Next Article in Journal
Mapping Circular Economy in Portuguese SMEs
Previous Article in Journal
The Impact of Environmental Courts on Green Total Factor Productivity in Chinese Cities
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Abdallah Elshawadfy Elwakeel
1,*,
Mohsen A. Gameh
2,
Awad Ali Tayoush Oraiath
3,
Ahmed S. Eissa
4,
Salah Elsayed
5,6,
Wael M. Elmessery
7,8,
Mostafa B. Mostafa
1,
Sadeq K. Alhag
9,
Laila A. Al-Shuraym
10,
Moustapha Eid Moustapha
11,
Ahmed Elbeltagi
12,
Ali Salem
13,14,* and
Aml Abubakr Tantawy
15
1
Agricultural Engineering Department, Faculty of Agriculture and Natural Resources, Aswan University, Aswan 81528, Egypt
2
Soils and Water Department, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
3
Department of Agricultural Engineering, Faculty of Agriculture, Omar Al Mukhtar University, Al Bayda 991, Libya
4
Agricultural Products Process Engineering Department, Faculty of Agricultural Engineering, Al-Azhar University, Cairo 11751, Egypt
5
Agriculture Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menoufia 32897, Egypt
6
New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah 64001, Thi-Qar, Iraq
7
Agricultural Engineering Department, Faculty of Agriculture, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
8
Engineering Group, Center de Investigaciones Biologicas del Noroeste, La Paz 23201, Mexico
9
Biology Department, College of Science and Arts, King Khalid University, Muhayl Asser 61913, Saudi Arabia
10
Biology Department, Faculty of Science, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia
11
Department of Chemistry, College of Science and Humanities, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
12
Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
13
Civil Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt
14
Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pécs, 7622 Pécs, Hungary
15
Food Science and Technology Department, Faculty of Agriculture and Natural Resources, Aswan University, Aswan 81528, Egypt
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(16), 7008; https://doi.org/10.3390/su16167008
Submission received: 6 July 2024 / Revised: 2 August 2024 / Accepted: 12 August 2024 / Published: 15 August 2024

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 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 = W w × M i M f 1 M f × 100
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 Cp of TF is 3.5174 ± 68.5 kJ/kg.K.
Q 1 = W w × C p × Δ T
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 × L v
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 (QT, J) was calculated using Equation (4), as mentioned in Eke and Simonyan [68]:
Q T = Q 1 + Q 2

2.1.3. The SC’s Surface Area

The SC’s surface area (Ac) required to receive SR can be calculated by Equation (5), as reported in Babar et al. [69].
A c = Q a b s η × I t × t d
where Q a b s is the total heat quantity absorbed by the solar collector, W; η 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 ( ) 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.
β = 2.66 ° + L a t  

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.
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 LDRE and Western LDRW), as shown in Figure 7.
Then, the controller compares the obtained signals from both LDR sensors (LDRE and LDRW) with the stored reference tolerance values (±5 Lux). If the LDRE sensor (fixed at the eastern side of the ASCT) is higher than the LDRW 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 LDRW sensor is higher than the LDRE 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 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].
W t h = w 1 2 + w 2 2 + w 3 2
As illustrated by Umayal Sundari and Veeramanipriya [72], the independent variables that had an impact on the measures were identified by using Equation (8).
W n = w i n s t r u m e n t 2 + w r e a d i n g 2
Equation (9) provided the total measurement errors for the various parameters.
W T o t a l = w t e m p e r t a t u r e 2 + w h u m i d i t y 2 + w s o l a r 2 + w a i r   s p e e d 2 + w s c a l e 2
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].
μ w = W w W d W w × 100
where μ w is the MC on a wet basis (w.b.); W w ,   a n d   W d are the initial and dried weights of the TF sample, kg.
The MC on a dry basis (μd) of dried TF was calculated based on Equation (11), as reported by Tesfaye and Habtu [75].
μ d = W w W d W d × 100

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].
D R = M w 1 M w 2 Δ t
where M w 1 a n d   M w 2 are the weight loss of the dried samples between two adjacent measurements, g, and Δ 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 o u t p u t = V o c × I s c
where P o u t p u t is the output power, W; V o c is the open circuit voltage, V; and I s c 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 (Pmax) to the output energy. The fill factor was calculated according to Equation (14).
F F = P m a x V o c × I s c
where P m a x 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 (Einput. coll, J) was calculated according to Equation (15).
E i n p u t . c o l l = A c o l l 0 t I n s c o l l t d t
where E i n p u t . c o l l is the input power, W; A c o l l is the SC surface area, m2.
The energy output from the SC (Einput. coll, J) was calculated according to Equations (16) and (17).
E o u t p u t . c o l l = 0 t m a   t × C p , a × T a , i n T a , o u t d t
where E o u t p u t . c o l l 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 , i n T a , o u t is the difference between the inlet and outlet air temperatures, k.
m a = ρ a   ×   V a = ρ a   ×   u a   ×   A coll
where ρa is the air density, kg/m3; Va is the air volume, m3; ua is the air speed, m/s.
As mentioned above, the SC efficiency (ηcoll) was calculated based on Equation (18).
η c o l l = E o u t p u t . c o l l E i n p u t . c o l l × 100

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 a c + C m V a
where C a c 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 a c = C c c × F c
F c = d ( 1 + d ) n ( 1 + d ) n 1
where C c c 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 = C a M y
The amount of product dried inside the dryer per year ( M y ) is calculated as
M y = M d × D D d
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 d s = C d p + C s
where C d p is the cost of fresh TF per kg of dried product, which is calculated as
C d p = C f d × M f M d
where M f is the quantity of fresh TFs loaded inside the SD, and C f d is the cost of fresh TFs.
The savings obtained per kg of dried product ( S k g ) is given by
S k g = S P c C d s
where S P 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 k g × M d
While the savings obtained from the SD per day ( S d ) is given by
S d = S b D
The savings obtained from the SD after j number of years is given by
S j = S d × D × 1 + j j 1
The payback time (N) for the SD integrated with an ASCT is calculated using Equation (30) [45,46,47].
N = l n 1 C c c S 1 ( d i ) ln 1 + i 1 + d
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 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 Voc and Isc were measured each hour. The efficiency was calculated by the input and output powers. The peak efficiency, Voc, Isc, and Poutput 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 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. 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. 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.

Author Contributions

Conceptualization, A.E.E., M.A.G., A.S.E. and A.A.T.; methodology, A.E.E., M.A.G., A.S.E. and A.A.T.; software, A.E.E. and M.B.M.; formal analysis, A.E.E., M.A.G., A.S.E., S.E. and A.A.T.; investigation, M.B.M., A.A.T.O. and W.M.E.; resources, S.K.A., L.A.A.-S., M.E.M., A.E. and A.S.; data curation, M.B.M., A.A.T.O., S.E. and W.M.E.; writing—review and editing, A.E.E., M.A.G., A.S.E. and A.A.T.; visualization, S.K.A., L.A.A.-S., M.E.M., A.E., S.E. and A.S.; supervision, A.E.E., M.A.G., A.S.E. and A.A.T.; and project administration, A.E.E., M.A.G., A.S.E., A.A.T., A.E. and A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are presented within the article.

Acknowledgments

This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R365), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number (R.G.P.2/60/45).

Conflicts of Interest

The authors declare no conflicts of interest.

Nomenclature and Abbreviation

Nomenclature
MiInitial moisture content of TF sample S 1 The savings obtained from the SD after the first year
MfFinal moisture content of TF sample S d The savings obtained from the SD per day
WwInitial total weight of TF sample, kg C s The drying cost per kg of TF
ΔTChange in air temperature inside the drying room M y The amount of product dried inside the dryer per year
WdFinal weight of tomato sample, g M d The amount of TF dried inside the SD per batch
CpSpecific heat of tomato, kJ/kg.KDThe number of days the SD operates in a year
MwAmount of moisture removed from TF, g D d The drying period per batch
L v Latent heat of vaporization, (J/kg), of water C d p The cost of fresh TF per kg of dried product
V o c Open-circuit voltage, V C f d The cost of fresh TF
ηEfficiency of the glass cover,% M f The quantity of fresh TF loaded inside the SD
S b The savings obtained from the SD per batch S k g
S P c
Savings obtained per kg of dried product
The selling price of dried TF per kg
AcThe surface area of the solar collector, m2Abbreviation
t d Total sunshine hours available on a particular dayTSTomato slice
nThe operating lifeTFTomato fruit
Q a b s Total heat quantity absorbed by the solar collector, WRHRelative humidity
NThe payback timeMCMoisture content
Lat ∅The latitude of the collector location, truly facing southSDSolar dryer
i The inflation rateSCSolar collector
I s c Short-circuit current, AIoTInternet of things
AcollSC surface area, m2OSDOpen-sun drying
maAir mass flow rate, kg/sDRDrying rate
uaAirspeed, m/sMCMoisture content
ρaAir density, kg/m3SRSolar radiation
InscollSolar intensity, W/m2ASCTAutomatic solar collector tracker
w Independent variable affecting the measurementFSCFixed solar collector
C a The annualized investment costPVPhotovoltaic
C a c The annualized capital costTEThermal efficiency
C m The maintenance costsDTDrying time, h
V a The salvage value of the SDDChDrying chamber
C c c The total capital cost of the SDDPDrying process
ItTotal solar radiation, W/m2AVAir velocity

References

  1. Global Production of Vegetables in 2022. Available online: https://www.statista.com/statistics/264065/global-production-of-vegetables-by-type/ (accessed on 16 July 2024).
  2. Wang, S.; Qiang, Q.; Xiang, L.; Fernie, A.R.; Yang, J. Targeted Approaches to Improve Tomato Fruit Taste. Hortic. Res. 2023, 10, uhac229. [Google Scholar] [CrossRef] [PubMed]
  3. Sonawane, P.D.; Gharat, S.A.; Jozwiak, A.; Barbole, R.; Heinicke, S.; Almekias-Siegl, E.; Meir, S.; Rogachev, I.; Connor, S.E.O.; Giri, A.P. A BAHD-Type Acyltransferase Concludes the Biosynthetic Pathway of Non-Bitter Glycoalkaloids in Ripe Tomato Fruit. Nat. Commun. 2023, 14, 4540. [Google Scholar] [CrossRef] [PubMed]
  4. Elwakeel, A.E.; Gameh, M.A.; Eissa, A.S.; Mostafa, M.B. Recent Advances in Solar Drying Technology for Tomato Fruits: A Comprehensive Review. Int. J. Appl. Energy Syst. 2024, 6, 37–44. [Google Scholar] [CrossRef]
  5. Mahmoud, W.A.E.-M.; Elwakeel, A. elshawadfy Study on Some Properties of Tomato Fruits for Natural Sun Drying. J. Soil. Sci. Agric. Eng. 2021, 12, 763–767. [Google Scholar]
  6. Naeem, M.; Zhao, W.; Ahmad, N.; Zhao, L. Beyond Green and Red: Unlocking the Genetic Orchestration of Tomato Fruit Color and Pigmentation. Funct. Integr. Genom. 2023, 23, 243. [Google Scholar] [CrossRef] [PubMed]
  7. Corzo, O.; Bracho, N.; Alvarez, C. Water Effective Diffusion Coefficient of Mango Slices at Different Maturity Stages during Air Drying. J. Food Eng. 2008, 87, 479–484. [Google Scholar] [CrossRef]
  8. El-Mesery, H.S.; Farag, H.A.; Kamel, R.M.; Alshaer, W.G. Convective Hot Air Drying of Grapes: Drying Kinetics, Mathematical Modeling, Energy, Thermal Analysis. J. Therm. Anal. Calorim. 2023, 148, 6893–6908. [Google Scholar] [CrossRef]
  9. Georgaki, E.; Nifakos, K.; Kotsiras, A.; Fanourakis, D.; Tsaniklidis, G.; Delis, C.; Spiliopoulos, I.K. Comparison of Nutrient Composition and Antioxidant Activity of Hydroponically Grown Commercial and Traditional Greek Tomato Cultivars. Horticulturae 2023, 9, 163. [Google Scholar] [CrossRef]
  10. Owureku-Asare, M.; Ambrose, R.P.K.; Oduro, I.; Tortoe, C.; Saalia, F.K. Consumer Knowledge, Preference, and Perceived Quality of Dried Tomato Products in Ghana. Food Sci. Nutr. 2017, 5, 617–624. [Google Scholar] [CrossRef]
  11. Bosona, T.; Gebresenbet, G. Life Cycle Analysis of Organic Tomato Production and Supply in Sweden. J. Clean. Prod. 2018, 196, 635–643. [Google Scholar] [CrossRef]
  12. Al Maiman, S.A.; Albadr, N.A.; Almusallam, I.A.; Al-Saád, M.J.; Alsuliam, S.; Osman, M.A.; Hassan, A.B. The Potential of Exploiting Economical Solar Dryer in Food Preservation: Storability, Physicochemical Properties, and Antioxidant Capacity of Solar-Dried Tomato (Solanum Lycopersicum) Fruits. Foods 2021, 10, 734. [Google Scholar] [CrossRef] [PubMed]
  13. Elwakeel, A.E.; Ahmed, S.F.; Zein, A.M.; Nasrat, L. Design and Evaluation of A Self-Propelled Field Sprayer to Be Operated and Controlled Remotely. Al-Azhar J. Agric. Eng. 2022, 2, 40–52. [Google Scholar] [CrossRef]
  14. Zhang, J.; Zhong, A.; Huang, G.; Yang, M.; Li, D.; Teng, M.; Han, D. Enhanced efficiency with CDCA co-adsorption for dye-sensitized solar cells based on metallosalophen complexes. Sol. Energy 2020, 209, 316–324. [Google Scholar] [CrossRef]
  15. Zhu, C.; Wang, M.; Guo, M.; Deng, J.; Du, Q.; Wei, W.; Ashraf Talesh, S.S. Optimizing solar-driven multi-generation sys-tems: A cascade heat recovery approach for power, cooling, and freshwater production. Appl. Therm. Eng. 2024, 240, 122214. [Google Scholar] [CrossRef]
  16. Zhu, C.; Zhang, Y.; Wang, M.; Deng, J.; Cai, Y.; Wei, W.; Guo, M. Optimization, validation and analyses of a hybrid PV-battery-diesel power system using enhanced electromagnetic field optimization algorithm and ε-constraint. Energy Rep. 2024, 11, 5335–5349. [Google Scholar] [CrossRef]
  17. Poonia, S.; Singh, A.K.; Santra, P.; Mishra, D. Design, Development and Performance Evolution of A Low-Cost Solar Dryer. In Concentrated Solar Thermal Energy Technologies: Recent Trends and Applications; Springer: Singapore, 2018; pp. 219–223. [Google Scholar]
  18. Shimpy; Kumar, M.; Kumar, A. Designs, Performance and Economic Feasibility of Domestic Solar Dryers. Food Eng. Rev. 2023, 15, 156–186. [Google Scholar] [CrossRef]
  19. ElGamal, R.; Kishk, S.; Al-Rejaie, S.; ElMasry, G. Incorporation of A Solar Tracking System for Enhancing the Performance of Solar Air Heaters in Drying Apple Slices. Renew. Energy 2021, 167, 676–684. [Google Scholar] [CrossRef]
  20. Samimi-Akhijahani, H.; Arabhosseini, A. Accelerating Drying Process of Tomato Slices in a PV-Assisted Solar Dryer Using a Sun Tracking System. Renew. Energy 2018, 123, 428–438. [Google Scholar] [CrossRef]
  21. Ghoniem, E.Y.; Gamea, G.R. Design and Evaluation of an Enhanced Solar Dryer Using Heat Storage Unit for Tomatoes Drying. Misr J. Agric. Eng. 2014, 31, 1025–1046. [Google Scholar] [CrossRef]
  22. Djebli, A.; Hanini, S.; Badaoui, O.; Boumahdi, M. A New Approach to the Thermodynamics Study of Drying Tomatoes in Mixed Solar Dryer. Sol. Energy 2019, 193, 164–174. [Google Scholar] [CrossRef]
  23. Bakry, R.S.; Khater, E.-S.G.; Bahnasawy, A.H.; Ali, S.A. Effect of Drying Methods on the Quality of Dried Tomatoes. Misr J. Agric. Eng. 2021, 38, 155–180. [Google Scholar]
  24. Ebadi, H.; Zare, D.; Ahmadi, M.; Chen, G. Performance of a Hybrid Compound Parabolic Concentrator Solar Dryer for Tomato Slices Drying. Sol. Energy 2021, 215, 44–63. [Google Scholar] [CrossRef]
  25. Chouikhi, H.; Amer, B.M.A. Performance Evaluation of an Indirect-Mode Forced Convection Solar Dryer Equipped with a PV/T Air Collector for Drying Tomato Slices. Sustainability 2023, 15, 5070. [Google Scholar] [CrossRef]
  26. Suherman, S.; Rilna, R.M.; Afriandi, N.; Susanto, E.E.; Hadiyanto, H. Drying of Tomato Slices Using Solar Drying Method. In AIP Conference Proceedings; AIP Publishing: Melville, NY, USA, 2023; Volume 2667. [Google Scholar]
  27. Kumar, L.; Prakash, O.; Pandey, V.K.; Ahmad, A.; Das, B. Development of Finite-Element Model for Drying of Tomato Flakes under Passive Greenhouse Dryer: Experimental Validation and Optimization Potential. Energy Technol. 2024, 12, 2300920. [Google Scholar] [CrossRef]
  28. Ahmad, A.; Prakash, O.; Kumar, A.; Hussain, M.S. Drying Kinetics and Performance Analysis of Thermal Storage-Based Hybrid Greenhouse Dryer for Uniform Drying of Tomato Flakes. J. Therm. Sci. Eng. Appl. 2023, 15, 50908. [Google Scholar] [CrossRef]
  29. Ahmad, A.; Prakash, O. Performance evaluation of A Solar Greenhouse Dryer At Different Bed Conditions Under Passive Mode. J. Sol. Energy Eng. 2020, 142, 11006. [Google Scholar] [CrossRef]
  30. Dehshiri, S.S.H.; Firoozabadi, B. Comparison, Evaluation and Prioritization of Solar Photovoltaic Tracking Systems Using Multi Criteria Decision Making Methods. Sustain. Energy Technol. Assess. 2023, 55, 102989. [Google Scholar]
  31. Bhowmik, H.; Amin, R. Efficiency Improvement of Flat Plate Solar Collector Using Reflector. Energy Rep. 2017, 3, 119–123. [Google Scholar] [CrossRef]
  32. Zheng, W.; Yang, L.; Zhang, H.; You, S.; Zhu, C. Numerical and Experimental investigation on a New Type of Compound Parabolic Concentrator Solar Collector. Energy Convers. Manag. 2016, 129, 11–22. [Google Scholar] [CrossRef]
  33. Zou, B.; Dong, J.; Yao, Y.; Jiang, Y. An Experimental investigation on a Small-Sized Parabolic Trough Solar Collector for Water Heating in Cold Areas. Appl. Energy 2016, 163, 396–407. [Google Scholar] [CrossRef]
  34. Chamsa-ard, W.; Sukchai, S.; Sonsaree, S.; Sirisamphanwong, C. Thermal Performance Testing of Heat Pipe Evacuated Tube with Compound Parabolic Concentrating Solar Collector by ISO 9806-1. Energy Procedia 2014, 56, 237–246. [Google Scholar] [CrossRef]
  35. Rittidech, S.; Donmaung, A.; Kumsombut, K. Experimental Study of the Performance of a Circular Tube Solar Collector with Closed-Loop Oscillating Heat-Pipe with Check Valve (CLOHP/CV). Renew. Energy 2009, 34, 2234–2238. [Google Scholar] [CrossRef]
  36. Wei, L.; Yuan, D.; Tang, D.; Wu, B. A Study on a Flat-Plate Type of Solar Heat Collector with an integrated Heat Pipe. Sol. Energy 2013, 97, 19–25. [Google Scholar] [CrossRef]
  37. Verma, S.K.; Sharma, K.; Gupta, N.K.; Soni, P.; Upadhyay, N. Performance Comparison of Innovative Spiral Shaped Solar Collector Design with Conventional Flat Plate Solar Collector. Energy 2020, 194, 116853. [Google Scholar] [CrossRef]
  38. Ramachandran, S.; Nene, A.A.; Suyambazhahan, S. integrated System of Flat Plate Collector and Scheffler Solar Concentrator for Enhancing Thermal Efficiency and Steam Generation Rate. Int. J. Ambient Energy 2022, 43, 3154–3163. [Google Scholar] [CrossRef]
  39. Das, M.; Akpinar, E.K. Determination of Thermal and Drying Performances of the Solar Air Dryer with Solar Tracking System: Apple Drying Test. Case Stud. Therm. Eng. 2020, 21, 100731. [Google Scholar] [CrossRef]
  40. Ismail, M.A.; Kreshnaveyashadev, A.; Ramanathan, L.; Idris, M.H.; Ananda-Rao, K.; Mazlan, M.; Fairuz, N. Improving the Performance of Solar Panels by the Used of Dual Axis Solar Tracking System with Mirror Reflection. J. Phys. Conf. Series 2020, 1432, 12060. [Google Scholar] [CrossRef]
  41. Sun, S.-C.; Wang, G.-J.; Chen, H. An efficient inverse approach for reconstructing time-and space-dependent heat flux of participating medium. Chin. Phys. B 2020, 29, 110202. [Google Scholar] [CrossRef]
  42. Sun, S.; Wang, G.; Chen, H. Application of Improved Decentralized Fuzzy inference Methods for Estimating the Thermal Boundary Condition of Participating Medium. Int. J. Therm. Sci. 2020, 149, 106216. [Google Scholar] [CrossRef]
  43. Sun, S.; Chang, Z.; Ji, Y.; Wang, G.; Wei, L. inverse Estimation of Transient Heat Flux Using Sequential Function Specification Method. Heat Transf. Eng. 2024, 45, 233–243. [Google Scholar] [CrossRef]
  44. Sun, S. Simultaneous Reconstruction of Thermal Boundary Condition and Physical Properties of Participating Medium. Int. J. Therm. Sci. 2021, 163, 106853. [Google Scholar] [CrossRef]
  45. Mohammed, I.A.; Al Dulaimi, M.A.K. An Economic Analysis of the Costs of Producing Tomato Under Greenhouse in Anbar Governorate for the Agricultural Season 2019-2020. In IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2021; Volume 904, p. 12061. [Google Scholar]
  46. ELkhadraoui, A.; Kooli, S.; Hamdi, I.; Farhat, A. Experimental investigation and economic evaluation of a new mixed-mode solar greenhouse dryer for drying of red pepper and grape. Renew. Energy 2015, 77, 1–8. [Google Scholar] [CrossRef]
  47. Singh, P.; Gaur, M.K. Environmental and Economic Analysis of Novel Hybrid Active Greenhouse Solar Dryer with Evacuated Tube Solar Collector. Sustain. Energy Technol. Assess. 2021, 47, 101428. [Google Scholar] [CrossRef]
  48. Saleem, A.; Iqbal, A.; Hayat, M.A.; Panjwani, M.K.; Mangi, F.H.; Larik, R.M. The Effect of Environmental Changes on the Efficiency of the PV System. Indones. J. Electr. Eng. Comput. Sci. 2020, 18, 558–564. [Google Scholar] [CrossRef]
  49. Breyer, C.; Koskinen, O.; Blechinger, P. Profitable Climate Change Mitigation: The Case of Greenhouse Gas Emission Reduction Benefits Enabled by Solar Photovoltaic Systems. Renew. Sustain. Energy Rev. 2015, 49, 610–628. [Google Scholar] [CrossRef]
  50. Ahmed, W.; Sheikh, J.A.; Farjana, S.H.; Mahmud, M.A.P. Defects impact on PV system GHG mitigation potential and climate change. Sustainability 2021, 13, 7793. [Google Scholar] [CrossRef]
  51. Creutzig, F.; Agoston, P.; Goldschmidt, J.C.; Luderer, G.; Nemet, G.; Pietzcker, R.C. The Underestimated Potential of Solar Energy to Mitigate Climate Change. Nat. Energy 2017, 2, 1–9. [Google Scholar] [CrossRef]
  52. Wang, J.; Liu, X.; Xu, Q.; Luo, Q.; Xuan, Y. MXene Reconciles Concurrent Enhancement of Thermal Conductivity and Mechanical Robustness of Sic-Based Thermal Energy Storage Composites. DeCarbon 2023, 1, 100005. [Google Scholar] [CrossRef]
  53. Koli, P.; Kumar, R.; Dayma, Y.; Dheerata; Jonwal, M. Graphite Counter Electrode Modified Tropaeolin-O Photo-Sensitized Photogalvanic Cells for Solar Power and Storage. EcoEnergy 2024, 2, 278–298. [Google Scholar]
  54. Madakam, S.; Ramaswamy, R.; Tripathi, S. Internet of Things (IoT): A literature review. J. Comput. Commun. 2015, 3, 164–173. [Google Scholar] [CrossRef]
  55. Senapaty, M.K.; Ray, A.; Padhy, N. IoT-enabled soil nutrient analysis and crop recommendation model for precision agriculture. Computers 2023, 12, 61. [Google Scholar] [CrossRef]
  56. Elwakeel, A.E.; Wapet, D.E.M.; Mahmoud, W.A.E.; Abdallah, S.E.; Mahmoud, M.M.; Ardjoun, S.A.E.M.; Tantawy, A.A. Design and Implementation of a PV-integrated Solar Dryer Based on internet of Things and Date Fruit Quality Monitoring and Control. Int. J. Energy Res. 2023, 2023, 7425045. [Google Scholar] [CrossRef]
  57. Rohith, M.; Sainivedhana, R.; Fatima, N.S. IoT Enabled Smart Farming and Irrigation System. In Proceedings of the 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, india, 6–8 May 2021; pp. 434–439. [Google Scholar]
  58. Batuto, A.; Dejeron, T.B.; Cruz, P.D.; Samonte, M.J.C. e-poultry: An IoT poultry management system for small farms. In Proceedings of the 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA), Bangkok, Thailand, 16–21 April 2020; pp. 738–742. [Google Scholar]
  59. Elwakeel, A.E.; Mazrou, Y.S.A.; Eissa, A.S.; Okasha, A.M.; Elmetwalli, A.H.; Makhlouf, A.H.; Metwally, K.A.; Mahmoud, W.A. Design and Validation of a Variable-Rate Control Metering Mechanism and Smart Monitoring System for a High-Precision Sugarcane Transplanter. Agriculture 2023, 13, 2218. [Google Scholar] [CrossRef]
  60. Elwakeel, A.E.; Mohamed, S.M.A.; Tantawy, A.A.; Okasha, A.M.; Elsayed, S.; Elsherbiny, O.; Farooque, A.A.; Yaseen, Z.M. Design, construction and field testing of a manually feeding semiautomatic sugarcane dud chipper. Sci. Rep. 2024, 14, 5373. [Google Scholar] [CrossRef] [PubMed]
  61. Yang, L.; Nasrat, L.S.; Badawy, M.E.; Mbadjoun Wapet, D.E.; Ourapi, M.A.; El-Messery, T.M.; Aleksandrova, I.; Mahmoud, M.M.; Hussein, M.M.; Elwakeel, A.E. A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor. PLoS ONE 2024, 19, e0301294. [Google Scholar] [CrossRef] [PubMed]
  62. Nasrat, L.S.; Badawy, M.E.; Ourapi, M.A.; Elwakeel, A.E. Some Engineering Factors Affecting the Performance of an Automatic Sugarcane Seed Cutting Machine 1- introduction. Aswan Univ. J. Environ. Stud. 2024, 5, 87–100. [Google Scholar] [CrossRef]
  63. Elwakeel, A.E.; Mazrou, Y.S.A.; Tantawy, A.A.; Okasha, A.M. Multi-Purpose, Automatic System-Based RGB Color Sensor for Sorting Fruits. Agriculture 2023, 13, 1824. [Google Scholar] [CrossRef]
  64. Vijayakumar, N.; Ramya, R. The Real time monitoring of water quality in IoT environment. In Proceedings of the 2015 international Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 19–20 March 2015; pp. 1–5. [Google Scholar]
  65. Eissa, A.S.; Gameh, M.A.; Mostafa, M.B.; Elwakeel, A.E. Some Engineering Factors Affecting Utilization of Solar Energy in Drying Tomato Fruits Introduction. Aswan Univ. J. Environ. Studies 2024, 5, 52–68. [Google Scholar] [CrossRef]
  66. Etim, P.J.; Eke, A.B.; Simonyan, K.J. Design and Development of an Active indirect Solar Dryer for Cooking Banana. Sci. African 2020, 8, e00463. [Google Scholar] [CrossRef]
  67. Dissa, A.O.; Desmorieux, H.; Bathiebo, J.; Koulidiati, J. A Comparative Study of Direct and indirect Solar Drying of Mango. Glob. J. Pure Appl. Sci. 2011, 17, 273–294. [Google Scholar]
  68. Eke, A.B.; Simonyan, K.J. Development of Small Scale Direct Mode Passive Solar Dryers for Effective Drying of Tomato. J. Appl. Agric. Res. 2014, 6, 111–119. [Google Scholar]
  69. Babar, O.A.; Tarafdar, A.; Malakar, S.; Arora, V.K.; Nema, P.K. Design and performance Evaluation of A Passive Flat Plate Collector Solar Dryer for Agricultural Products. J. Food Process Eng. 2020, 43, e13484. [Google Scholar] [CrossRef]
  70. Eke, A.B. Prediction of Optimum Angle of inclination for Flat Plate Solar Collector in Zaria, Nigeria. Agric. Eng. Int. CIGR J. 2011, 13, 1–7. [Google Scholar]
  71. Gulcimen, F.; Karakaya, H.; Durmus, A. Drying of Sweet Basil with Solar Air Collectors. Renew. Energy 2016, 93, 77–86. [Google Scholar] [CrossRef]
  72. AR, U.S.; Veeramanipriya, E. Performance Evaluation, Morphological Properties and Drying Kinetics of Untreated Carica Papaya Using Solar Hybrid Dryer integrated with Heat Storage Material. J. Energy Storage 2022, 55, 105679. [Google Scholar] [CrossRef]
  73. Rulazi, E.L.; Marwa, J.; Kichonge, B.; Kivevele, T.T. Techno-economic Analysis of A Solar-Assisted Heat Pump Dryer for Drying Agricultural Products. Food Sci. Nutr. 2023, 12, 952–970. [Google Scholar] [CrossRef] [PubMed]
  74. AOAC. Official Methods of Analysis, 18th ed.; Association of Official Analytical Chemists: Gaithersburg, MD, USA, 2007. [Google Scholar]
  75. Tesfaye, A.; Habtu, N.G. Fabrication and Performance Evaluation of Solar Tunnel Dryer for Ginger Drying. Int. J. Photoenergy 2022, 2022, 6435080. [Google Scholar] [CrossRef]
  76. Etim, P.J.; Eke, A.B.; Simonyan, K.J. Effect of Air inlet Duct Features and Grater Thickness on Cooking Banana Drying Characteristics Using Active indirect Mode Solar Dryer. Niger. J. Technol. 2019, 38, 1056–1063. [Google Scholar] [CrossRef]
  77. Shen, L.; Li, Z.; Ma, T. Analysis of the Power Loss and Quantification of the Energy Distribution in PV Module. Appl. Energy 2020, 260, 114333. [Google Scholar] [CrossRef]
  78. Qi, B.; Wang, J. Fill Factor in Organic Solar Cells. Phys. Chem. Chem. Phys. 2013, 15, 8972–8982. [Google Scholar] [CrossRef]
  79. Usub, T.; Lertsatitthanakorn, C.; Poomsa-ad, N.; Wiset, L.; Yang, L.; Siriamornpun, S. Experimental Performance of A Solar Tunnel Dryer for Drying Silkworm Pupae. Biosyst. Eng. 2008, 101, 209–216. [Google Scholar] [CrossRef]
  80. Kocabiyik, H.; Yilmaz, N.; Tuncel, N.B.; Sumer, S.K.; Buyukcan, M.B. Drying, Energy, and Some Physical and Nutritional Quality Properties of Tomatoes Dried with Short-Infrared Radiation. Food Bioprocess Technol. 2015, 8, 516–525. [Google Scholar] [CrossRef]
  81. Wang, J.; Sheng, K. Far-Infrared and Microwave Drying of Peach. LWT-Food Sci. Technol. 2006, 39, 247–255. [Google Scholar] [CrossRef]
  82. Nasiroglu, S.; Kocabiyik, H. Thin-layer infrared Radiation Drying of Red Pepper Slices. J. Food Process Eng. 2009, 32, 1–16. [Google Scholar] [CrossRef]
  83. Kocabiyik, H.; Tezer, D. Drying of Carrot Slices Using Infrared Radiation. Int. J. Food Sci. Technol. 2009, 44, 953–959. [Google Scholar] [CrossRef]
  84. Das Purkayastha, M.; Nath, A.; Deka, B.C.; Mahanta, C.L. Thin Layer Drying of Tomato Slices. J. Food Sci. Technol. 2013, 50, 642–653. [Google Scholar] [CrossRef] [PubMed]
  85. Shahi, N.C.; Khan, J.N.; Lohani, U.C.; Singh, A.; Kumar, A. Development of Polyhouse Type Solar Dryer for Kashmir Valley. J. Food Sci. Technol. 2011, 48, 290–295. [Google Scholar] [CrossRef] [PubMed]
  86. Jaiganesh, K.; Duraiswamy, K. Experimental Study of Enhancing the Performance of PV Panel Integrated with Solar Thermal System. Int. J. Eng. Technol. 2013, 5, 3419–3426. [Google Scholar]
  87. Yamamoto, K.; Yoshikawa, K.; Uzu, H.; Adachi, D. High-Efficiency Heterojunction Crystalline Si Solar Cells. Jpn. J. Appl. Phys. 2018, 57, 08RB20. [Google Scholar] [CrossRef]
  88. Haschke, J.; Dupré, O.; Boccard, M.; Ballif, C. Silicon Heterojunction Solar Cells: Recent Technological Development and Practical Aspects-FROM Lab to Industry. Sol. Energy Mater. Sol. Cells 2018, 187, 140–153. [Google Scholar] [CrossRef]
  89. Ho, W.-J.; Liu, J.-J.; Yang, Y.-C.; Ho, C.-H. Enhancing Output Power of Textured Silicon Solar Cells by Embedding Indium Plasmonic Nanoparticles in Layers within Antireflective Coating. Nanomaterials 2018, 8, 1003. [Google Scholar] [CrossRef]
  90. Müller, M.; Fischer, G.; Bitnar, B.; Steckemetz, S.; Schiepe, R.; Mühlbauer, M.; Köhler, R.; Richter, P.; Kusterer, C.; Oehlke, A. Loss Analysis of 22% Efficient Industrial PERC Solar Cells. Energy Procedia 2017, 124, 131–137. [Google Scholar] [CrossRef]
Figure 1. The SD integrated with an ASCT and the SD integrated with an FSC used in the current study [65].
Figure 1. The SD integrated with an ASCT and the SD integrated with an FSC used in the current study [65].
Sustainability 16 07008 g001
Figure 2. Main component of SD integrated with ASCT and SD integrated with FSC.
Figure 2. Main component of SD integrated with ASCT and SD integrated with FSC.
Sustainability 16 07008 g002
Figure 3. Detailed drawings of SD integrated with ASCT and SD integrated with FSC; dim in cm.
Figure 3. Detailed drawings of SD integrated with ASCT and SD integrated with FSC; dim in cm.
Sustainability 16 07008 g003
Figure 4. SolidWorks model of ASCT showing the main parts.
Figure 4. SolidWorks model of ASCT showing the main parts.
Sustainability 16 07008 g004
Figure 5. Workflow diagram and main parts of the ASCT.
Figure 5. Workflow diagram and main parts of the ASCT.
Sustainability 16 07008 g005
Figure 6. Schematic diagram of LDR sensors, relay kit, Arduino Uno board, and linear DC motor with successful electrical connections (design of smart monitoring unit and solar tracking circuit).
Figure 6. Schematic diagram of LDR sensors, relay kit, Arduino Uno board, and linear DC motor with successful electrical connections (design of smart monitoring unit and solar tracking circuit).
Sustainability 16 07008 g006
Figure 7. Flowchart of the ASCT.
Figure 7. Flowchart of the ASCT.
Sustainability 16 07008 g007
Figure 8. AT and RH measuring unit.
Figure 8. AT and RH measuring unit.
Sustainability 16 07008 g008
Figure 9. Drying tray covered with plastic mesh and hinged with electronic digital balance [65].
Figure 9. Drying tray covered with plastic mesh and hinged with electronic digital balance [65].
Sustainability 16 07008 g009
Figure 10. Fresh and dried TSs [65].
Figure 10. Fresh and dried TSs [65].
Sustainability 16 07008 g010
Figure 11. Variation in SR, ambient AT, and RH of dry air on the 1st day of the field experiments (29 August 2023).
Figure 11. Variation in SR, ambient AT, and RH of dry air on the 1st day of the field experiments (29 August 2023).
Sustainability 16 07008 g011
Figure 12. Variation in the AT and RH of ambient air, air inside DCh, and both the ASCT and FSC on the first day of the field experiments (29 August 2023) from 8.00 a.m. to 6.00 p.m. The numbers in the figure legend refer to the following: 1 and 2 refer to both (AT and RH) of hot air inside the ASCT and Dch, respectively; 4 and 3 refer to both (AT and RH) of hot air inside the FSC and Dch, respectively; and 5 refers to (AT and RH) of ambient air.
Figure 12. Variation in the AT and RH of ambient air, air inside DCh, and both the ASCT and FSC on the first day of the field experiments (29 August 2023) from 8.00 a.m. to 6.00 p.m. The numbers in the figure legend refer to the following: 1 and 2 refer to both (AT and RH) of hot air inside the ASCT and Dch, respectively; 4 and 3 refer to both (AT and RH) of hot air inside the FSC and Dch, respectively; and 5 refers to (AT and RH) of ambient air.
Sustainability 16 07008 g012
Figure 13. (a) Effect of air velocities on moisture content and drying rate for the ASCT. (b) Effect of air velocities on moisture content and drying rate for the FSC.
Figure 13. (a) Effect of air velocities on moisture content and drying rate for the ASCT. (b) Effect of air velocities on moisture content and drying rate for the FSC.
Sustainability 16 07008 g013aSustainability 16 07008 g013b
Figure 14. (a) Effect of slice thickness on moisture content and drying rate for the ASCT. (b) Effect of slice thickness on moisture content and drying rate for the FSC.
Figure 14. (a) Effect of slice thickness on moisture content and drying rate for the ASCT. (b) Effect of slice thickness on moisture content and drying rate for the FSC.
Sustainability 16 07008 g014aSustainability 16 07008 g014b
Figure 15. Thermal balance of PV system.
Figure 15. Thermal balance of PV system.
Sustainability 16 07008 g015
Figure 16. Thermal analysis of ASCT and FSC, where Ein is the input energy, Eout is the output energy, and Eloss is the energy loss.
Figure 16. Thermal analysis of ASCT and FSC, where Ein is the input energy, Eout is the output energy, and Eloss is the energy loss.
Sustainability 16 07008 g016
Table 1. The design calculation and SD.
Table 1. The design calculation and SD.
ItemsConditions and Assumptions
Drying location25.6890° N, and 32.6975° E
OrientationSouthwards direction
Tilt angle28°
ProductTF (Lycopersi conesculentum)
Loading capacity 5 kg
Initial MC, %92 ± 2%
Total drying time 10 h
SC dimensions (length × width × height), cm100 × 50 × 15 cm
DCh dimensions (length × width × height), cm44 × 44 × 63 cm
SD dimensions (length × width × height), cm44 × 46 × 163 cm
Tray dimensions (Length × Width), cm44 × 44 cm
Table 2. List of components and specification of the main electronic components of the ASCT *.
Table 2. List of components and specification of the main electronic components of the ASCT *.
No. QuantityComponentAccuracy
1.1Arduino Uno (7–12 Vdc), China---
2.2LDR sensor, China ±1 Lux
3.1Relay kit (4—channel), Generic, China---
4.1Linear DC motor (actuator) (10 W, 0.4–0.8 A–36 V, 24-inch, model No: HARL-3624+), Germany---
5.1Converter (input: 110–220 V AC; output: 36 V DC 10 A), DAJUNGUO, China---
6.2Linear resistor (10 kΩ), China±0.5%
* All electronic parts were purchased from local markets in Egypt.
Table 3. The accuracy of the different instruments and sensors was used in the current investigation.
Table 3. The accuracy of the different instruments and sensors was used in the current investigation.
ParametersUnitInstrument Range Resolution Standard Division
AT°CDHT-22 sensor−10–80°C0.1 °C±1 °C
RH%DHT-22 sensor0–100%0.1%±2%
SRW/m2Spectral pyranometers 0.1 W/m2±10 W/m2
Weight of TF samples kgElectronic digital balance0.0–50 kg5 g±0.020
Weight of dried TF inside DCh kgElectronic digital balance0.0–10 kg10 g±10 g
Weight of dried TF in laboratory kgElectronic digital balance0.0–1.0 kg0.1 g±0.15 g
Voltage and current (PV system)V, ADigital multimeter 0.2–1000 V
20 µA–20 A
0.01 V
0.01 A
--
Airspeed m/sA digital anemometer 0.0–30 m/s0.1 m/s±0.1 m/s
Light intensity (ASCT)LuxLDR sensor0.0–1000 Lux0.1 Lux±1 Lux
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 **.
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 **.
TimeWeigh of SampleDrying RateMoisture 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)
ASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCT
8195 b195 b200 ab200 ab204.67 a205 a0 p0 p0 p0 p0 p0 p92.3 a92.3 a92.3 a92.3 a92.2 a92.3 a
9120 e135 d130 d135 d145 c150 c75 a60 d70 b65 c59. d55 e87.5 a–c88.9 ab88.2 a–c88.6 a–c89.1 ab89.5 b
1075 g115 e70 gh85 f75 g90 f45 g20 l60 d50 f70 b60 d79.9 de86.9 a–c77.9 de81.9 cd78.9 de82.5 b–d
1140 l75 g40 l65 hi45 jk60 i35 i40 h30 j20 l30 j30 j62.3 i79.9 de61.4 i76.3 de64.8 hi73.6 ef
1225 m50 j21.3 mn40 l30 m40 l15 m25 k18.7 l25 k15 m20 l39.8 k69.8 gh26.1 l61.4 i46.8 j60.3 i
1320 mn25 m17.6 n25 m20 mn30 m5 o25 k3.7 op15 m10 n10 n24.4 l39.6 k12.8 m38.3 k20.5 l47.0 j
8265 b260 b188 c–e250 b303 a300 a0 g0 g0 g0 g0 g0 g92.3 a92.3 a81.8 c–f92.3 a92.2 a92.3 a
TimeSlice 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)
ASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCTASCT
9190 c–e200 cd180 de200 cd220 c280 ab75 a60 a–c8 fg50 a–e83 a20 d–g89.3 a–c89.9 ab89.1 a–d90.4 ab89.3 a–c91.8 a
10110 h–j145 fg105 h–j160 ef165 ef215 c80 a55 a–d75 a40 b–f55 a–d65 ab81.5 e–h86.2 a–f81.3 e–h87.9 a–e85.8 a–f89.2 a–c
1180 j–m120 g–i75 k–n125 g–i115 g–i164 ef30 b–g25 c–g30 b–g34.66 b–g49.67 a–e51 a–e74.5 g–j83.3 b–f73.7 i–k84.6 a–f79.6 f–i85.91 a–f
1250 m–q95 i–l45 n–q105 h–j80 j–m135 f–h30 b–g25 c–g30 b–g20 d–g35 b–g29 c–g59.2 no7898 f–i56.3 op81.6 d–g70.6 j–m82.9 b–f
1335 pq70 l–o40 o–q75 k–n65 l–p110 h–j15 e–g25 c–g5 fg30 b–g15 e–g25 c–g41.4 r71.4 j–l50.8 pq74.3 h–j63.7 mn78.9 f–i
1430 pq60 m–q30 pq58 m–q50 m–q75 k–n5 fg10 fg10 fg17 e–g15 e–g35 b–g31.9 s66.5 lm34.1 s66.8 k–m52.9 oq69.1 j–m
1525 q35 pq25 q38 o–q35 pq45 n–q5 fg25 c–g5 fg20 d–g15 e–g30 b–g17.6 t42.0 r21.4 t48.75 q32.56 s48.5 q
TimeSlice 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)
ASCTFSCASCTFSCASCTFSCASCTFSCASCTFSCASCTFSCASCTFSCASCTFSCASCTFSC
8375 a370 a380 a375 a375 a370 a0 m0 m0 m0 m0 m0 m92.4 a92.2 a92.3 a92.4 a92.3 a92.3 a
9315 de340 b310 ef330 bc300 fg325 cd60 c–f30 h–k70 a–d45 f–g75 a–c45 f–g90.8 a91.6 a90.6 a91.3 a91.4 a92.2 a
10250 mn295 hi260 lm278 jk245 no285 ij65 b–e45 f–g50 e–g52 e–g55 d–g40 g–i88.5 a–c90.4 a88.7 a–c89.6 a89.2 a–c9.01 a
11175 pq270 kl175 pq245 no165 pq240 no75 a–c25 i–k85 a33 h–j80 a–b45 f–g83.5 ef89.5 ab83.3 ef88.2 a–d83.5 ef89.1 a–c
12120 s–v195 o–q110 v185 o–q105 vw180 o–q55 d–g75 a–c65 b–e60 c–f60 c–f60 c–f75.9 gh85.4 b–e73.4 hi84.4 de73.5 hi85.2 c–e
1390 x–y140 r80 x–y130 r–t85 x–y135 rs30 h–k55 d–g30 h–k55 d–g20 j–l45 f–g67.9 jk79.6 fg63.4 m–o77.8 g67.0 k–m79.9 fg
1475 x–y125 s–v65 xyz110 v70 x–y115 s–v15 k–m15 k–m15 k–m20 j–l15 k–m20 j–l61.4 no77.2 gh54.9 q73.6 hi59.8 op77.2 gh
1550 w100 vw40 z80 x–y55 wz85 x–y25 i–k25 i–k25 i–k30 h–k15 k–m30 h–k41.8 r71.5 ij26.34 s63.9 l–n48.5 q67.5 kl
1633 z75 x–y35 z55 wz33 z65 yz17 j–i25 i–k5 lm25 i–k22 jk20 j–l12.23 t61.9 no15.7 t47.3 q13.5 t57.2 pq
** Mean values with the same letter did not differ significantly at p ≤ 0.05.
Table 5. Some studies in the literature examining the TE of the SC.
Table 5. Some studies in the literature examining the TE of the SC.
ReferenceDC TypeTE of Traditional SCTE of Tracking SCIncreasing 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 studyFlat-plate SC integrated with ASCT61.6%83.2%21.6%
Table 6. Different costs related to the two SDs.
Table 6. Different costs related to the two SDs.
Cost ParametersSD Integrated with FSC (USD)SD Integrated with ASCT (USD)
Capital Cost of Dryer108.57125
Annual Capital Cost36.26241.75
Annual Maintenance Cost1.0881.2525
Annual Salvage Value2.9013.34
Annual Cost of SD34.44939.662
Table 7. Economic analysis of the two SDs.
Table 7. Economic analysis of the two SDs.
Economic Analysis ParametersSD Integrated with FSCSD Integrated with ASCT
Mass of product dried per batch (kg)0.5 0.5
Quantity of dried product annually (kg)104.3124.1
Drying Cost of per kg (USD)0.330.319
Cost of 1 kg fresh product (USD)0.2850.285
Mass of fresh product per batch (kg)5.05.0
Cost of fresh product per kg of dried product (USD)2.852.85
Cost of 1 kg of crop dried inside the dryer (USD)3.183.17
Selling price per kg (USD)5.05.0
Saving per Kg (USD)1.821.83
Saving per batch (USD)0.910.915
Saving per day (USD)0.5610.627
Saving after 1 year (USD)204228.9
Payback Time (Years)0.6550.672
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Elwakeel, A.E.; Gameh, M.A.; Oraiath, A.A.T.; Eissa, A.S.; Elsayed, S.; Elmessery, W.M.; Mostafa, M.B.; Alhag, S.K.; Al-Shuraym, L.A.; Moustapha, M.E.; et al. Development and Techno-Economic Analysis of a Tracked Indirect Forced Solar Dryer Integrated Photovoltaic System for Drying Tomatoes. Sustainability 2024, 16, 7008. https://doi.org/10.3390/su16167008

AMA Style

Elwakeel AE, Gameh MA, Oraiath AAT, Eissa AS, Elsayed S, Elmessery WM, Mostafa MB, Alhag SK, Al-Shuraym LA, Moustapha ME, et al. Development and Techno-Economic Analysis of a Tracked Indirect Forced Solar Dryer Integrated Photovoltaic System for Drying Tomatoes. Sustainability. 2024; 16(16):7008. https://doi.org/10.3390/su16167008

Chicago/Turabian Style

Elwakeel, Abdallah Elshawadfy, Mohsen A. Gameh, Awad Ali Tayoush Oraiath, Ahmed S. Eissa, Salah Elsayed, Wael M. Elmessery, Mostafa B. Mostafa, Sadeq K. Alhag, Laila A. Al-Shuraym, Moustapha Eid Moustapha, and et al. 2024. "Development and Techno-Economic Analysis of a Tracked Indirect Forced Solar Dryer Integrated Photovoltaic System for Drying Tomatoes" Sustainability 16, no. 16: 7008. https://doi.org/10.3390/su16167008

APA Style

Elwakeel, A. E., Gameh, M. A., Oraiath, A. A. T., Eissa, A. S., Elsayed, S., Elmessery, W. M., Mostafa, M. B., Alhag, S. K., Al-Shuraym, L. A., Moustapha, M. E., Elbeltagi, A., Salem, A., & Tantawy, A. A. (2024). Development and Techno-Economic Analysis of a Tracked Indirect Forced Solar Dryer Integrated Photovoltaic System for Drying Tomatoes. Sustainability, 16(16), 7008. https://doi.org/10.3390/su16167008

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop