Development, Thermodynamic Evaluation, and Economic Analysis of a PVT-Based Automated Indirect Solar Dryer for Date Fruits

Abstract

The present study focuses on the development, thermodynamic evaluation, and economic analysis of a photovoltaic-thermal (PVT)-based automated mixed-mode indirect solar dryer (AMMISD). The developed AMMISD was used for drying five date varieties native to the Aswan area, namely Shamia, Bartamuda, Sakkoti, Malkabii, and Gondaila. The initial and final moisture contents of date varieties ranged between 15.7% and 17.2% and 4.91% and 6.41%, respectively. All date fruit varieties reached equilibrium moisture content after 6 days (60 h) compared with 14 days (140 h) in a traditional indirect solar dryer (SD) and 14–25 days (140–250 h) under open-air sun drying. The energy analysis of both the solar air collector (SAC) and the SD showed that the SAC efficiency and the drying efficiency were in the range of 15.15–63.33% and 4.01–4.41%, respectively. Additionally, the exergy analysis of the SAC and drying room (DR) revealed a maximum efficiency of 27.5% and 96.62%, respectively. The improvement potential (IP) ranged from 4.62 to 13.64 W, the mean value of waste exergy ratio (WER) was 0.84, and the sustainability index (SI) ranged from 1.01 to 1.38. Moreover, the economic analysis showed substantial economic advantages for date fruit drying, yielding annual savings of approximately USD 236.9, with an investment recovery period of about 2.091 years.

1. Introduction

Dates, often referred to as “nature’s candy”, are a highly nutritious and versatile food with significant cultural, historical, and health importance. Packed with essential vitamins, minerals, and fiber, dates provide a natural energy boost, aid digestion, and support heart health [1,2,3]. Global production of dates has seen steady growth, with over 9.82 million metric tons produced annually. Egypt, the world’s largest date producer, contributes significantly to global output, with an estimated 1.8 million metric tons in 2023 [4].

Traditional drying methods for dates, such as sun drying in open air, have been widely used for centuries due to their simplicity and low cost. However, this method poses several challenges, including exposure to dust, insects, and unpredictable weather, which can lead to contamination and inconsistent drying. Additionally, prolonged exposure to direct sunlight may degrade the nutritional quality and flavor of the fruit. In contrast, SDs offer a more controlled and efficient alternative [5,6,7,8]. These devices use solar energy to create a regulated drying environment, reducing the risk of contamination and ensuring uniform drying. SDs also preserve the fruit’s nutritional value, color, and taste while significantly shortening drying time [9,10]. Although the initial investment in SDs is higher, their long-term benefits, including improved product quality and reduced post-harvest losses, make them a sustainable solution for modern date production, aligning with global food safety and quality standards [11,12,13,14]. Unlike conventional fixed implementations with average thermal efficiencies, Bayrak et al. [15] developed a product-focused approach to optimize electrical and drying performance. Their system integrated an innovative PVT collector with an advanced temperature control algorithm, achieving a peak thermal efficiency of 39.05%, and Gupta et al. [16] enhanced the performance of a PVT solar dryer by incorporating a modified design featuring sand-filled thermal energy storage units. Their novel approach achieved maximum energy storage efficiency with improvements of 47.36% for the collector and 39.65% for the dryer system. Ceballos et al. [17] conducted a comprehensive evaluation of a PVT solar dryer by analyzing its technical, economic, embodied energy, and environmental performance across two operational modes enabled by its adaptable design: solar mode and hybrid mode. The energy payback times were found to be 1.22 years and 1.25 years, respectively.

Energy and exergy analysis are critical tools for evaluating the efficiency and sustainability of SDs and the drying process [18,19]. Energy analysis focuses on quantifying the total energy input and output, helping to identify energy losses and optimize system performance. Exergy analysis, on the other hand, assesses the quality of energy by measuring its usable portion, providing deeper insights into inefficiencies and irreversibilities within the system [20,21,22]. These analyses enable the design of more efficient SDs by pinpointing areas for improvement, such as heat retention, airflow management, and thermal insulation [23,24,25]. By optimizing energy use, SDs can reduce operational costs and environmental impact, making them more sustainable. Additionally, exergy analysis ensures that the drying process preserves the quality of the product, such as dates, by minimizing nutrient loss and maintaining texture and flavor. These analyses are essential for advancing renewable energy technologies and promoting eco-friendly agricultural practices [26,27,28,29].

Several studies have investigated the energy and exergy performance of different SDs used for drying agricultural products. For instance, Mugi and Chandramohan [30] analyzed the energy and exergy of okra drying in an indirect SD using forced and natural convection. Forced convection showed higher mean SAC and drying efficiencies (74.98% and 24.95%) compared to natural convection (61.49% and 20.13%). Exergy outflow in forced convection ranged from 1.04 to 46.85 W, while in natural convection, it was between 1.13 and 50.94 W. The average exergy efficiencies for SAC were 2.03% (forced) and 2.44% (natural). Additionally, the IP in forced convection varied from 0.0095 to 10.51 W, with SI and WER ranging from 0.06 to 17.05. Selimefendigil et al. [31] studied the impact of adding nanoparticles to a paraffin-based thermal storage system in a greenhouse dryer. At a flow rate of 0.016 kg/s, the average exergy efficiencies were 3.45% (with nanoparticles) and 2.74% (without), while at 0.010 kg/s, they were 3.01% and 2.40%, respectively. Ekka et al. [32] evaluated a mixed-mode SD with dual double-pass SACs for drying cluster figs. The total exergy efficiency was found to be between 18.8% and 41.4%. Their research indicated that higher air mass flow rates reduced exergy loss potential, with SI values between 1.26 and 1.71, where higher values correlated with better exergy efficiency. Şevik et al. [33] tested a double-pass SD with and without infrared assistance, achieving energy efficiencies between 1.15% and 26.46%. Chowdhury et al. [34] evaluated the performance of a solar tunnel dryer for dehydrating jackfruit leather. Their findings revealed that the SAC efficiency varied from 27.45% to 42.50%, while the dryer efficiency ranged between 32.34% and 65.30% under solar radiation levels of 100–600 W/m². The system’s total energy efficiency was 42.47%, with exergetic efficiencies reaching 32–69% for the SAC and 41.42% for the drying unit. Shringi et al. [35] examined garlic clove drying in an SD equipped with phase-change material (PCM) for thermal storage. Their study reported exergy efficiencies for the DR between 67.06% and 88.24%. Similarly, Panwar [36] studied leaf drying in a natural convection SD, observing DR exergy efficiencies from 55.35% to 79.35%. Ndukwu et al. [37] explored an SD integrated with sodium sulfate decahydrate and sodium chloride as thermal storage mediums. Their results indicated that the DR’s exergy efficiency spanned 66.67% to 96.09%. Kesavan et al. [38] conducted a thermodynamic analysis of a triple-pass SD for potato slices, finding DR exergy efficiencies between 2.8% and 87.02%. Karthikeyan and Murugavelh [39] performed an exergy analysis on turmeric drying in a mixed-mode forced convection solar tunnel dryer, reporting DR efficiencies of 23.25–73.31%. Tiwari and Tiwari [40] assessed a mixed-mode greenhouse SD with a partially covered SAC, recording SAC exergy efficiencies of 19.11–28.96%. Lastly, Abdelkader et al. [41] investigated a carbon nanotube-enhanced SD, with SAC exergy efficiencies ranging from 8.1% to 11.9%. Fiorentino et al. [42] developed a low-cost smart/AI alert system using weather data and internal sensors. A feed-forward neural network compared empty vs. pepper-filled greenhouse conditions, identifying the first 3–4 days as critical and setting a humidity threshold. The system predicted risks one hour in advance, reducing rot by alerting operators to adjust conditions. Walke et al. [43] developed an IoT-based smart solar dryer for red onions, testing slice thicknesses from 2–4 mm (0.5 mm intervals) over three-day drying cycles (9 a.m.–6 p.m.). Results showed that 4 mm slices reduced drying time to 10% moisture by 3.77–30.19% compared to thinner slices, though 2 mm slices achieved higher thermal efficiency (27.89% vs. 19.50%). Onions dried at 2 mm and 2.5 mm were ideal for powdered production due to optimal dehydration.

Traditional SDs rely on manual operation, utilizing natural convection or forced air circulation. Farmers must continuously monitor and adjust the drying process—such as opening vents or protecting produce from rain and pests—to prevent uneven drying, overheating, or moisture retention, which can degrade product quality. Although these systems are low-cost and simple to construct, their reliance on human intervention often leads to inconsistent drying rates and suboptimal performance. Mixed-mode SDs address these limitations by combining natural and forced air circulation, enhancing thermal efficiency and drying uniformity. To further optimize this system, the current study integrated electronic control circuits equipped with sensors and microcontrollers to automate temperature, humidity, and airflow regulation. This automation eliminates manual intervention, improves energy efficiency, and ensures precise drying conditions. Compared to traditional methods, automated mixed-mode SDs significantly reduce labor requirements while enhancing drying efficiency and output quality. Additionally, this study conducted a thermodynamic analysis of the developed AMMISD, evaluating its energy and exergy performance. Sustainability indicators were also assessed to determine the system’s environmental and operational efficiency.

2. Materials and Methods

2.1. Description of the Developed AMMISD

This study presents an innovative design for a developed AMMISD that operates using a PV system (Figure 1 and Figure 2). It was integrated with an electronic circuit that enables it to operate in two different modes: active mode and passive mode. The developed AMMISD automatically switches between active mode and passive mode based on the air temperature (AT) inside the DR and the light intensity outside the DR. The measured data of AT, relative humidity (RH), and light intensity (Li) can be automatically transmitted via a GSM module or displayed on an integrated LCD. For safe operation, the developed AMMISD was equipped with a novel early warning system (SOS) that can send a warning message (SMS) to the operator in case of system failure. The developed AMMISD was manufactured from recycled iron angle (30 mm × 30 mm), square metal bars (30 mm × 30 mm), and sheet metal 1 mm in thickness, and it consists of many parts, including (1) SAC with dimensions of 3000 mm in length, 1000 mm in width, and 200 mm in height (Figure 3); insulated with wood dust; and an absorber plate manufactured using aluminum corrugated sheets and painted black; it was covered with a glass cover 3 mm in thickness; (2) DR with dimensions of 600 mm in width, 1000 mm in height, and 1000 mm in length; it was designed to accumulate eight drying trays (Figure 1); (3) the photovoltaic system (PV), which comprises a universal-TPS-P6U (72)-320 W solar panel module and includes a battery charger rated 12/24 V and 20 A, along with a 12 V/60 Ah battery (Figure 4); (4) an AC exhaust fan rated at 220 V and 50 W, which was used for exhausting the hot air from the SAC through the drying trays to reduce the RH of air inside the DR; (4) and a measuring and controlling electronic circuit.

2.2. Control Circuit and Operating Algorithm for the Developed AMMISD

2.2.1. Design of a Control Circuit

The developed AMMISD consists of many components (Figure 5 and Figure 6). The specifications of these parts are shown in Figure 7: (1) a speed sensor (model: LM 393 IC) measures the velocity of the air exhaust fan; (2) a Li sensor (model: GL5506) quantifies the Li outside the DR; (3) DHT-22 sensors detect the AT and RH, as seen in Figure 5; (4) an Arduino Mega board (model: 2500 R3) receives and evaluates data from several terminal sensors and subsequently makes a decision depending on operational algorithms (Figure 6); (5) A GSM module (model: SIM900A) transmits measured data from the Arduino board to the user interface and dispatches warning messages (SOS) in the event of system failure; (6) an LCD (model: 16 × 2 characters) exhibits the AT, RH, and Li; (7) an exhaust fan extracts hot air for the SAC through the drying trays and dates to the outside; (8) a 2-channel relay model controls the AC exhaust fan; and (9) many auxiliary components were also utilized, including (a) a laptop for editing programming codes and receiving measured data through a USB cord during field experiments; (b) a smartphone for receiving SMS messages from the GSM module; and (c) breadboards, wires, and USB cables as well. Figure 6 illustrates the signal flow rate and electrical energy interconnections among the different electronic components.

Table 1. List, quantity, and accuracy of electronic components of the developed AMMISD.

No.	Quantity	Component
1.	1	Arduino Mega board (7–12 Vdc)
2.	1	Li sensor
3.	1	Relay kit (2—channel)
6.	1	Linear resistor (10 kΩ)
7.	3	DHT-22 sensor
8.	1	LCD
9.	1	Speed sensor
10.	1	GSM module

shows the list, quantity, and accuracy of electronic components of the developed AMMISD.

Table 2. The accuracy, range, and error of the measuring devices and sensors.

Parameters	Device	Accuracy	Range	Error
Air temperature	DHT-22 sensor	±1 °C	−10–80 °C	0.1 °C
Relative humidity	DHT-22 sensor	±2%	0–100%	0.1%
Solar radiation	Spectral pyranometers (model: SENTEC RS485, SenTec, Sichuan, China)	±10 W/m2	------------	0.1 W/m2
Weight of SF samples	Electronic digital balance	±0.020 g	0.0–50 kg	5 g
Voltage and current (PV system)	Digital multi-meter	------------	0.2–1000 V 20 µA–20 A	0.01 V 0.01 A
Air speed	Digital anemometer (model: Extech AN100, EXTECH, Beijing, China)	±0.1 m/s	0.0–30 m/s	0.1 m/s
Light intensity	LDR sensor	±1 Lux	0.0–1000 Lux	0.1 Lux

shows the accuracy, range, and error of the measuring devices and sensors.

2.2.2. Operating Algorithm of the Developed AMMISD

The operational algorithm for the developed AMMISD is illustrated in Figure 8. The algorithm commences by initializing the AT, RH, and Li sensors as well as the speed sensor for the exhaust fan motor. Next, it collects data from the Li sensor, calculates the Li of surround environment, and makes two decisions based on this calculation. If Li surpasses or equals a predetermined threshold (Li ≥ Set Point), the operating algorithm makes a decision to turn on the air exhaust fan. Conversely, if the Li is lower than the set point (Li ≤ Set Point), the operating algorithm makes a decision to turn off the air exhaust and compiles data from the AT and RH sensors to compute the AT and RH. Subsequently, the algorithm verifies whether the AT or RH surpasses their respective set points (T ≥ Set Point or RH ≥ Set Point). If the Li is low and the AT or RH within their set points, the algorithm employs natural circulation. If Li is high or the AT and RH surpass their set points, the algorithm utilizes forced circulation by activating the air exhaust fan. Thus, based on the Li and the AT or RH conditions, the algorithm selects the appropriate circulation system: natural or forced circulation. After selecting the circulation system, the algorithm introduces a delay of 5 min before restarting the cycle. The algorithm’s primary objective is to optimize the drying process in the developed AMMISD by automatically calibrating the ventilation and circulation of air based on the Li of the surrounding environment, AT, and RH. This strategy can enhance drying efficiency, forestall over-drying, and prevent spoilage of the products being dried. However, it is crucial to note that the specific set points for Li, AT, and RH will vary depending on the type of product being dried. Furthermore, the algorithm could be further refined by considering other factors, such as the MC of the product being dried. The use of the developed AMMISD can potentially reduce energy consumption compared to conventional dryers that rely on fossil fuels.

2.2.3. Operating Algorithm of the GSM Module

The operating algorithm utilized for transmitting measured data via a GSM module was demonstrated in Figure 9. The GSM module acquires data from different sensors that monitor AT, RH, and light intensity, and then transmits these data to the user interface (smartphone) via SMS. The procedure begins with the initialization of the GSM module and subsequently attempts to establish a connection to the GSM. Upon successful connection establishment, the program disseminates an SMS containing measured analog values obtained from the sensors to the operator’s smartphone. In the interest of mitigating errors in the event of system failure, the GSM module was programmed to transmit a warning SMS (SOS) in the event of the air exhaust fan not functioning at high AT.

2.3. Performance Analysis of the Developed AMMISD

2.3.1. Moisture Content (MC)

The measurement of MC of dates was conducted by heating at 70 °C for 24 h in an electric oven until reaching constant weight. This method adheres to the procedure specified by AOAC [44]. The moisture content of the date samples was determined using Equation (1) [45].

$$MC \left(d.b.\right)={\displaystyle \frac{\stackrel{\mathrm{D}\mathrm{i}\mathrm{f}\mathrm{f}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{e} \mathrm{b}\mathrm{e}\mathrm{t}\mathrm{w}\mathrm{e}\mathrm{e}\mathrm{n} \mathrm{w}\mathrm{e}\mathrm{t} \mathrm{a}\mathrm{n}\mathrm{d} \mathrm{d}\mathrm{r}\mathrm{y} \mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{d}\mathrm{a}\mathrm{t}\mathrm{e} \mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}{\overbrace{{\mathrm{W}}_{\mathrm{w}}-{\mathrm{W}}_{\mathrm{d}}}}}{\stackrel{\mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{d}\mathrm{r}\mathrm{y} \mathrm{d}\mathrm{a}\mathrm{t}\mathrm{e} \mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}{\overbrace{{\mathrm{W}}_{\mathrm{d}}}}}}\times 100$$

(1)

2.3.2. Energy Analysis of the Developed AMMISD

The developed AMMISD consists of SAC and DR. These components were evaluated using fundamental thermodynamic principles of mass and energy conservation for steady-flow systems [46]. According to the mass conservation and energy principles, the air mass flow rate remains constant throughout the system, meaning the inflow rate at the inlet precisely matches the outflow rate at the exit.

$$\sum \stackrel{Inlet mass flowrate}{\overbrace{{\dot{m}}_{ai}}}=\sum \stackrel{Outlet mass flowrate}{\overbrace{{\dot{m}}_{ao}}}$$

(2)

$$\sum \stackrel{Inlet energy flowrate}{\overbrace{{\dot{E}}_{ai}}}=\sum \stackrel{Outlet energy flowrate}{\overbrace{{\dot{E}}_{ao}}}$$

(3)

$$\stackrel{\mathrm{H}\mathrm{e}\mathrm{a}\mathrm{t}\mathrm{t} \mathrm{r}\mathrm{a}\mathrm{n}\mathrm{s}\mathrm{f}\mathrm{e}\mathrm{r}}{\overbrace{\dot{Q}}}+\sum {\dot{m}}_{ai}\left(\stackrel{\mathrm{I}\mathrm{n}\mathrm{t}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{p}\mathrm{y}}{\overbrace{h_{ai}}}+{\displaystyle \frac{\stackrel{\mathrm{V}\mathrm{e}\mathrm{l}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y}}{\overbrace{v_{ai}}}}{2}}+\stackrel{\mathrm{H}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}}{\overbrace{z_{ai}}}g\right)=\sum {\dot{m}}_{ao}\left(\stackrel{\mathrm{I}\mathrm{n}\mathrm{t}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{p}\mathrm{y}}{\overbrace{h_{ao}}}+{\displaystyle \frac{\stackrel{\mathrm{V}\mathrm{e}\mathrm{l}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y}}{\overbrace{v_{ao}}}}{2}}+\stackrel{\mathrm{H}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}}{\overbrace{z_{ao}}}g\right)+\stackrel{\mathrm{W}\mathrm{o}\mathrm{r}\mathrm{k} \mathrm{d}\mathrm{o}\mathrm{n}\mathrm{e}}{\overbrace{\dot{W}}}$$

(4)

where

$$\stackrel{\mathrm{W}\mathrm{o}\mathrm{r}\mathrm{k} \mathrm{d}\mathrm{o}\mathrm{n}\mathrm{e}}{\overbrace{\dot{W}}}=\mathit{zero}$$

(5)

$$\left[{\displaystyle \frac{\stackrel{\mathrm{V}\mathrm{e}\mathrm{l}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{o}\mathrm{f} \mathrm{i}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{a}\mathrm{i}\mathrm{r}}{\overbrace{v_{ai}}}}{2}}-{\displaystyle \frac{\stackrel{\mathrm{V}\mathrm{e}\mathrm{l}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{o}\mathrm{f} \mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{a}\mathrm{i}\mathrm{r}}{\overbrace{v_{ao}}}}{2}}\right] \mathrm{a}\mathrm{n}\mathrm{d} \left[\stackrel{\mathrm{H}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{i}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{a}\mathrm{i}\mathrm{r}}{\overbrace{z_{ai}}}g-\stackrel{\mathrm{H}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{a}\mathrm{i}\mathrm{r}}{\overbrace{z_{ao}}}g\right]=\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{y} \mathrm{s}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{l}, \mathrm{a}\mathrm{n}\mathrm{d} \mathrm{i}\mathrm{t} \mathrm{w}\mathrm{a}\mathrm{s} \mathrm{n}\mathrm{e}\mathrm{g}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{e}\mathrm{d}.$$

(6)

2.3.3. Energy Analysis of the SAC and the Developed AMMISD

By applying Equations (5) and (6) to SAC, the following equations were obtained.

$$\sum \stackrel{Inlet mass flowrate}{\overbrace{{\dot{m}}_{ai}}}=\sum \stackrel{Outlet mass flowrate}{\overbrace{{\dot{m}}_{ao}}}=\sum \stackrel{Mass flowrate}{\overbrace{{\dot{m}}_a}}$$

(7)

$$\stackrel{\mathrm{H}\mathrm{e}\mathrm{a}\mathrm{t} \mathrm{t}\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{s}\mathrm{f}\mathrm{e}\mathrm{r}}{\overbrace{\dot{Q}}}=\stackrel{\mathrm{U}\mathrm{s}\mathrm{e}\mathrm{f}\mathrm{u}\mathrm{l} \mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{{\dot{Q}}_{u, \mathrm{S}\mathrm{A}\mathrm{C}}}}=\stackrel{\mathrm{I}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{{\dot{Q}}_{in, \mathrm{S}\mathrm{A}\mathrm{C}}}}-\stackrel{\mathrm{E}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y} \mathrm{l}\mathrm{o}\mathrm{s}\mathrm{s}}{\overbrace{{\dot{Q}}_{ls, \mathrm{S}\mathrm{A}\mathrm{C}}}}=\stackrel{Air mass flow rate}{\overbrace{{\dot{m}}_a}}\stackrel{Change in enthalpy}{\left(\overbrace{h_{ao}-h_{ai}}\right)}$$

(8)

The input energy of a solar collector refers to the total solar radiation incident on its surface, representing the primary energy source for the drying system. The useful energy denotes the portion of this input that is effectively converted into thermal energy for heating the air. Energy efficiency quantifies the collector’s performance by comparing the useful energy output to the input energy, expressed as a percentage. Higher efficiency indicates better solar energy utilization with minimal losses. These parameters are crucial for evaluating and optimizing SD performance, ensuring effective heat transfer and sustainable operation in agricultural drying applications. The input energy (${\dot{Q}}_{in, \mathrm{S}\mathrm{A}\mathrm{C}}$), useful energy (${\dot{Q}}_{u, \mathrm{S}\mathrm{A}\mathrm{C}}$), and energy efficiency (${\eta }_{en, SC}$) of the SAC were thus calculated according to Equations (9)–(11) [34,36,47].

$$\stackrel{\mathrm{I}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{{\dot{Q}}_{in, \mathrm{S}\mathrm{A}\mathrm{C}}}}=\stackrel{\mathrm{S}\mathrm{o}\mathrm{l}\mathrm{a}\mathrm{r} \mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y}}{\overbrace{I_s}}\times \stackrel{\mathrm{S}\mathrm{u}\mathrm{r}\mathrm{f}\mathrm{a}\mathrm{c}\mathrm{e} \mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a} \mathrm{o}\mathrm{f} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{s}\mathrm{o}\mathrm{l}\mathrm{a}\mathrm{r} \mathrm{c}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}}{\overbrace{A_{SAC}}}$$

(9)

$$\stackrel{\mathrm{U}\mathrm{s}\mathrm{e}\mathrm{f}\mathrm{u}\mathrm{l} \mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{{\dot{Q}}_{u, \mathrm{S}\mathrm{A}\mathrm{C}}}}=\stackrel{Mass flowrate}{\overbrace{{\dot{m}}_a}}\times \stackrel{\mathrm{S}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{f}\mathrm{i}\mathrm{c} \mathrm{h}\mathrm{e}\mathrm{a}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{a}\mathrm{i}\mathrm{r} (0.718 \mathrm{k}\mathrm{J}/\mathrm{k}\mathrm{g} \mathrm{K})}{\overbrace{C_{pa}}}\times \stackrel{Change in air temperature}{\left(\overbrace{T_{co}-T_{ci}}\right)}$$

(10)

$${\eta }_{en, SAC}={\displaystyle \frac{{\dot{Q}}_{u, \mathrm{S}\mathrm{A}\mathrm{C}}}{{\dot{Q}}_{in, \mathrm{S}\mathrm{A}\mathrm{C}}}}={\displaystyle \frac{{\dot{m}}_a{C}_{pa}\left(T_{co}-T_{ci}\right)}{I_s{A}_{SAC}}}$$

(11)

Drying efficiency in SDs denotes the system’s capability to transform solar energy into efficient heat for the extraction of moisture from agricultural products. It measures the efficiency of the dryer in retaining thermal energy while reducing environmental losses. Elevated drying efficiency signifies optimal thermal consumption, expedited drying durations, and enhanced product quality. Factors influencing efficiency encompass solar radiation intensity, airflow rate, dryer design, and insulation quality. Enhancing drying efficiency in SDs can improve food preservation, minimize energy loss, and decrease operational expenses, thus rendering them more sustainable for agricultural use. The drying efficiency (${\eta }_{en, Dryer}$) of the developed AMMISD was established using Equation (11) [34].

$${\eta }_{en, Dryer}={\displaystyle \frac{\stackrel{\mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{o}\mathrm{f} \mathrm{r}\mathrm{e}\mathrm{m}\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{d} \mathrm{w}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{d}\mathrm{a}\mathrm{t}\mathrm{e} \mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}{\overbrace{m_w}}\times \stackrel{\mathrm{L}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{h}\mathrm{e}\mathrm{a}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{v}\mathrm{a}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{i}\mathrm{z}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{o}\mathrm{f} \mathrm{w}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r} (2370 \mathrm{k}\mathrm{J} \mathrm{k}{\mathrm{g}}^{-1})}{\overbrace{L}}}{\stackrel{\mathrm{U}\mathrm{s}\mathrm{e}\mathrm{f}\mathrm{u}\mathrm{l} \mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{{\dot{Q}}_{u, \mathrm{S}\mathrm{A}\mathrm{C}}}}\times \stackrel{\mathrm{D}\mathrm{r}\mathrm{y}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e}}{\overbrace{t_d}}}}$$

(12)

2.3.4. Exergy Analysis ($\dot{Ex}$)

Exergy ($\dot{Ex}$) is the available energy that can be used in the developed AMMISD, and it is an indication of the quality of energy. The $\dot{Ex}$ analysis of the developed AMMISD is based on the second law of thermodynamics, and it is calculated using Equation (13).

$$\begin{gathered} \stackrel{\mathrm{E}\mathrm{x}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{\dot{Ex}}}=\left(\stackrel{\mathrm{I}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{n}\mathrm{a}\mathrm{l} \mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{u-u_{\infty }}}\right)-T_0\left(\stackrel{\mathrm{E}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{p}\mathrm{y}}{\overbrace{s-s_{\infty }}}\right)+P_0\left(\stackrel{\mathrm{F}\mathrm{l}\mathrm{o}\mathrm{w} \mathrm{w}\mathrm{o}\mathrm{r}\mathrm{k}}{\overbrace{v-v_{\infty }}}\right)+\stackrel{\mathrm{M}\mathrm{o}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{u}\mathrm{m} \mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{{\displaystyle \frac{V^2}{2}}}}+g\left(\stackrel{\mathrm{G}\mathrm{r}\mathrm{a}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{l} \mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{z-z_{\infty }}}\right) \\ +\sum _{ch}\left(\stackrel{\mathrm{C}\mathrm{h}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{l} \mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{{\mu }_{ch}-{\mu }_{\infty }}}\right)\times N_{ch}+\left(\stackrel{\mathrm{R}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{\sigma A_i{F}_i\left({3T}^4-T_{\infty }^4-4T_{\infty }{T}^3\right)}}\right) \end{gathered}$$

(13)

By applying Equation (13) in the current study for the developed AMMISD, it was rewritten by neglecting the unnecessary parts related to the flow process: the momentum, gravitational, chemical, and radiation energies. Equation (14) can thus be obtained by applying the above assumptions [30].

$$\dot{Ex}={\dot{m}}_a{C}_{pa}\left(\left(\stackrel{Change in air temperature}{\overbrace{T-T_0}}\right)-T_{0}ln\left({\displaystyle \frac{T}{T_0}}\right)\right)$$

(14)

where $T_0$ is atmospheric AT.

Exergy Analysis of the SAC

The $\dot{Ex}$ balance for the SAC is given by Equation (19) [26,34,48].

$$\stackrel{\mathrm{E}\mathrm{x}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y} \mathrm{l}\mathrm{o}\mathrm{s}\mathrm{s}}{\overbrace{{\dot{Ex}}_{ls, SAC}}}=\stackrel{\mathrm{I}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{e}\mathrm{x}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{{\dot{Ex}}_{in, SAC}}}-\stackrel{\mathrm{O}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{e}\mathrm{x}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}{\overbrace{{\dot{Ex}}_{out, SAC}}}$$

(15)

$${\dot{Ex}}_{in, SAC}=\left[1-{\displaystyle \frac{\stackrel{\mathrm{A}\mathrm{m}\mathrm{b}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{a}\mathrm{i}\mathrm{r} \mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{e}}{\overbrace{T_0}}}{\stackrel{\mathrm{S}\mathrm{u}\mathrm{n} \mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{e} (6000 \mathrm{k})}{\overbrace{T_s}}}}\right]\times \stackrel{\mathrm{E}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y} \mathrm{a}\mathrm{b}\mathrm{s}\mathrm{o}\mathrm{r}\mathrm{b}\mathrm{e}\mathrm{d} \mathrm{b}\mathrm{y} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{a}\mathrm{b}\mathrm{s}\mathrm{o}\mathrm{r}\mathrm{p}\mathrm{e}\mathrm{r} \mathrm{p}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}}{\overbrace{{\dot{Q}}_{in, abs}}}$$

(16)

$${\dot{Q}}_{in, abs}=\stackrel{\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{s}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{o}\mathrm{f} \mathrm{g}\mathrm{l}\mathrm{a}\mathrm{s}\mathrm{s} \left(0.88\right)}{\overbrace{\tau }}\times \stackrel{\mathrm{A}\mathrm{b}\mathrm{s}\mathrm{o}\mathrm{r}\mathrm{p}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{o}\mathrm{f} \mathrm{g}\mathrm{l}\mathrm{a}\mathrm{s}\mathrm{s} \left(0.95\right)}{\overbrace{\alpha }}\times {\dot{Q}}_{in, \mathrm{S}\mathrm{A}\mathrm{C}}$$

(17)

$${\dot{Ex}}_{out, SAC}={\dot{m}}_a{C}_{pa}\left(\left(T_{co}-T_{ci}\right)-T_{0}ln\left({\displaystyle \frac{T_{co}}{T_{ci}}}\right)\right)$$

(18)

The $\dot{Ex}$ efficiency of the SAC is obtained using Equation (19) [34,49].

$${\eta }_{ex, SAC}={\displaystyle \frac{{\dot{Ex}}_{out, SAC}}{{\dot{Ex}}_{in, SAC}}}=1-{\displaystyle \frac{{\dot{Ex}}_{ls, SAC}}{{\dot{Ex}}_{in, SAC}}}=1-{\displaystyle \frac{T_0{S}_{gen}}{\left[1-\frac{T_0}{T_s}\right]{\dot{Q}}_{in, SAC}}}$$

(19)

$\dot{Ex}$ Analysis of the DR

The $\dot{Ex}$ balance for the DR is expressed as follows:

$$\stackrel{Exergy loss }{\overbrace{{\dot{Ex}}_{ls, DR}}}=\stackrel{Input exergy }{\overbrace{{\dot{Ex}}_{in, DR}}}-\stackrel{Output exergy}{\overbrace{{\dot{Ex}}_{out,DR}}}$$

(20)

The ${\dot{Ex}}_{in, DR}$, ${\dot{Ex}}_{out,DR}$, and exergy efficiency (${\eta }_{ex, DR}$) of the DR are calculated using Equations (21)–(23) [30,50].

$${\dot{Ex}}_{in, DR}={\dot{m}}_a{C}_{pa}\left(\left(T_{in,DR}-T_0\right)-T_{0}ln\left({\displaystyle \frac{T_{in,DR}}{T_0}}\right)\right)$$

(21)

$${\dot{Ex}}_{out, DR}={\dot{m}}_a{C}_{pa}\left(\left(T_{out,DR}-T_{ci}\right)-T_{0}ln\left({\displaystyle \frac{T_{out,DR}}{T_0}}\right)\right)$$

(22)

where $T_{in,DR}$ and $T_{out,DR}$ are inlet and outlet ATs from the DR, respectively.

$${\eta }_{ex,DR}={\displaystyle \frac{{\dot{Ex}}_{out, DR}}{{\dot{Ex}}_{in, DR}}}$$

(23)

2.3.5. Sustainability Indicators

During the current study, three $\dot{Ex}$ sustainability indicators were used to evaluate the sustainability performance of the developed AMMISD to address the irreversibilities and exergy losses in a process for a given exergy input. These indicators include IP, WER, and SI. These thermodynamic indicators provide a comprehensive framework for system evaluation. An inverse relationship exists between exergetic performance metrics: as exergy losses rise, the IP and WER increase, while the SI declines. Each parameter offers distinct insights: IP quantifies the system’s optimization capacity, WER measures exergy destruction per unit input, and SI reflects the system’s operational longevity. Together, they characterize the dryer’s irreversibility patterns, thermodynamic efficiency, and environmental sustainability. Engineers can optimize dryer design by minimizing energy degradation by carefully analyzing these indicators (IP, WER, and SI) [30]. The mathematical formulations for these parameters are derived as follows:

$$IP=\left(1-{\eta }_{ex}\right){\dot{Ex}}_{ls}$$

(24)

$$WER={\displaystyle \frac{{\dot{Ex}}_{ls}}{{\dot{Ex}}_{in}}}$$

(25)

$$SI={\displaystyle \frac{1}{1-{\eta }_{ex}}}$$

(26)

2.3.6. Economic Analysis

A comprehensive economic analysis was conducted to evaluate the commercial viability of the developed AMMISD. This assessment focused on determining its commercial sustainability within the Egyptian financial context. Following established methodologies in renewable energy systems evaluation [51,52,53], three key performance indicators were analyzed: (1) annualized investment cost ($C_a$), (2) payback time ($N$), and (3) net cost saving value ($S_j$). These metrics collectively provide a robust framework for assessing the system’s economic performance. The annualized investment cost ($C_a$) of the AMMISD was calculated using the parameters specified in Equations (27)–(29). This financial analysis approach ensures accurate projection of the system’s long-term economic feasibility while accounting for local market conditions and capital investment requirements.

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

(27)

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

(28)

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

(29)

where $C_{ac}$ is the annual capital cost of the developed AMMISD, $V_a$ is the salvage value of the developed AMMISD, $C_m$ is the maintenance cost (8% of the $C_{ac}$), $n$ is the operational life (20 years for the developed AMMISD and 25 years for the PV system), $C_{cc}$ is the total capital cost, $F_c$ is the capital recovery factor, and d is the interest rate (equal to 15%).

The drying cost per kg of date fruit ($C_s$) using the developed AMMISD ($C_s$) can be calculated using Equation (30) [51,52,53].

$$C_s={\displaystyle \frac{C_a\times D_d}{M_d\times D }}$$

(30)

where D is the number of available drying days per year, $M_d$ is the amount of date fruit dried per batch, and $D_d$ is the drying time per batch.

The total cost of one kilogram of the dried date fruit ($C_{ds}$) using the developed AMMISD [51,52,53] can be obtained as follows:

$$C_{ds}=C_{fd}\times {\displaystyle \frac{M_f}{M_d}}+C_s$$

(31)

where ${\displaystyle \frac{M_f}{M_d}}$ is the ratio between fresh and dried date fruit per batch.

The cost savings per one kilogram of dried date fruit ($S_{kg}$) is estimated using Equation (32).

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

(32)

where ${SP}_c$ is the selling price of dried date fruit per kilogram.

The cost savings ($S_j$) from the developed AMMISD following (j) years can be obtained by Equation (33).

$$S_j={\displaystyle \frac{S_{kg}\times M_d}{D}}\times D\times {\left(1+j\right)}^{j-1}$$

(33)

The payback time (N) of the investigation cost is obtained according to Equation (34) [51,52,53,54].

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

(34)

where i is the inflation rate (equal to 36%), and $S_1$ is the cost savings after the first year.

3. Results and Discussion

3.1. Moisture Content (MC)

To evaluate the performance of the developed AMMISD, approximately 10 kg of five distinct date varieties native to the Aswan area, namely Shamia, Bartamuda, Sakkoti, Malkabii, and Gondaila, were purchased from a local market in Aswan, Egypt, for the study. Figure 10 shows the moisture content of different date varieties dried by the developed AMMISD during the current study. The initial moisture content was 15.7%, 16.7%, 16.2%, 17.2%, and 16.1% for the date varieties Shamia, Bartamuda, Sakkoti, Malkabii, and Gondaila, respectively. The drying process took about 90 h to reach the equilibrium moisture content and contact weight. The final moisture content was 4.91%, 5.89%, 5.39%, 6.41%, and 5.28% for the date varieties Shamia, Bartamuda, Sakkoti, Malkabii, and Gondaila, respectively. High sugar content leads to a long drying time because sugar is hygroscopic. It attracts and holds water molecules, making it harder for moisture to evaporate from the food product. Additionally, high sugar content lowers the effective water activity in the material, slowing down the drying process because water is more tightly bound. Thus, the drying of dates must be at a medium temperature range between 60 and 70 °C to maintain the sugar content and quality of dried dates [1,2]. The previous studies showed that the Aswan date varieties take about 14 days (140 h) to reach the equilibrium MC using traditional indirect SD and about 14–25 days (140–250 h) in open air under direct sun rays [1,2,11].

3.2. Weather Conditions During the Drying Experiments

Figure 11, Figure 12 and Figure 13 illustrate the ambient AT and solar radiation intensity throughout the 7 days of field tests from 8:00 a.m. to 6:00 p.m. for 10 h per day, where all drying processes of different date varieties were conducted from 3 to 8 October 2024 at Aswan University, Aswan, Egypt. The solar intensity and ambient ATs were measured by the weather station of the Faculty of Agriculture and Natural Resources, Aswan University. The presented data in the same figure show that solar radiation gradually increased, peaking at approximately 870 W/m² at 12 p.m. The AT followed a similar trend, reaching a maximum of 33 °C at 12 p.m. Also, Figure 12 shows that the outlet AT from the SAC ranged between 28 and 66 °C at 8 a.m. and 12 p.m., respectively, while the outlet AT from the DR ranged between 23.6 and 39.2 °C at 8 a.m. and 12 p.m., respectively. All measured data of AT include an average air mass flowrate of 0.078 kg/s.

3.3. Energy Analysis

3.3.1. Energy Analysis of the SAC

Figure 14 shows the energy analysis of the SAC, where the input energy (${\dot{Q}}_{in, \mathrm{S}\mathrm{A}\mathrm{C}}$), useful energy (${\dot{Q}}_{u, \mathrm{S}\mathrm{A}\mathrm{C}}$), loss energy (${\dot{Q}}_{ls, \mathrm{S}\mathrm{A}\mathrm{C}}$), and efficiency (${\eta }_{en, SAC}$) were calculated hourly based on the solar radiation flux and the difference between the AT the input and output of the developed AMMISD. The ${\dot{Q}}_{in, \mathrm{S}\mathrm{A}\mathrm{C}}$ to the SAC refers to the total solar radiation received by the SAC. It depends on solar irradiance, SAC area, and orientation. During the current study, the total ${\dot{Q}}_{in, \mathrm{S}\mathrm{A}\mathrm{C}}$ to the SAC ranged between 696 and 2610 W. Figure 14 shows the ${\dot{Q}}_{u, \mathrm{S}\mathrm{A}\mathrm{C}}$ supplied by the developed AMMISD (estimated using Equation (10)) while drying different date varieties. The ${\dot{Q}}_{u, \mathrm{S}\mathrm{A}\mathrm{C}}$ was between 111.41 and 1643.35 W, depending on solar radiation, as shown in Figure 14. It was observed that ${\dot{Q}}_{u, \mathrm{S}\mathrm{A}\mathrm{C}}$ was greater because air exhaust fans run continuously. The energy efficiency of the SAC of the developed AMMISD was found (using Equation (11)) and is shown in Figure 14. Due to its relationship with ${\dot{Q}}_{u, \mathrm{S}\mathrm{A}\mathrm{C}}$, the ${\eta }_{en, SAC}$ increased until noon and then decreased at 5 p.m. The ${\eta }_{en, SAC}$ was in the range of 15.15% and 63.33%. The maximum ${\eta }_{en, SAC}$ was observed at 12 p.m. because of higher solar radiation.

Table 3. Previous studies that have examined the ${\eta }_{en, SAC}$ of different types of SAC.

Reference	Type	${\mathit{\eta }}_{\mathit{e}\mathit{n},\mathit{S}\mathit{A}\mathit{C}}$, %
Fudholi et al. [55]	Natural and forced SAC	62%
Luan et al. [56]	Multi-pass SAC	52.1%
Rezaei et al. [57]	SAC without phase-change material	52.1%
Rezaei et al. [57]	Bobbin absorber plate without phase-change material	36.3%
Rezaei et al. [57]	SAC with phase-change material	12.9%
Lingayat et al. [58]	SAC with V-corrugated absorption plates	31.50%
Hegde et al. [59]	Top and bottom flow SAC	50.0%
Şevik et al. [33]	Double-pass SAC with and without infrared assistance	1.15% to 26.46%
Chowdhury et al. [34]	Tunnel SD	27.45% to 42.50%
Lingayat et al. [49]	Flat plate SAC	45.32%
Current study	The developed AMMISD	63.33%

presents a comparison between the obtained ${\eta }_{en, SAC}$ and that previously studied.

3.3.2. Energy Analysis of the Developed AMMISD

Figure 15 shows the drying efficiency (${\eta }_{en, Dryer}$) of the developed AMMISD. and measurements of how effectively it converts solar energy into usable heat for the drying process for different date varieties. This was calculated as the ratio of energy used for moisture removal from different date varieties to the total energy input by the SAC and estimated using Equation (12). Similar to ${\eta }_{en, SAC}$, it was also higher at 12 p.m. because of higher solar radiation. The efficiency of the developed AMMISD (${\eta }_{en, Dryer}$) varied according to the variety of dates. As shown in Figure 15, the highest efficiency of the developed AMMISD for each variety of dates was 4.13%, 4.39%, 4.01%, 4.41%, and 4.39% for Shamia, Bartamuda, Sakkoti, Malkabii, and Gondaila, respectively. Efficiency in drying was often measured by how quickly and effectively moisture was removed using available solar energy. If the drying time was prolonged due to high sugar content, the system uses more energy (over a longer period) to achieve the same result, making it less efficient. High sugar content leads to a long drying time because sugar is hygroscopic. It attracts and holds water molecules, making it harder for moisture to evaporate from the food product. Additionally, high sugar content lowers the effective water activity in the material, slowing down the drying process because water is more tightly bound. Since sugar retains moisture, more energy and time are required to remove the same amount of water compared to low-sugar materials [1,2,12]. While prolonged drying durations negatively impact SD performance, this effect is particularly pronounced in systems dependent on intermittent solar availability. Extended drying cycles risk extending into periods of diminished solar irradiance (e.g., evening hours), when reduced thermal input further compromises drying rates. The efficiency reduction manifests through three primary mechanisms: (1) progressive heat losses via conduction, convection, and radiation that accumulate with extended operation time; (2) declining drying rates in the afternoon due to both reduced solar intensity and decreasing moisture gradients in the product (particularly evident in our date samples); and (3) thermodynamic inefficiencies when removing the final moisture fractions to reach equilibrium conditions. In the developed AMMISD, these factors collectively contributed to a measurable efficiency decline after noon, as the system struggled to maintain effective moisture removal during the latter stages of drying when working with low residual moisture content in the dates. This phenomenon underscores the inherent challenge in solar drying systems when processing low-moisture products requiring precise final moisture content control. Future design improvements could significantly increase drying efficiency (${\eta }_{en, Dryer}$) to 8–10% by addressing thermal losses and energy recovery. Secondary glazing on dryer apertures would reduce convective heat loss, while enhanced insulation (e.g., aerogel or vacuum panels) could minimize conductive dissipation. Integrating phase-change materials (PCMs) for latent heat storage would stabilize temperatures during operational fluctuations, and a heat exchanger could recycle exhaust air enthalpy. Additionally, optimizing airflow patterns via CFD modeling or adopting hybrid solar-thermal/electric heating during low-irradiance periods may further boost performance. These measures collectively target the dominant inefficiencies in the current system, potentially doubling ${\eta }_{en, Dryer}$.

3.4. Exergy Analysis ($\dot{Ex}$)

3.4.1. Exergy Analysis of the SAC (${\dot{Ex}}_{SAC}$)

Figure 16 shows the temporal change of inlet exergy (${\dot{Ex}}_{in, SAC}$), outlet exergy (${\dot{Ex}}_{out, SAC}$), exergy loss (${\dot{Ex}}_{ls, SAC}$), and exergy efficiency for the SAC (${\eta }_{ex, SAC}$), derived utilizing Equations (15)–(19). ${\dot{Ex}}_{in, SAC}$, ${\dot{Ex}}_{out, SAC}$, and ${\dot{Ex}}_{ls, SAC}$ are directly proportional to solar radiation intensity, as illustrated in Figure 16. Consequently, these parameters rose from morning until midday and subsequently began to decline in the afternoon. The ${\dot{Ex}}_{in, SAC}$ was calculated based on the solar radiation intensity (Equation (19)). The ${\dot{Ex}}_{in, SAC}$ was assessed relative to ambient AT, ranging from 552.67 to 2069.95 W during the tests, with the peak value recorded at 12 p.m. The ${\dot{Ex}}_{out, SAC}$ for the developed AMMISD ranged from 3.93 W to 566.19 W. The ${\dot{Ex}}_{ls, SAC}$ for the developed AMMISD varied between 513.8 and 1646.28 W. ${\dot{Ex}}_{ls, SAC}$ was greater at midday due to the elevated solar radiation flux measured during that time (Figure 16). The average ${\dot{Ex}}_{in, SAC}$, ${\dot{Ex}}_{out, SAC}$, and ${\dot{Ex}}_{ls, SAC}$ for the developed AMMISD were determined to be 1347.4 W, 196.8 W, and 1150.7 W, respectively. The ${\eta }_{ex, SAC}$ with time was determined using Equation (23) and is illustrated in Figure 16. The ${\dot{Ex}}_{out, SAC}$ is mostly contingent upon AT, while the exergy inflow correlates with solar radiation, which remained almost identical in both configurations; thus, ${\eta }_{ex, SAC}$ was determined by outlet AT. It was noticed that ${\eta }_{ex, SAC}$ was between 0.67 and 27.5%, and the average ${\eta }_{ex, SAC}$ was 11.9%.

Table 4. Previous studies examined the ${\eta }_{ex, SAC}$ of different types of SDs.

Reference	Temperature	Airflow	Type	${\mathit{\eta }}_{\mathit{e}\mathit{x},\mathit{S}\mathit{A}\mathit{C}}$
Mugi and Chandramohan [60]	32–63 °C	Not available	Forced convection indirect SD	2.44%
	34–69 °C	Not available	Natural convection indirect SD	2.03%
Selimefendigil et al. [31]	60 °C	0.010–0.016 kg/s	Active greenhouse SD with Al2O3 nano-embedded latent heat thermal storage system	3.45%
Chowdhury et al. [34]	54.2 °C	Not available	Solar drying of jackfruit leather in a tunnel SD	41.42%
Ekka et al. [32]	36–62 °C	0.018–0.062 kg/s	Mixed-mode SD with dual double-pass SACs	18.8 to 41.4%
Tiwari and Tiwari [40]	29–122.78 °C	Not available	Hybrid mixed-mode greenhouse SD, integrated with partially covered number of photovoltaic thermal (PVT) SACs	19.11 to 28.96%
Chowdhury et al. [34]	54.2 °C	Not available	Tunnel SD	32 to 69%
Abdelkader et al. [41]	26.3–60.3 °C	0.031–0.0381 m3/s	Carbon nanotubes-based SD	8.1 to 11.9%
Lingayat et al. [49]	28–82 °C	Not available	Using indirect-type natural convection SD	7.4 to 45.23%
Current study	28–66 °C	0.09 m3/s	The developed AMMISD	27.5%

shows previous studies that have examined the ${\eta }_{ex, SAC}$ of different types of SDs.

3.4.2. Exergy Analysis of the DR (${\dot{Ex}}_{DR}$)

The ${\dot{Ex}}_{in, DR}$, ${\dot{Ex}}_{out,DR}$, and ${\dot{Ex}}_{ls, DR}$ were estimated using Equations (20)–(23) and are shown in Figure 17. The ${\dot{Ex}}_{in, DR}$ predominantly depends on the outlet AT from the SAC (DR inlet). The ${\dot{Ex}}_{in, DR}$ and ${\dot{Ex}}_{out,DR}$ were in the range of 3.93–586.49 W and 0.65–355.97 W, respectively. Additionally, the ${\dot{Ex}}_{ls, DR}$ was from 1.01 to 287.9 W. From Figure 17, it can be observed that ${\dot{Ex}}_{in, DR}$, ${\dot{Ex}}_{out,DR}$, and ${\dot{Ex}}_{ls, DR}$ were higher at noon because the AT at the room inlet was higher at noon. The ${\dot{Ex}}_{in, DR}$, ${\dot{Ex}}_{out,DR}$, and ${\dot{Ex}}_{ls, DR}$ depend on the mass flow rate of the air and the AT. In the developed AMMISD, the DR inlet AT was moderate and ranged between 28 and 66 °C, but the mass flow rate was higher (about 0.078 kg/s). Since DR inlet AT dominates the ${\dot{Ex}}_{in, DR}$, ${\dot{Ex}}_{out,DR}$, and ${\dot{Ex}}_{ls, DR}$, these were higher for the developed AMMISD (Figure 17) setup. Figure 17 shows the variation of the ${\eta }_{ex, DR}$ according to time of day. It was calculated using Equation (23). Therefore, the DR outlet AT was closer to the DR inlet AT. The ${\eta }_{ex, DR}$ was between 11.92 and 96.62%. The ${\eta }_{ex, DR}$ increased with drying time because ${\dot{Ex}}_{out,DR}$ per ${\dot{Ex}}_{in, DR}$ increased with time due to a lower drop of AT in the DR with time. It means that at the end of drying, the AT drop in the DR was much smaller since the loss of moisture from the products was lower at the end of drying.

Table 5. Previous studies that have examined the ${\eta }_{ex, DR}$ of different types of SDs.

Reference	Temperature	Airflow	Type	${\mathit{\eta }}_{\mathit{e}\mathit{x},\mathit{D}\mathit{R}}$
Mugi and Chandramohan [60]	32–63 °C	Not available	Forced convection indirect SD	16.19–97.75%
	34–69 °C	Not available	Natural convection indirect SD	15.17–91.08%
Shringi et al. [35]	50.85–97.85 °C	Not available	SD using phase-change material as energy storage	67.06–88.24%
Panwar [36]	36–56 °C	Not available	Natural convection SD	55.35–79.35%
Ndukwu et al. [37]	30–45 °C	Not available	SD integrated with sodium sulfate decahydrate and sodium chloride as thermal storage medium	66.67–96.09%
Kesavan et al. [38]	62 °C	0.062 kg/s	Triple-pass SD	2.8–87.02%
Chowdhury et al. [34]	54.2 °C	Not available	Tunnel SD	41.42%
Akpiner et al. [61]	60–80 °C	1.0–1.5 m/s	Two-tray hot air cyclone dryer	18–100%
Akpinar [62]	55–70 °C	1.5 m/s	Laboratory tray dryer	67.27–97.29%
Akpinar [63]	60–85 °C	0.5–1.5 m/s	Experimental tray dryer	24.81–100%
Akpinar et al. [64]	60–80 °C	1.5 m/s	Two-tray hot air cyclone type dryer	32–100%
Ghasemkhani et al. [65]	50–80 °C	1.0–2.0 m/s	Rotating tray dryer equipped with heat exchanger	23–96.1%
Midilli and Kucuk [66]	40–60 °C	1.23 m/s	Forced convection solar dryer	15.65–100%
Akpinar et al. [67]	60–80 °C	1.0–1.5 m/s	Two-tray hot air cyclone type dryer	19.4–100%
Corzo et al. [68]	71–93 °C	0.82–1.18 m/s	Thin-layer air dryer	80–97%
Çolak et al. [69]	40–50 °C	0.01–0.05 kg/s	Ground source heat	76.03–97.24%
Karthikeyan and Murugavelh [39]	42.2–82.8 °C	Not available	Mixed-mode forced convection solar tunnel dryer	23.25–73.31%
Current study	28–66 °C	0.09 m3/s	The developed AMMISD	11.92 and 96.62%

illustrates the previous studies that have examined the ${\eta }_{ex, DR}$ of different types of SDs.

3.5. Sustainable Indicators

The exergy sustainability indicators, including IP, WER, and SI, were computed for the developed AMMISD to evaluate ${\dot{Ex}}_{ls, DR}$ and ${\eta }_{ex, DR}$ concerning the total ${\dot{Ex}}_{in, DR}$. These indicators are essential for assessing the system’s performance and refining the architecture of the DR. Through the examination of these indicators, engineers may pinpoint inefficiencies, minimize energy waste, and improve the sustainability of the drying process, therefore ensuring more efficient and environmentally friendly operations. The IP in exergy-based sustainable indicators quantifies the capacity to improve ${\eta }_{ex, DR}$ and decrease WER in systems. It measures the disparity between present and ideal performance, directing initiatives to reduce ${\dot{Ex}}_{ls, DR}$. By recognizing inefficiencies, IP facilitates sustainable growth, resource conservation, and the shift to cleaner, more efficient energy systems. IP is a function of exergy dissipation (Equation (24)). The observed IP ranged from 4.62 to 13.64 W (Figure 18). This indicates a reduced level of ${\dot{Ex}}_{ls, DR}$ in the current configuration [40,68]. The WER and SI of the developed AMMISD were computed using Equations (25) and (26), as depicted in Figure 18. WER values diminished with rising AT, with the lowest values recorded around 12 p.m. The mean value of WER was 0.84. On the other hand, the SI value ranged from 1.01 to 1.38.

Table 6. Comparison between the observed sustainable indicators and previous studies.

Reference	Type	Crop	Sustainable Indicators
			IP	WER	SI
Mugi et al. [30]	Natural convection SD	Okra	0.035 to 12.75 W	0.41 to 0.445	3.69
	Forced convection SD	Okra	9.5 × 103 to 10.51 W	0.41	5.1
Akpinar et al. [70]	Forced convection SD	Pepper	0 to 17 W	0.38 to 0.55	0.393 to 6.156
Ndukwu et al. [71]	Hybrid solar-biomass dryer	--	0.036 to 20.6 W	0.38 to 0.55	2.3 to 6.11
Ekka et al. [32]	Forced convection mixed-mode SD	Cluster figs	--	--	1.26 to 1.71
Current study	Developed AMMISD		4.62 to 13.64 W	0.69 to 0.94	1.01 to 1.38

shows the comparison between the observed sustainable indicators and previous studies.

3.6. Economic Analysis

This study performed a thorough economic analysis to assess the financial viability of incorporating photovoltaic technology with the developed AMMISD for drying date fruit. The economic analysis findings are presented in

Table 7. Various costs related to the AMMISD and PV system.

Cost Parameters	AMMISD Integrated with PV System
Capital cost, USD
I. Metal frame	300
II. PV system	70
III. Electronic and electrical components	150
Lifespan, years	45
Annual capital cost, USD	106.56
Annual maintenance cost, USD	3.196
Annual salvage value, USD	8.525
Annual investment cost, USD	101.23
Labor cost, USD/month	100

and

Table 8. Economic analysis of the AMMISD integrated with PV system for drying date fruit.

Economic Parameters	AMMISD Integrated with PV System
Mass of date fruit dried per batch, kg	35
Number of drying days per patch, day	6
Quantity of dried date fruit annually, kg	210
Drying cost of per kg of date fruit, USD	0.48
Cost of 1 kg fresh date fruit, USD	1
Total cost of 1 kg of crop dried date fruit, USD	1.176
Selling price per kg of date fruit, USD	3
Saving after 1 year, USD	236.9
Payback time, years	2.091

, employing life-cycle cost assessment and payback period methodologies while accounting for the prevailing market conditions in Egypt and anticipated component costs. The tabulated results indicate substantial economic advantages for date fruit drying, yielding annual savings of approximately USD 236.9—realized within a 30-day validation period (harvesting season). The projected yield of dried date fruit generated by the new AMMISD was approximately 210 kg, with an investment recovery period of about 2.091 years. This duration constitutes merely 10.455% of the AMMISD’s 20-year lifespan and 9.5% of the PV system’s 25-year lifespan, demonstrating its significant cost effectiveness. Moreover, the AMMISD’s versatility for year-round multi-crop drying amplifies its commercial viability, as outlined in Table 8. The findings confirm the PV-AMMISD system as a financially feasible option for industrial-scale dried fruit production in Egypt and comparable climates.

4. Conclusions

The current study aimed to integrate electronic control circuits with sensors and microcontrollers to automate temperature, humidity, and airflow regulation. This method eliminates manual intervention, optimizes energy use, and ensures precise drying conditions. The current study also aimed to look at the AMMISD from a thermodynamic point of view to see how well it worked in terms of energy and exergy as well as estimating the sustainability indicators of the developed AMMISD. The developed AMMISD was used for drying five date varieties native to the Aswan area, namely Shamia, Bartamuda, Sakkoti, Malkabii, and Gondaila.

➢

The initial and final moisture content of date varieties ranged between 15.7% and 17.2% and 4.91% and 6.41%, respectively. And all date fruit varieties reached the equilibrium moisture content after 6 days (60 h);

➢

The ${\dot{Q}}_{in, \mathrm{S}\mathrm{A}\mathrm{C}}$ and ${\dot{Q}}_{u, \mathrm{S}\mathrm{A}\mathrm{C}}$ ranged between 696 and 2610 W and 111.41 and 1643.35 W, respectively, depending on solar radiation, and the ${\eta }_{en, SAC}$ was in the range of 15.15%–63.33%. The maximum ${\eta }_{en, SAC}$ was observed at 12 p.m.;

➢

The highest ${\eta }_{en, Dryer}$ for each date variety was 4.13%, 4.39%, 4.01%, 4.41%, and 4.39% for Shamia, Bartamuda, Sakkoti, Malkabii, and Gondaila, respectively;

➢

The ${\dot{Ex}}_{in, SAC}$, ${\dot{Ex}}_{out, SAC}$, and ${\eta }_{ex, SAC}$ ranged between 552.67 and 2069.95 W, 3.93 W and 566.19 W, and 0.67 and 27.5%, respectively;

➢

The IP was in the range of 4.62 to 13.64 W, while the SI and WER varied from 1.01 to 1.38 and 0.69 to 0.94, respectively;

➢

The results of the economic analysis indicated substantial economic advantages for date fruit drying, yielding annual savings of approximately USD 236.9—realized within a 30-day validation period (harvesting season);

➢

The investment recovery period was about 2.091years. This period constitutes merely 10.455% of the AMMISD’s 20-year lifespan and 9.5% of the PV system’s 25-year lifespan, demonstrating its significant cost effectiveness.