Figure 7.

Then, the controller compares the obtained signals from both LDR sensors (LDR_E and LDR_W) with the stored reference tolerance values (±5 Lux). If the LDR_E sensor (fixed at the eastern side of the ASCT) is higher than the LDR_W sensor (fixed at the western side of the ASCT) plus tolerance value (+5 lux), the controller outputs signals to the relay kit and turns on the linear DC motor (clockwise), and the ASCT rotates around the pivot axis from the western to the eastern direction. Also, if the LDR_W sensor is higher than the LDR_E sensor plus the tolerance value (+5 lux), the controller outputs signals to the relay kit and turns on the linear DC motor (anti-clockwise), and the ASCT rotates around the pivot axis from the eastern to the western direction; else, the linear DC motor is still turned off. In the morning of the next day, the ASCT rotates automatically from west to east, and the controller initializes the linear DC motor and both LDR sensors.

This real-time adjustment ensures that the ASCT constantly maximizes its exposure to sunlight, significantly enhancing its energy absorption efficiency. The process operates seamlessly, with the LDR sensors performing readings every second and repeating the cycle of steps. If both sensors detect equal lighting intensities, the unit remains closed, maintaining its current orientation. This mechanism provides stability and avoids unnecessary movements when there is no significant difference in lighting intensities. This continuous alignment improves the overall performance of the SD, reducing the DT and increasing the TE of the drying experiment.

2.2.5. AT and RH Measuring Unit

Figure 8 illustrates how five DHT22 sensors linked with the Arduino board are used to measure both AT and RH. Then the controller interprets the obtained data and sends them immediately to a laptop via a USB cable. DHT sensors No. 1 and 2 measure both the AT and RH of hot air in an FSC; DHT sensors No. 3 and 4 measure both the T and H of hot air in the ASCT; and DHT sensor No. 5 measures both the T and H of ambient air.

2.3. Experimental Procedure and Uncertainties

TF was chosen as a sample biological material for this investigation, and it was dried and evaluated. Fifty kilograms of fresh, mature TFs were collected from local farms in July 2023 in Luxor City. The TFs utilized in the present investigation varied in mass, with values ranging from 80 to 100 g. The TFs underwent a washing process to eliminate any dirt and dust particles. Subsequently, they were cut into three different thicknesses of 4.0, 6.0, and 8.0 mm using a kitchen knife that had been sharpened with a laser. Ultimately, the TSs were evenly distributed on the DCh. Bakry et al. [23] studied the drying of TF under five STs of the TFs used (half, quarter, 7 mm, 5 mm, and 3 mm); Ebadi et al. [24] evaluated the performance of a hybrid SD, where three STs (4.0, 6.0, and 8.0 mm) were used; Sadin et al. [52] designed and evaluated a hot-air dryer for drying TSs, where three STs (3, 5, and 7 mm) were used, AV 1.1 m/s. Dufera et al. [53] dried TFs at 5.0 mm STs.

All tests associated with the drying process were carried out in the weather conditions of Luxor, Egypt, at 25.6890 °N latitude and 32.6975 °E longitude, since the availability of sunshine was higher and there was clear solar insolation during the period of July to August 2023. The average yearly weather condition of Luxor shows that July is the hottest month in Luxor, with an average temperature of 33 °C, and the coldest is January at 14 °C, with the most daily sunshine hours (13) in August. The wettest month is October, with an average of 1 mm of rain. All laboratory tests were carried out at the Faculty of Agricultural and Natural Resources, Aswan University, Aswan, Egypt (latitude and longitude of 23.9965° N and 32.8599° E, respectively). The field tests of drying processes started at 8:00 a.m. and ended at 6:00 p.m. for 10 h every day, and the TF sample’s weight was measured and recorded every hour. During the drying process, solar radiation (SR), air temperature (AT), and relative humidity (RH) were measured, where AT and RH were measured at three positions (ambient air, inlet of the DCh, and outlet of the DCh).

Table 3. The accuracy of the different instruments and sensors was used in the current investigation.

Parameters	Unit	Instrument	Range	Resolution	Standard Division
AT	°C	DHT-22 sensor	−10–80°C	0.1 °C	±1 °C
RH	%	DHT-22 sensor	0–100%	0.1%	±2%
SR	W/m2	Spectral pyranometers		0.1 W/m2	±10 W/m2
Weight of TF samples	kg	Electronic digital balance	0.0–50 kg	5 g	±0.020
Weight of dried TF inside DCh	kg	Electronic digital balance	0.0–10 kg	10 g	±10 g
Weight of dried TF in laboratory	kg	Electronic digital balance	0.0–1.0 kg	0.1 g	±0.15 g
Voltage and current (PV system)	V, A	Digital multimeter	0.2–1000 V 20 µA–20 A	0.01 V 0.01 A	--
Airspeed	m/s	A digital anemometer	0.0–30 m/s	0.1 m/s	±0.1 m/s
Light intensity (ASCT)	Lux	LDR sensor	0.0–1000 Lux	0.1 Lux	±1 Lux

is an illustration of the accuracy of several devices and sensors utilized in the current investigation. Any measurement, no matter how exact and reliable, always has some degree of uncertainty. The two primary sources of these uncertainties are measurement methods, which are also referred to as random errors, and measurement apparatus, which are referred to as systematic errors. The total errors were estimated by using Equation (7), as stated by Gulcimen et al. [71].

$${\mathcal{W}}_{th}=\sqrt{{\left(w_1\right)}^2+{\left(w_2\right)}^2+{\left(w_3\right)}^2}$$

(7)

As illustrated by Umayal Sundari and Veeramanipriya [72], the independent variables that had an impact on the measures were identified by using Equation (8).

$${\mathcal{W}}_n=\sqrt{{\left(w_{instrument}\right)}^2+{\left(w_{reading}\right)}^2}$$

(8)

Equation (9) provided the total measurement errors for the various parameters.

$${\mathcal{W}}_{Total}=\sqrt{{\left(w_{tempertature}\right)}^2+{\left(w_{humidity}\right)}^2+{\left(w_{solar}\right)}^2+{\left(w_{air speed}\right)}^2+{\left(w_{scale}\right)}^2}$$

(9)

where $w$ = independent variable affecting measurement.

Using Equation (9), the total uncertainties in the sensors’ reading errors and measurement devices were computed, and the result was ±1.61%. This value is small when compared to the acceptable range of ±10% as established by Rulazi et al. [73].

2.4. Performance Analysis of the SDs

2.4.1. Moisture Content (MC)

The MC was estimated by heating a TF sample at 105 ± 1 °C in a hot-air electrical oven for 10 h, based on the method described by AOAC [74]. The initial and final MCs of the TF samples on a wet basis were estimated using Equation (10), as mentioned by Eke [68].

$${\mu }_w=\left[{\displaystyle \frac{W_w-W_d}{W_w}}\right]\times 100$$

(10)

where ${\mu }_w$ is the MC on a wet basis (w.b.); $W_w, \mathrm{a}\mathrm{n}\mathrm{d} W_d$ are the initial and dried weights of the TF sample, kg.

The MC on a dry basis (μ_d) of dried TF was calculated based on Equation (11), as reported by Tesfaye and Habtu [75].

$${\mu }_d=\left[{\displaystyle \frac{W_w-W_d}{W_d}}\right]\times 100$$

(11)

2.4.2. Drying Rate (DR)

The DR is the amount of moisture extracted from the dried TF samples to obtain the right MC in a certain amount of time. The proportion of residual solids might be computed by measuring the MC of hourly random samples of TF. It was computed using Equation (12), as stated by Etim et al. [76].

$$DR={\displaystyle \frac{M_{w1}-M_{w2}}{\Delta t}}$$

(12)

where $M_{w1}\mathrm{a}\mathrm{n}\mathrm{d} M_{w2}$ are the weight loss of the dried samples between two adjacent measurements, g, and $\Delta t$ is the time consumed between two adjacent measurements, h.

2.4.3. Thermal Balance of PV System

The PV system’s efficiency is determined by taking into account the energy required by the AC suction fans, control circuit, and measurement electronic circuit, as reported by Shen et al. [77] and as shown in Equation (13).

$$P_{output}=V_{oc}\times I_{sc}$$

(13)

where $P_{output}$ is the output power, W; $V_{oc}$ is the open circuit voltage, V; and $I_{sc}$ is the short-circuit current, A.

According to Qi et al. [78], the fill factor (FF) may be defined as the ratio of the maximum output energy of the PV system (P_max) to the output energy. The fill factor was calculated according to Equation (14).

$$FF={\displaystyle \frac{P_{max}}{V_{oc}\times I_{sc}}}$$

(14)

where $P_{max}$ is the maximum output power, W.

2.4.4. Thermal Analysis of the Solar Collectors

The SC efficiency is the ratio of the input energy absorbed from the SR to the output energy consumed to raise the AT. It was estimated according to Usub et al. [79]. The energy input from the SC (E_{input. coll,} J) was calculated according to Equation (15).

$$E_{input.coll}=A_{coll}{\int }_0^t{Ins}_{coll}\left(t\right)dt$$

(15)

where $E_{input.coll}$ is the input power, W; $A_{coll}$ is the SC surface area, m².

The energy output from the SC (E_{input. coll,} J) was calculated according to Equations (16) and (17).

$$E_{output.coll}={\int }_0^t{m}_a \left(t\right)\times C_{p,a}\times \left(T_{a,in}-T_{a,out}\right)dt$$

(16)

where $E_{output.coll}$ is the output power, W; $m_a$ is the air mass flow rate, kg/s; $C_{p,a}$ is the specific heat of air, kJ/kg.K; and $T_{a,in}-T_{a,out}$ is the difference between the inlet and outlet air temperatures, k.

$$m_a={\rho }_a \times V_a={\rho }_a \times u_a \times A_{\mathit{coll}}$$

(17)

where ρ_a is the air density, kg/m³; V_a is the air volume, m³; u_a is the air speed, m/s.

As mentioned above, the SC efficiency (η_coll) was calculated based on Equation (18).

$${\eta }_{coll}={\displaystyle \frac{E_{output.coll}}{E_{input.coll}}}\times 100$$

(18)

2.4.5. Economic Analysis

The annualized investment cost ($C_a$) of the SD integrated with an ASCT was calculated using parameters in Equation (19).

$$C_a=C_{ac}+C_m-V_a$$

(19)

where $C_{ac}$ is the annualized capital cost of the SD; $C_m$ is the maintenance costs, which are taken as 3% of the annual capital cost; and $V_a$ is the salvage value of the SD, which is taken as 8% of the annual capital cost.

$$C_{ac}=C_{cc}\times F_c$$

(20)

$$F_c={\displaystyle \frac{{d(1+d)}^n}{{(1+d)}^n-1}}$$

(21)

where $C_{cc}$ is the total capital cost of the SD, $F_c$ is the capital recovery factor, $d$ is the interest rate (equal to 20%), and n is the operating life equal to 5 years for the SD and the PV system.

The drying cost per kg of TF inside the SD ($C_s$) is calculated using Equation (22) [45,46,47].

$$C_s={\displaystyle \frac{C_a}{M_y}}$$

(22)

The amount of product dried inside the dryer per year ($M_y$) is calculated as

$$M_y={\displaystyle \frac{M_d\times D}{D_d}}$$

(23)

where $M_d$ is the amount of TF dried inside the SD per batch, D is the number of days in which the SD operates in a year, and $D_d$ is the drying period per batch.

The cost of 1 kg of the dried product is calculated using Equation (24) [45,46,47].

$$C_{ds}=C_{dp}+C_s$$

(24)

where $C_{dp}$ is the cost of fresh TF per kg of dried product, which is calculated as

$$C_{dp}=C_{fd}\times {\displaystyle \frac{M_f}{M_d}}$$

(25)

where $M_f$ is the quantity of fresh TFs loaded inside the SD, and $C_{fd}$ is the cost of fresh TFs.

The savings obtained per kg of dried product ($S_{kg}$) is given by

$$S_{kg}={SP}_c-C_{ds}$$

(26)

where ${SP}_c$ is the selling price of dried TFs per kg.

The savings obtained from the SD per batch of TF drying ($S_b$) is given by

$$S_b=S_{kg}\times M_d$$

(27)

While the savings obtained from the SD per day ($S_d$) is given by

$$S_d={\displaystyle \frac{S_b}{D}}$$

(28)

The savings obtained from the SD after j number of years is given by

$$S_j=S_d\times D\times {\left(1+j\right)}^{j-1}$$

(29)

The payback time (N) for the SD integrated with an ASCT is calculated using Equation (30) [45,46,47].

$$N={\displaystyle \frac{ln\left[1-\frac{C_{cc}}{S_1}(d-i)\right]}{\ln \left(\frac{1+i}{1+d}\right)}}$$

(30)

where $i$ is the inflation rate (equal to 39.7%), and $S_1$ is the savings obtained from the SD after the first year.

2.5. Statistical Analysis

Some statistical parameters were used, including coefficient of determination (R²), where R² denotes the level of the relationship between the measured data. The R² values and significance levels were determined at 0.001. The means of each parameter were compared between two types of solar dryers under different tomato thicknesses and air velocities using Duncan’s test at a significant level of 5% (SPSS 22, SPSS Inc., Chicago, IL, USA). Mean values with the same letter did not differ significantly at p ≤ 0.05.

3. Results and Discussions

Using the above-mentioned equations and methodologies, an SD integrated with an ASCT and an SD integrated with an FSC were designed, manufactured, and had their performance assessed for drying TFs with three different STs (8.0, 6.0, and 4.0 mm) and three different AVs (2.0, 1.5, and 1.0 m/s). TF samples for each ST were spread over a single drying tray on both the SD integrated with an ASCT and the SD integrated with an FSC at the same time and with the same AVs. After that, the drying trays were hinged at the end of the electronic balance on the DCh. Figure 9 and Figure 10 show the difference between the TF before and after the drying process.

3.1. Initial MC

The initial MC of the TF samples was 92 ± 2% on a wet basis. This value is in agreement with Mahmoud and Elwkeel [5] and Djebli et al. [22], who stated that the initial moisture content ranged between 89.33 and 93.6% on a wet basis (w.b.). The experiment on the MC of the TF samples was conducted at the laboratory of Food Science and Technology, Aswan University.

3.2. Estimation of Drying Weather Conditions

The SR data were obtained from the weather station inside Luxor city, while ambient AT and air RH were measured using the developed measuring electronic circuit, as shown in Figure 8. Figure 11 shows variations in the SR and ambient AT and RH of air, where it was found that the low and high SR values were 193 and 898 W/m², respectively, on the first day of the field tests (29 August 2023) from 8.00 a.m. to 6.00 p.m., where the maximum SR was recorded at 1.00 p.m., as well as the total daily SR of 6856.0 watts/m². In addition, the highest value of the ambient AT was 43.6 °C, and the corresponding RH was 18.3% at 2.00 p.m.

Figure 12 demonstrates the AT and RH of ambient air, air inside DCh, and both the ASCT and FSC on the first day of the field experiments. The differences between the AT curves and RH curves are insignificant for the field experimental period. The obtained data show a significant increase in AT inside the SD integrated with an ASCT by 4.9 °C at 2.00 p.m. compared with the SD integrated with an FSC.

3.3. Effect of AV on MC and DR

To clarify the influence of the AV on the performance of the SD integrated with an ASCT and the SD integrated with an FSC, the regression analysis of the SD integrated with an ASCT and the SD integrated with an FSC is performed. A preliminary look at the test findings indicates that for an AV range of 1.5 m/s to 2.0 m/s, the field evaluation results are relatively stable for both the SD integrated with an ASCT and the SD integrated with an FSC. After the previously mentioned range for each SD, the test results vary gradually. The previous trend of the drying curve agrees with [20], where it is reported that the DR of TFs was initially low but subsequently rose with increasing SR at all levels of ST and AV. According to Kocabiyik et al. [80], there was a brief period of rising or heating up at the start of the drying process as a result of the impact of SR, and the DR demonstrated a brief increasing trend at this time. After the heating period, the DRs were reduced gradually.

The AV has a greater impact on the performance of both the SD integrated with an ASCT and the SD integrated with an FSC, so the relationship between MC and AV is studied and presented at different STs in the segmented regression analysis between the output data for each curve. Figure 13 is the regression analysis of two types of SDs on two evaluation parameters (MC and DR).

Figure 13 demonstrates that the two evaluation parameters of the two SDs are similar in the law of change of AV: within the range of 1.5 m/s to 2.0 m/s, the fluctuation of the presented results is relatively stable; at an AV of 1.0 m/s, the presented results fluctuate greatly. In addition, from the presented results in Figure 13, we find that there is no significant difference between the drying curves for the SD integrated with an ASCT and the SD integrated with an FSC at the tested AV, while there is a significant difference for both MC curves and DR curves between the SD integrated with an ASCT and the SD integrated with an FSC. The TF samples dried on the SD integrated with an ASCT reached EMC after 5–8 h, but the TF samples dried on the SD integrated with an FSC reached EMC after 6–10 h. This means that using the developed ASCT led to a decrease in the time required for drying TF slices by 20–25%.

It is obvious that as the MC increases, the DR steadily drops. The DRs were greater at the start of the operation and subsequently dropped as the samples’ MC dropped. Similar DR trends have been reported for carrots, potatoes, peaches, and red peppers [81,82,83], respectively. Furthermore, all the drying processes are seen to take place throughout the decreasing DR period, and the TSs did not show a consistent DR period. These outcomes match up with those of previous TF investigations [84].

3.4. Effect of ST on MC and DR

To clarify the influence of the ST on the presented results of the SD integrated with an ASCT and the SD integrated with an FSC, the regression analysis of the ST is performed. Similarly, based on the preliminary analysis of the test results, in the range of 4.0–6.0 mm, the test results vary greatly, and the ST has a greater impact on the SD’s result accuracy at all levels of AV; for a ST of 8.0 mm, the test results are relatively stable, and the ST has a small effect on the result accuracy of the SD’s.

Figure 14 shows the regression analysis of two types of SDs on two evaluation parameters. It can be seen from Figure 14 that the two evaluation parameters of the two SDs are not similar in the law of change of the ST: in the range of the 4.0–6.0 mm ST, the presented results fluctuate greatly, but at an 8.0 mm ST, the change in the presented results is relatively stable. In addition, from the presented results in Figure 14, we find that there is a significant difference between the drying curves of the SD integrated with an ASCT and the SD integrated with an FSC at the tested STs, as well as a significant difference for both MC curves and DR curves between the SD integrated with an ASCT and the SD integrated with an FSC.

The TF samples dried on the SD integrated with an ASCT reached EMC after 5, 7, and 8 h for 4.0, 6.0, and 8.0 mm STs, respectively, but the TF samples dried on the SD integrated with an FSC reached EMC after 6, 9, and 10 h for 4.0, 6.0, and 8.0 mm ST, respectively, at the same operation conditions. This means that using the developed ASCT led to a decrease in the time required for drying TF slices by 20–25%. In addition, drying TFs at a 4.0 mm ST reached EMC faster than at 6.0 mm and 8.0 mm STs, decreasing the DT by 50%, as shown in Figure 14.

Table 4. Statistical analysis of the variation values of the tomato parameters (weight, drying rate, moisture content) under different tomato thicknesses, air velocities, and two types of solar dryers **.

Time	Weigh of Sample						Drying Rate						Moisture Content
	Slice Thickness = 4.0 mm
	Air Velocity (1 m/s)		Air Velocity (1.5 m/s)		Air Velocity (2 m/s)		Air Velocity (1 m/s)		Air Velocity (1.5 m/s)		Air Velocity (2 m/s)		Air Velocity (1 m/s)		Air Velocity (1.5 m/s)		Air Velocity (2 m/s)
	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT
8	195 b	195 b	200 ab	200 ab	204.67 a	205 a	0 p	0 p	0 p	0 p	0 p	0 p	92.3 a	92.3 a	92.3 a	92.3 a	92.2 a	92.3 a
9	120 e	135 d	130 d	135 d	145 c	150 c	75 a	60 d	70 b	65 c	59. d	55 e	87.5 a–c	88.9 ab	88.2 a–c	88.6 a–c	89.1 ab	89.5 b
10	75 g	115 e	70 gh	85 f	75 g	90 f	45 g	20 l	60 d	50 f	70 b	60 d	79.9 de	86.9 a–c	77.9 de	81.9 cd	78.9 de	82.5 b–d
11	40 l	75 g	40 l	65 hi	45 jk	60 i	35 i	40 h	30 j	20 l	30 j	30 j	62.3 i	79.9 de	61.4 i	76.3 de	64.8 hi	73.6 ef
12	25 m	50 j	21.3 mn	40 l	30 m	40 l	15 m	25 k	18.7 l	25 k	15 m	20 l	39.8 k	69.8 gh	26.1 l	61.4 i	46.8 j	60.3 i
13	20 mn	25 m	17.6 n	25 m	20 mn	30 m	5 o	25 k	3.7 op	15 m	10 n	10 n	24.4 l	39.6 k	12.8 m	38.3 k	20.5 l	47.0 j
8	265 b	260 b	188 c–e	250 b	303 a	300 a	0 g	0 g	0 g	0 g	0 g	0 g	92.3 a	92.3 a	81.8 c–f	92.3 a	92.2 a	92.3 a
Time	Slice thickness = 6.0 mm
	Air velocity (1 m/s)		Air velocity (1.5 m/s)		Air velocity (2 m/s)		Air velocity (1 m/s)		Air velocity (1.5 m/s)		Air velocity (2 m/s)		Air velocity (1 m/s)		Air velocity (1.5 m/s)		Air velocity (2 m/s)
	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT	ASCT
9	190 c–e	200 cd	180 de	200 cd	220 c	280 ab	75 a	60 a–c	8 fg	50 a–e	83 a	20 d–g	89.3 a–c	89.9 ab	89.1 a–d	90.4 ab	89.3 a–c	91.8 a
10	110 h–j	145 fg	105 h–j	160 ef	165 ef	215 c	80 a	55 a–d	75 a	40 b–f	55 a–d	65 ab	81.5 e–h	86.2 a–f	81.3 e–h	87.9 a–e	85.8 a–f	89.2 a–c
11	80 j–m	120 g–i	75 k–n	125 g–i	115 g–i	164 ef	30 b–g	25 c–g	30 b–g	34.66 b–g	49.67 a–e	51 a–e	74.5 g–j	83.3 b–f	73.7 i–k	84.6 a–f	79.6 f–i	85.91 a–f
12	50 m–q	95 i–l	45 n–q	105 h–j	80 j–m	135 f–h	30 b–g	25 c–g	30 b–g	20 d–g	35 b–g	29 c–g	59.2 no	7898 f–i	56.3 op	81.6 d–g	70.6 j–m	82.9 b–f
13	35 pq	70 l–o	40 o–q	75 k–n	65 l–p	110 h–j	15 e–g	25 c–g	5 fg	30 b–g	15 e–g	25 c–g	41.4 r	71.4 j–l	50.8 pq	74.3 h–j	63.7 mn	78.9 f–i
14	30 pq	60 m–q	30 pq	58 m–q	50 m–q	75 k–n	5 fg	10 fg	10 fg	17 e–g	15 e–g	35 b–g	31.9 s	66.5 lm	34.1 s	66.8 k–m	52.9 oq	69.1 j–m
15	25 q	35 pq	25 q	38 o–q	35 pq	45 n–q	5 fg	25 c–g	5 fg	20 d–g	15 e–g	30 b–g	17.6 t	42.0 r	21.4 t	48.75 q	32.56 s	48.5 q
Time	Slice thickness = 8.0 mm
	Air velocity (1 m/s)		Air velocity (1.5 m/s)		Air velocity (2 m/s)		Air velocity (1 m/s)		Air velocity (1.5 m/s)		Air velocity (2 m/s)		Air velocity (1 m/s)		Air velocity (1.5 m/s)		Air velocity (2 m/s)
	ASCT	FSC	ASCT	FSC	ASCT	FSC	ASCT	FSC	ASCT	FSC	ASCT	FSC	ASCT	FSC	ASCT	FSC	ASCT	FSC
8	375 a	370 a	380 a	375 a	375 a	370 a	0 m	0 m	0 m	0 m	0 m	0 m	92.4 a	92.2 a	92.3 a	92.4 a	92.3 a	92.3 a
9	315 de	340 b	310 ef	330 bc	300 fg	325 cd	60 c–f	30 h–k	70 a–d	45 f–g	75 a–c	45 f–g	90.8 a	91.6 a	90.6 a	91.3 a	91.4 a	92.2 a
10	250 mn	295 hi	260 lm	278 jk	245 no	285 ij	65 b–e	45 f–g	50 e–g	52 e–g	55 d–g	40 g–i	88.5 a–c	90.4 a	88.7 a–c	89.6 a	89.2 a–c	9.01 a
11	175 pq	270 kl	175 pq	245 no	165 pq	240 no	75 a–c	25 i–k	85 a	33 h–j	80 a–b	45 f–g	83.5 ef	89.5 ab	83.3 ef	88.2 a–d	83.5 ef	89.1 a–c
12	120 s–v	195 o–q	110 v	185 o–q	105 vw	180 o–q	55 d–g	75 a–c	65 b–e	60 c–f	60 c–f	60 c–f	75.9 gh	85.4 b–e	73.4 hi	84.4 de	73.5 hi	85.2 c–e
13	90 x–y	140 r	80 x–y	130 r–t	85 x–y	135 rs	30 h–k	55 d–g	30 h–k	55 d–g	20 j–l	45 f–g	67.9 jk	79.6 fg	63.4 m–o	77.8 g	67.0 k–m	79.9 fg
14	75 x–y	125 s–v	65 xyz	110 v	70 x–y	115 s–v	15 k–m	15 k–m	15 k–m	20 j–l	15 k–m	20 j–l	61.4 no	77.2 gh	54.9 q	73.6 hi	59.8 op	77.2 gh
15	50 w	100 vw	40 z	80 x–y	55 wz	85 x–y	25 i–k	25 i–k	25 i–k	30 h–k	15 k–m	30 h–k	41.8 r	71.5 ij	26.34 s	63.9 l–n	48.5 q	67.5 kl
16	33 z	75 x–y	35 z	55 wz	33 z	65 yz	17 j–i	25 i–k	5 lm	25 i–k	22 jk	20 j–l	12.23 t	61.9 no	15.7 t	47.3 q	13.5 t	57.2 pq

** Mean values with the same letter did not differ significantly at p ≤ 0.05.

shows that there is a significant difference in the values of the weight, drying rate, and moisture content of tomato for different thicknesses and velocities. There were clear differences between the values of weight, drying rate, and moisture content of tomato under two types of solar dryers. For example, the drying rate values of the samples at a 4 mm thickness and a 1 m/s air velocity for the ASCT was 75, and for the FSC, it was 60. Similarly, for the moisture content under the same conditions, the values were 87.5 for ASCT and 88.9 for FSC. Samimi-Akhijahani et al. [20] stated that the effect of ST was more significant than AV on DT. Also, using ASCT reduces the DT by about 16.6–36.6% compared to traditional drying systems. The variation in the DR can be assigned to a difference in DR because of variations in AT, RH, sunshine hours, SR, and wind speed, as reported by Shahi et al. [85].

3.5. Thermal Balance of PV System

Figure 15 shows the thermal balance of the PV panel, where both V_oc and I_sc were measured each hour. The efficiency was calculated by the input and output powers. The peak efficiency, V_oc, I_sc, and P_output were 17.45%, 19.78 V, 3.93 A, and 72 W, respectively. Jaiganesh et al. [86] reported that the efficiency of the PV panel ranged between 9.52% and 14.5%; Yamamoto et al. [87] and Haschke et al. [88] reported that the PV efficiency ranged between 24 and 27%. Ho et al. [89] and Müller et al. [90] stated that the currently produced commercial PV systems have an efficiency of between 14% and 19%.

3.6. Thermal Analysis of SC

Figure 16 shows the thermal analysis of the SD integrated with an ASCT compared to the SD integrated with an FSC under the same operating conditions and SR intensities on the first day of the field experiments.

The input energy refers to the solar energy that is gained from the sun’s rays, while the output energy is calculated from the difference between the ambient AT and the hot AT inside the SC. The input energy depends directly on the SR falling on the SC per hour and the surface area of the SC. The illustrated data in Figure 16 show that the input energy, output energy, and SC efficiency increase gradually in the daily drying period. The data in the same figures indicate that the maximum efficiency of the SC integrated with an ASCT and the SC integrated with an FSC was 83.2% and 61.6%, respectively, at 2.00 p.m., where the efficiency of the SC integrated with an ASCT increased by about 21.6% compared to the SC integrated with an FSC. In addition, the minimum efficiency of the SC integrated with an ASCT and the SC integrated with an FSC at 8.00 a.m. and 6.00 p.m. was (44.3 and 47.7%) and (25.7 and 24.5%), respectively. The SD integrated with an ASCT showed the best field performance compared to the SD integrated with an FSC during the day.

Table 5. Some studies in the literature examining the TE of the SC.

Reference	DC Type	TE of Traditional SC	TE of Tracking SC	Increasing the Ratio of TE
ElGamal et al. [19]	Solar air heater	--	45%	--
Bhowmik [31]	Flat-plate SC	--	--	10%
Zheng et al. [32]	Parabolic concentrator SC	--	60.5%	--
Zou et al. [33]	Small-sized parabolic trough SC	--	67%	--
Chamsa-ard et al. [34]	Heat pipe evacuated tube with an SC	--	78%.	--
Rittidech et al. [35]	Circular glass tube SC	--	76%	--
Wei et al. [36]	Flat-plate heat SC	--	66%	--
Verma et al. [37]	Single spiral-shaped SC tube	--	--	21.94%
Ramachandran et al. [38]	Flat-plate SC integrated with Scheffler solar concentrator	--	--	6%
Das and Akpinar [39]	SD integrated with ASCT	--	75.7%	--
Current study	Flat-plate SC integrated with ASCT	61.6%	83.2%	21.6%

presents a comparison between the SD designed and evaluated in the current study and previous studies in the literature that examined the thermal energy (TE) of various types of SCs. We find that the TE of the previous studies ranged between 45% and 78%, where the highest TE is observed in Chamsa-ard et al. [34] who used an SD integrated with a heat pipe evacuated tube with an SC. The highest TE of the current study was 83.2% when we used an ASCT, compared with 61.6% when using an FSC (traditional SC). Due to the use of an ASCT, the TE increased by an average of 21.6% under the same operating conditions.

3.7. Economic Analysis

The annual cost of the SD depends on its capital cost, maintenance cost, operational cost, and salvage value [47]. All the costs associated with the SD integrated with an ASCT are shown in

Table 6. Different costs related to the two SDs.

Cost Parameters	SD Integrated with FSC (USD)	SD Integrated with ASCT (USD)
Capital Cost of Dryer	108.57	125
Annual Capital Cost	36.262	41.75
Annual Maintenance Cost	1.088	1.2525
Annual Salvage Value	2.901	3.34
Annual Cost of SD	34.449	39.662

. The capital cost of the SD is high because of the use of electronic parts. Due to this, the annual cost of the SD integrated with an ASCT is high. In the case of the SD integrated with an FSC, the capital cost is 13.14% less than the SD integrated with an ASCT, so its annual cost is also reduced to 13.14% of the SD integrated with an ASCT.

The economic parameters that depend on the TFs dried inside the SD are shown in

Table 7. Economic analysis of the two SDs.

Economic Analysis Parameters	SD Integrated with FSC	SD Integrated with ASCT
Mass of product dried per batch (kg)	0.5	0.5
Quantity of dried product annually (kg)	104.3	124.1
Drying Cost of per kg (USD)	0.33	0.319
Cost of 1 kg fresh product (USD)	0.285	0.285
Mass of fresh product per batch (kg)	5.0	5.0
Cost of fresh product per kg of dried product (USD)	2.85	2.85
Cost of 1 kg of crop dried inside the dryer (USD)	3.18	3.17
Selling price per kg (USD)	5.0	5.0
Saving per Kg (USD)	1.82	1.83
Saving per batch (USD)	0.91	0.915
Saving per day (USD)	0.561	0.627
Saving after 1 year (USD)	204	228.9
Payback Time (Years)	0.655	0.672

. The payback time of the SD depends on the capital cost of the SD and the savings obtained from the SD per year [47]. In the case of an ASCT, the DT of the selected crops is less compared to the case of an FSC. Thus, a greater quantity of the dried TFs can be dried on the SD, and for the ASCT, a greater quantity of dried TFs can be sold in the market. Thus, the savings from the SD integrated with an ASCT are greater than for the SD integrated with an FSC. As shown in Table 7, however, the payback time for both SDs is very close, but a greater quantity of dried TFs is available in the markets.

4. Conclusions and Future Works

Comparative tests were carried out between an SD integrated with an ASCT and an SD integrated with an FSC for drying the most popular TF variety grown in the Luxor region, Egypt, to study some operation parameters that affect the performance of SDs, where three AVs of 1.0, 1.5, and 2.0 m/s and three STs of 4.0, 6.0, and 8.0 mm were used. The obtained results of the current study showed that there was a significant difference between the drying curves, MC curves, and DR curves for the SD integrated with an ASCT and the SD integrated with an FSC at field tests, where using the developed ASCT led to a decrease in the time required for drying the TF slices by 20%–25%. On the other hand, the drying of TFs at a 4.0 mm ST reached EMC faster than those at 6.0 mm and 8.0 mm STs and decreased the drying time by 50%. Furthermore, the efficiency of the SD integrated with an ASCT increased by about 35.06% compared to the SD integrated with an FSC. Based on the economic analysis of both drying systems, we found that the capital cost was 13.14% less than the SD integrated with an ASCT, so its annual cost was also reduced to 13.14% of the SD integrated with an ASCT. However, the payback time for both solar dryers was very close, but greater quantities of the dried TF would be available in the markets.

Finally, the results of the current study show the good performance of the ASCT used, which will lead us in the future to use machine learning and artificial intelligence to manufacture an intelligent automatic SD and monitor the dryer’s performance and product quality remotely.