Performance evaluation of a triple-sided solar dryer in terms of energy-exergy analysis, sustainable indicators and CFD simulation during drying tilapia fish strips

Abstract

Fixed flat-plate solar collectors suffer from low energy efficiency during mornings and evenings due to suboptimal solar incidence angles, reducing thermal output. While tracking systems improve efficiency by following the sun’s path, their high initial costs, mechanical complexity, and need for advanced control systems limit widespread adoption. These drawbacks demonstrate the importance of cost-effective, efficient alternatives that balance performance and simplicity in solar thermal applications. Thus, a triple-sided solar dryer (TSSD) integrated with intelligent airflow gating was developed to overcome these issues. This study evaluates the performance of a TSSD for drying tilapia strips at three thicknesses (4, 8, and 12 mm) using computational fluid dynamics (CFD), energy-exergy analysis, and sustainability indicators. According to the CFD simulations, they were employed to analyze airflow patterns, temperature distribution, and velocity profiles inside the TSSC and drying room (DR) during a day from 8 a.m. to 5 p.m. Additionally, the CFD was used to estimate the highest air temperature inside the drying to choose the appropriate speed of the air exhaust fan. The simulation analysis indicated that the highest air temperatures were 188.67, 124.4, and 96.51 °C, at three corresponding air velocities of the exhaust fan (1.0, 1.5, and 2.0 m/s), respectively, under a solar intensity of 872 W/m². Where the best velocity of the air exhaust fan was 2 m/s, it provided a uniform drying temperature of 96.51 °C, at solar noon (less than 100 °C). On the other hand, the energy-exergy analysis and sustainable indicators were estimated over two consecutive drying days (8 a.m.–5 p.m.) to assess the thermal behavior of the TSSC and TSSD. The energy analysis showed that the TSSC attained a maximum input energy of 1752.72 W and a useful energy of 810.31 W. Its energy efficiencies ranged from 40.79% to 57.21%. Meanwhile, the maximum drying efficiency was 8.19%, 8.51%, and 8.46% for tilapia strip thicknesses of 4, 8, and 12 mm, respectively. Furthermore, the exergy efficiency ranged from 7.28% to 32.83% (TSSC) and from 66.5% to 87.19% (DR). Additionally, sustainability indicators, such as improvement potential (IP) ranging from 1.19 to 7.22 W, waste exergy ratio (WER) between 0.67 and 0.93, and sustainability index (SI) from 1.08 to 1.49, showed that the system is both environmentally friendly and effective in its operations. The results show that the TSSD is an effective, eco-friendly, and affordable option compared to traditional solar drying systems, providing the best heat performance, better energy-exergy efficiency, and less harm to the environment for drying tilapia.

Introduction

Drying is a crucial preservation method for aquatic products, extending shelf life while maintaining nutritional value^1,2. However, traditional drying techniques often rely on fossil fuels, leading to high energy costs, environmental pollution, and inconsistent product quality^3–5. Solar dryers offer a sustainable alternative by utilizing renewable solar energy, reducing dependence on non-renewable resources and minimizing carbon emissions^6–10. By harnessing solar thermal energy, these dryers significantly lower operational costs compared to conventional methods, making them economically viable for small and large-scale producers^11–17. Additionally, solar drying enhances product quality by providing controlled temperature and airflow, reducing over-drying or microbial contamination^18,19. This method also helps preserve proteins, vitamins, and essential fatty acids in seafood, improving marketability^2,20. Given the global push toward sustainability, solar dryers present a practical solution to energy consumption challenges in food processing, aligning with climate goals while ensuring food security. Their adoption can revolutionize the drying industry by offering an eco-friendly, cost-effective, and efficient approach to preserving aquatic products^21–24.

Fixed flat plate solar collectors offer simplicity and lower maintenance compared to tracking systems, but suffer from reduced efficiency as they cannot follow the sun’s movement, leading to suboptimal solar incidence angles throughout the day^18,22,26. This results in approximately 20–30% lower energy collection compared to single-axis tracking systems, and 30–40% less than dual-axis tracking collectors, particularly during morning and afternoon hours when the sun’s angle deviates significantly from the collector’s fixed orientation. Additionally, fixed collectors exhibit higher thermal losses in cold or cloudy conditions due to prolonged exposure to non-ideal angles, while tracking systems maintain near-perpendicular alignment, maximizing irradiance absorption^18,22,26. Solar dryers incorporating sun tracking systems represent a significant technological advancement in agricultural processing, designed to optimize solar energy capture by continuously aligning with the sun’s path throughout the day^27–29. These systems demonstrate substantial improvements in both drying efficiency and product quality compared to conventional fixed-position dryers. Research consistently shows that tracking mechanisms enhance thermal efficiency by 21% to 45% while reducing drying times by 16% to 36%, with specific performance metrics varying according to crop characteristics and system configuration^30–32. Beyond efficiency gains, tracking systems contribute to superior product outcomes by ensuring more uniform moisture removal and better preservation of nutritional content, as documented in studies involving delicate products like tea leaves and tomatoes^33,34. The ability to maintain consistent, elevated temperatures within the drying chamber proves particularly valuable for processing a wide range of agricultural commodities^35,36. However, these performance benefits come with notable technical complexities, as implementation typically requires sophisticated components including programmable logic controllers, precision sensors, and electromechanical actuators, all of which increase system complexity and maintenance requirements^37,38. While modern automated trackers achieve impressive alignment accuracy, even minor deviations of approximately 3.25 degrees can measurably impact system performance³³, and simpler manual or limited-range tracking systems may offer only marginal improvements over fixed installations³⁵. From an economic perspective, while the initial capital investment for tracking systems exceeds that of fixed dryers, numerous studies demonstrate favorable payback periods due to energy savings and enhanced product value^32,36, with many designs incorporating photovoltaic elements to minimize external energy requirements for the tracking mechanism itself³³. Practical implementation challenges persist, particularly regarding scalability for diverse crops and operational environments, with system complexity presenting barriers to adoption in resource-constrained settings^37,39. Ongoing reliability concerns stem from the increased maintenance needs of mechanical and electronic components, especially in remote agricultural locations³⁰, while the current literature reveals gaps in comprehensive environmental impact assessments and detailed quality evaluations across different product categories⁴⁰. Future development efforts should prioritize the refinement of tracking algorithms, cost reduction strategies, and system simplification to enhance accessibility^30,32.

Recent literature provides a detailed comparison of single- and double-sided solar collectors, emphasizing their relative efficiencies and the impact of solar incidence angles on performance. Double-sided collectors generally demonstrate higher thermal efficiency and energy collection rates compared to single-sided designs, with reported efficiency gains ranging from 4% to 12% depending on collector type and operating conditions^41–45. For example, double exposure solar air collectors can be 4.17% more efficient than single exposure types at specific mass flow rates⁴², and double flow flat plate collectors show a 10–12% efficiency increase over single flow designs^41,46. In photovoltaic applications, double-sided collection can yield up to a 0.4% absolute efficiency gain, though this is sometimes offset by a decrease in open circuit voltage^47,48. A key limitation for both single- and double-sided collectors is reduced efficiency during morning and evening hours, primarily due to suboptimal solar incidence angles. Optical analyses reveal that as the angle of incidence deviates from normal (perpendicular), both optical and thermal efficiencies decline, with single-sided collectors being more susceptible to these losses^44,46. Double-sided collectors, especially those with optimized reflector designs, can partially mitigate these losses by capturing diffuse and reflected sunlight, but their performance still drops at low solar angles^44,46. Ray-trace studies confirm that angular acceptance and flux distribution are critical factors, and truncated reflector designs can help reduce related losses but do not fully eliminate the morning/evening performance gap⁴⁶.

Building upon the need for more efficient solar drying technologies, the current study was designed to develop and comprehensively evaluate an innovative solar dryer integrated with triple-sided solar collector (TSSC) and intelligent airflow gating with enhanced solar energy utilization capabilities. The research methodology incorporated a multi-phase approach beginning with advanced CFD simulations to model and optimize the system’s thermal and aerodynamic performance. Following the simulation phase, the developed triple-sided solar dryer (TSSD) prototype was subjected to rigorous experimental testing using salted tilapia strips (STS) as the drying medium, with samples prepared to evaluate the system’s performance across different product geometries. The study further incorporated a robust energy-exergy analysis framework to quantify the system’s thermodynamic efficiency and identify potential energy losses. This dual assessment approach yielded critical sustainability indicators including improvement potential, waste exergy ratio, and sustainability index, providing a holistic evaluation of the TSSD’s environmental and operational performance. The integration of numerical simulation with physical experimentation and thermodynamic analysis represents a novel methodological approach in solar dryer development, offering insights into both the fundamental heat and mass transfer processes and the practical drying performance for aquatic products. By examining multiple thickness variations of STS, the study additionally contributes valuable data on the scalability and adaptability of the technology for different product specifications in the seafood processing sector.

Materials and methods

This study holds significant value for the community as it demonstrates how a triple-sided solar dryer can efficiently and sustainably dry tilapia fish strips, a vital protein source in many regions, by optimizing energy use, minimizing waste, and reducing reliance on fossil-fuel-based drying methods. Through energy–exergy analysis, sustainable performance indicators, and CFD simulations, the research provides scientific evidence for improving dryer design and operation, enabling small-scale fish processors and rural communities to enhance product quality, extend shelf life, and increase income while lowering environmental impact. By promoting renewable-energy-based food preservation, the findings support food security, economic development, and environmental sustainability in fish-producing communities.

Description of the TSSD.

The TSSD was developed and evaluated in the New and Renewable Energy Unit, Food Technology Research Institute, Agricultural Research Center, Giza, Egypt. Figure 1 shows the main components of the developed TSSD.

Fig. 1.

Main components of the developed TSSD.

As shown in Fig. 2, the developed TSSD features a compact yet efficient design with external dimensions of 100 cm (length) × 95 cm (width) × 0.95 m (height). The drying chamber is constructed in a triangular prism shape, which enhances structural stability while optimizing heat distribution. To maximize solar energy absorption, the developed TSSD was equipped with three sides solar collectors (TSSC) strategically positioned on the upper (top), eastern, and western sides of the dryer.

Fig. 2.

Main dimensions of the developed TSSD.

The drying chamber incorporates a double-walled insulated door at the rear, constructed with an air gap between layers to significantly reduce conductive heat loss. This design feature maintains stable internal temperatures while preventing excessive energy dissipation to the external environment. The chamber’s exterior is fully enclosed by the TSSC, which serves a dual purpose:

• Solar Energy Absorption: The TSSC’s three-panel configuration (top, east, and west-facing) continuously captures solar radiation, converting it into thermal energy.

• Thermal Insulation: By enveloping the drying chamber, the TSSC creates a buffer zone where the outer walls of the chamber remain at elevated temperatures.

• Temperature Distribution and Heat Retention.

• Outer Wall Temperatures: Due to direct exposure to the TSSC’s absorbed heat, the external surfaces of the drying chamber’s walls maintain higher temperatures than the interior surfaces.

• Inner Wall Conditions: The temperature gradient between the outer and inner walls ensures that heat flows inward toward the drying trays, rather than escaping outward.

• Minimized Heat Loss: This configuration effectively reduces convective and radiative losses, as the warm outer walls prevent ambient cooling from affecting the internal drying environment.

• Air Gap Insulation: The space between the TSSC and the chamber walls acts as a passive insulating layer, further slowing heat transfer to the surroundings.

As shown in Fig. 2, the stainless-steel drying tray was securely mounted inside the drying chamber using a precision digital balance to enable continuous gravimetric monitoring. Throughout the dehydration process, the weight of the tilapia strip samples was recorded at hourly intervals, allowing for precise tracking of moisture loss dynamics. This instrumentation provided real-time mass measurements with an accuracy of ± 0.1 g, facilitating the calculation of moisture ratio and drying rates at each time point. The hinged tray design permitted easy access for sample loading and unloading while maintaining consistent positioning during measurements to ensure data reliability.

The developed TSSD incorporates a precisely engineered air circulation system to ensure optimal thermal uniformity. A centrifugal electric fan (100 W power rating, axial flow type) continuously drives heated air through the drying chamber at a regulated velocity of 2 m/s (± 0.05 m/s tolerance). This forced convection system creates a consistent laminar airflow pattern that eliminates hot spots and ensures even heat distribution across all drying trays. Complementing the circulation system, an exhaust subsystem maintains proper humidity control. A high-efficiency suction fan (150 W nominal power, backward-curved blade design) actively evacuates moisture-laden air through a dedicated chimney duct. The exhaust stream passes through a multi-stage filtration system featuring: A primary silica gel desiccant bed (1 kg capacity, 3–5 mm bead size) for active moisture absorption. A secondary particulate filter (10 μm mesh) to prevent product contamination. And an adjustable damper system for precise airflow regulation. The balanced airflow ensures a uniform drying environment while minimizing energy consumption through optimized air recirculation (approximately 70% of heated air is reused).

The developed TSSD was designed in a way to maximize the absorbed energy during morning and night hours by dividing the solar collector to three sides (front side, right side and left side). As shown in Fig. 3, each solar collector of the TSSC incorporates a double-walled insulated construction, with a 3 mm thick transparent glass cover on the outermost layer. This design allows short-wave solar radiation to penetrate and strike the black-painted absorber plate, where it is converted into long-wave thermal energy. The trapped heat is then transferred into the drying chamber, ensuring consistent temperatures for effective dehydration (Fig. 3).

Fig. 3.

Air circulation map inside the TSSC. Whereas numbers from 1 to 4 are the air gates.

• Primary Collector (Inclined, South-Facing): Positioned at a 30° inclination angle in the southward horizontal direction, this collector maximizes solar exposure during peak daylight hours, ensuring optimal energy absorption.

• Secondary Collector (Vertical, East-Facing): This auxiliary collector captures morning sunlight, supplementing the primary collector by preheating the system and improving early-day drying efficiency.

• Tertiary Collector (Vertical, West-Facing): Designed to absorb afternoon and evening solar radiation, this collector compensates for the declining angle of the sun, extending the effective drying period.

The TSSC incorporates an intelligent gate control system to optimize solar energy capture throughout daylight hours. The system automatically manages airflow between three distinct collector units:

1. (1) Primary Collector (south-facing, 30° inclination).

2. (2) Secondary Collector (east-facing, vertical).

3. (3) Tertiary Collector (west-facing, vertical).

System Configuration:

• The TSSC features three interconnected collector sections linked by four motorized gates (Gate 1 to Gate 4, as illustrated in Fig. 4).

• Precision servo motors control each gate’s operation.

• An Arduino Uno microcontroller coordinates the system using programmed time-based algorithms.

Fig. 4.

Temporal operation protocol of the automated air circulation system inside the TSSC, and the main electronic parts of the control circuit.

Temporal operation protocol:

Step (I): Morning phase (08:00–10:00): Solar exposure concentrates on the east-facing secondary collector.

Gate configuration:

• Gates 1 and 2 remain closed (isolating west-facing collector).

• Gates 3 and 4 open (enabling airflow through primary and secondary collectors).

• Thermal management prevents heat loss through inactive collector surfaces.

Step (II): Midday phase (11:00–14:00): Maximum solar irradiance occurs across all collector surfaces.

Gate configuration:

• All four gates open completely.

• System achieves peak efficiency by utilizing both direct and reflected sunlight.

Step (III): Afternoon Phase (15:00–17:00): Solar exposure shifts to west-facing tertiary collector.

Gate configuration:

• Gates 3 and 4 close (isolating east-facing collector).

• Gates 1 and 2 open (maintaining airflow through primary and tertiary collectors).

System Advantages:

• Adaptive solar tracking without mechanical collector rotation.

• Intelligent thermal management reduces energy losses.

• Cost-effective automation using open-source hardware.

• Maintains consistent thermal output throughout daylight hours.

Experimental set-up

All drying experiments were conducted during June 2024 at the Agricultural Research Center in Giza, Egypt (30°01’N, 31°13’E), where the developed TSSD was operated under controlled conditions with a maintained air velocity of 2.0 ± 0.1 m/s throughout the drying chamber. Daily trials ran from 08:00 to 17:00 h to coincide with peak solar intensity periods, using STS purchased from a local Aswan fish market to ensure regional relevance. The study systematically evaluated three standardized strip thicknesses (4, 8, and 12 mm) based on optimal dehydration parameters established in prior research⁴⁹. Sample preparation followed a rigorous protocol: purchased Nile tilapia were first washed with potable water, manually filleted to remove skin and bones, then precision-cut to the target thicknesses using calibrated food processing equipment. Prepared strips underwent uniform brining in a 5% (w/v) NaCl solution for 15 min at 25 ± 2 °C (Fig. 5) before being arranged in single layers on drying trays. Moisture content (MC) was monitored hourly using a precision balance (± 0.1 g) until reaching equilibrium moisture content (EMC), defined as less than 0.5% weight variation over three consecutive measurements, ensuring complete dehydration while preserving product quality.

Fig. 5.

Step-by-step drying process flow diagram using the developed TSSD.

A comprehensive monitoring protocol was implemented throughout the drying experiments, with hourly measurements recorded for four critical parameters: (1) sample weight, (2) air temperature, (3) relative humidity, and (4) solar radiation intensity. weight measurements were obtained using a precision digital balance (accuracy: ± 0.1 g) with automated data logging capabilities. Solar radiation intensity was quantified using a calibrated SRI sensor (model: SENTEC RS485, Sichuan, China; measurement range: 0–2000 W/m², accuracy: ± 5%) positioned parallel to the collector surface. Simultaneously, ambient conditions were monitored using a digital humidity-temperature sensor (model: DHT-22, China; temperature range: −40 °C to 80 °C, accuracy: ±0.5 °C; humidity range: 0–100% RH, accuracy: ±2% RH) installed at four locations on the TSSC (Fig. 6). All sensors were connected to a centralized data acquisition system that recorded measurements at 60-minute intervals, ensuring synchronized parameter tracking throughout each drying trial.

Fig. 6.

Schematic illustration of the experimental setup connected with measuring instrumentation.

Performance analysis of the developed TSSD

Moisture content (MC)

The initial MC of tilapia strips were conducted at 70 °C in an electric oven until reaching constant weight⁵⁰. Then it was determined using Eq. (1)^51,52.

1

Energy analysis

Both TSSC and drying chamber were assessed utilizing fundamental thermodynamic concepts of mass and energy conservation in steady-flow systems⁵³. In accordance with the laws of mass conservation and energy, the air mass flowrate remains invariant throughout the TSSD, indicating that the inflow rate at the inlet exactly corresponds to the outflow rate at the exit.

2

3

4

Where:

5

6

By applying Eqs. 5 and 6 to TSSC, the following equations were obtained.

7

8

The input energy (), useful energy () and energy efficiency () of the was calculated according to Eqs. 9–11^54–56,

9

10

11

The drying efficiency of the developed () was established using Eq. (11)⁵⁶,

12

Exergy analysis ()

The analysis of the developed TSSD is based on the second law of thermodynamics, and it is calculated using Eq. 13.

13

By applying the Eq. (13) in the current study for the developed TSSD, it will be rewritten by neglecting the unnecessary parts related to the flow process. The momentum, gravitational, chemical and radiation energies. And Eq. (14) by applying the above assumptions⁵⁷.

14

where, is atmospheric AT.

balance for TSSC is given by Eq. (19)^56,58,59,

15

16

17

18

The efficiency of TSSC is obtained using Eqs^56,60.,

19

  analysis of the drying chamber (DCh)

balance for drying chamber is expressed as follows:

20

The , and exergy efficiency () of the DCh are calculated using Eqs. (21–23)^57,61,

21

22

where, and are inlet and outlet air temperatures from the drying chamber, respectively.

23

Sustainability indicators

During the current study, three sustainability indicators were estimated according to the following Equation from 24 to 26⁵⁷.

24

25

26

CFD simulation setup

In this study, CFD simulations were performed under transient conditions to better capture the dynamic nature of the drying process. This approach accounts for time-dependent variations in solar radiation, airflow distribution, and exhaust fan operation, all of which strongly affect drying kinetics. Transient modeling offers a more realistic representation of the unsteady heat and mass transfer processes in solar-assisted systems, where environmental factors fluctuate throughout the day. The CFD methodology generally involves three key stages: (i) Preprocessing – defining the computational domain geometry, generating the mesh, and assigning boundary conditions, physical models, material properties, and numerical parameters; (ii) Processing – solving the governing equations through iterative numerical computations; and (iii) Post-processing – interpreting and visualizing the outcomes using profiles, contour maps, streamlines, and surface plots of variables such as velocity, pressure, temperature, and concentration.

Governing equations

The governing equations for mass, momentum, and energy conservation, which are solved using CFD in this study, are shown in Eqs. 27–29.

27

28

29

The air flow in the TSSD was turbulent; therefore, the mean flow variables must be solved in conjunction with a turbulence model that describes the effect of turbulence on the mean flow. In this study, the standard k-ε turbulence model integrated in SolidWorks 2019 was used, because it provides convergence in a short time and accurate results. The standard k-ε model is a semiempirical model that contains and solves two equations, the first for the turbulent kinetic energy (k) and the second for the dissipation rate of this energy (ε), which are based on the Reynolds equation and Navier-Stokes. The developed equations are shown below:

Turbulent kinetic energy:

30

Turbulent dissipation rate:

31

32

33

34

In the turbulent viscosity, we have

35

36

37

38

39

where, is the tensor of the center of rotation velocity in a rotating zone with angular velocity . They have constants and that are described by Eqs. 40–45.

40

41

φ(42)

43

44

45

Boundary conditions

The values of the boundary conditions used in this study are the averages of the experimental measurements carried out with the experimental equipment during the current study. The numerical finite volume method, as used in (SolidWorks 2019), is used for solving the equations on a PC P 13th Gen Intel(R) Core (TM) i7-13620 H 2.40 GHz with 16 GB RAM. In this study, various boundary conditions are defined as follows:

• Inlet: inflow condition is given. Environmental pressure = 101,325 Pa, air temperature of 40.1 ᵒC. The software extrapolates the required information from the interior of the drying chamber to the outlet.

• Outlet: outlet three air speeds of 1.0, 1.5, and 2.0 m/s.

• Porous media: empirical parameters of pressure drop equation and leaves tray porosity are defined.

• Gravitational acceleration (m = s²) 9.81.

• Bed wall boundary condition: Bed wall boundary condition.

• Basic mesh dimensions: 12 × 24 × 48.

• Analysis mesh: total cell count: 43,645; fluid cells: 17,361; solid cells: 26,284; partial cells: 9815.

Uncertainty analysis

The measurement uncertainties for temperature, relative humidity, wind speed, and solar radiation were found to be 0.35%, 0.27%, 0.22%, and 0.15%, respectively. Taking all these variables into account, the overall uncertainty in evaluating the efficiency of the TSSD was estimated at around ± 2%.

46

Results and discussion

Moisture content

To ensure accuracy and reliability of the results, each experiment was conducted in triplicate, and the mean values were calculated and used for plotting Fig. 7. Figure 7 presents the influence of strip thickness (4, 8, and 12 mm) on the moisture content of STS during drying using the TSSD. The TSSD process was conducted under controlled drying temperatures ranging from 31.35 to 96.51 °C, ensuring optimal moisture removal while preserving product quality. The results demonstrate a clear relationship between strip thickness and drying time. Thinner strips (4 mm) reached the target moisture content of 15% (w.b.) in 16 h, while thicker strips required progressively longer drying periods—18 h for 8 mm and 20 h for 12 mm strips. This trend can be attributed to the reduced diffusion path length in thinner samples, which facilitates faster moisture migration from the interior to the surface. Additionally, the TSSD system’s ability to modulate temperature within the tested range contributed to efficient moisture evaporation without compromising structural integrity. The observed drying kinetics align with Fick’s second law of diffusion, where thinner geometries exhibit steeper moisture gradients, thereby accelerating drying rates^62–66. These findings highlight the critical role of strip thickness optimization in industrial drying processes for STS.

Fig. 7.

Moisture content of different thicknesses of STS dried using the developed TSSD.

Weather conditions and air temperatures

Figure 8 illustrates the variations in solar radiation intensity (SRI), ambient temperature (T₀), inlet and outlet air temperatures from the TSSC (T_{in, TSSC} and T_{out, TSSC}), and outlet air temperature from the DCh (T_{out, DCh}) over the observed period. The SRI exhibited significant fluctuations, with minimum and maximum values recorded at 241 W/m² and 916 W/m², respectively, reflecting varying cloud cover and atmospheric conditions. The ambient temperature (T₀) ranged between 23.5 °C and 30.7 °C, indicating moderate climatic conditions during the experiment. The inlet air temperature to the TSSC (T_{in, TSSC}) varied from 24 °C to 32 °C, closely tracking ambient temperatures, while the outlet air temperature (T_{out, TSSC}) demonstrated a substantial increase due to solar thermal energy absorption, reaching 31.5 °C to 96.51 °C. This highlights the TSSC’s effectiveness in heating the air. Furthermore, the outlet air temperature from the drying chamber (T_{out, DR}) ranged from 30 °C to 86 °C, suggesting effective heat retention and utilization within the drying system, though slightly lower than the TSSC’s peak output due to thermal losses or energy absorption by the drying material. These results underscore the system’s capability to harness solar energy efficiently for thermal applications.

Fig. 8.

Solar radiation intensity, ambient temperature and temperature inside the TSSD during field experiments.

CFD simulation

Using CFD to choose the appropriate fan velocity

In earlier studies on fish drying, maintaining an appropriate drying temperature below 100 °C was critical to preserving product quality, as excessive heat can degrade protein content, alter texture, and reduce nutritional value^67–69. Conversely, insufficient drying temperatures can lead to microbial growth and spoilage, compromising food safety^70–72. To ensure optimal drying conditions, controlling the airflow velocity of the exhaust fan is essential, as it directly influences heat distribution and moisture removal inside the drying chamber. However, experimental trial-and-error methods for determining the correct fan speed can be time-consuming and costly^18,23,73,74.

CFD offers a powerful alternative by simulating airflow patterns, temperature distribution, and humidity levels within the drying chamber before physical implementation^75,76. By modeling different fan velocities, CFD helps identify the optimal exhaust rate that maintains temperatures below the 100 °C threshold while ensuring efficient moisture extraction. This approach not only enhances drying efficiency but also minimizes energy waste and prevents product damage. Earlier studies that relied solely on empirical measurements could benefit significantly from CFD-based optimization, as it provides precise, data-driven insights into thermal and aerodynamic behavior, leading to improved drying performance and consistent product quality^77,78.

Figure 9 presents the CFD-generated contours of air temperature distribution inside the DCh at varying exhaust fan velocities (1.0, 1.5, and 2.0 m/s). The simulations, conducted using SolidWorks 2019, analyzed thermal behavior under a solar irradiance of 872 W/m², representing peak drying conditions. The results revealed a clear inverse relationship between fan velocity and maximum air temperature: at 1.0 m/s, localized overheating occurred, reaching 188.67 °C, far exceeding the safe drying threshold for fish (below 100 °C). Increasing the velocity to 1.5 m/s reduced peak temperatures to 124.4 °C, while the 2.0 m/s setting achieved the most uniform and controlled thermal environment, with a maximum of 96.51 °C—within the optimal range for preserving product quality.

Fig. 9.

The contours of air temperatures inside the DCh at different air velocities of the exhaust fan.

The temperature contours further demonstrated that higher fan velocities (e.g., 2.0 m/s) enhanced airflow circulation, minimizing hot spots and ensuring consistent heat distribution across the DR. This velocity not only prevented thermal degradation of fish strips but also aligned with energy efficiency goals, as excessive speeds (beyond 2.0 m/s) could unnecessarily increase power consumption without significant thermal benefits. The CFD analysis thus identified 2.0 m/s as the optimal exhaust fan velocity for balancing temperature control, drying uniformity, and energy use during solar noon operations.

Using CFD to estimate the air velocity patterns and temperature distribution inside the DCh over drying hours

1. Using CFD to estimate the air velocity patterns and temperature distribution inside the DCh over drying hours.

Figures 10 and 11 illustrate the outcomes of a CFD simulation applied to the TSSD, designed to analyze temperature distribution and airflow characteristics within the drying chamber over a typical operational day. The model incorporates transient boundary conditions, updated hourly based on field-recorded ambient temperature and solar radiation data, to replicate real-world environmental fluctuations. Simulations were conducted at ten discrete time points, spanning from 8:00 a.m. to 5:00 p.m. (Table 1), to capture the chamber’s dynamic thermal behavior. The results demonstrate a clear diurnal pattern in thermal and fluid performance, with air temperatures steadily increasing through the morning and peaking between 11:00 a.m. and 3:00 p.m., where the highest internal temperatures (84.17–95.15 °C) and the most vigorous convective flow trajectories are observed. During this period, pronounced natural convection loops form, characterized by upward and recirculating flow paths that enhance heat and mass transfer—critical for efficient moisture evaporation. In contrast, weaker airflow and lower temperatures dominate during early and late hours (8:00–10:00 a.m. and 4:00–5:00 p.m.), corresponding with reduced SRI. These findings highlight the importance of synchronizing drying operations with peak solar availability to maximize thermal efficiency and overall drying performance.

Fig. 10.

The contours of air temperatures inside the DCh over drying hours.

Fig. 11.

The contours of air velocity inside the DCh over drying hours.

Table 1.

The observed temperatures and air velocities inside the DCh based on the obtained results of simulation using the CFD throughout the first day of the drying experiments from 8 a.m. To 5 p.m.

Drying time, h	Time of day	SRI, W/m2	Inlet temperature to the TSSC, ᵒC	Air characteristics at outlet hole of the TSSC
				Absolute total enthalpy rate, W	Mass flow rate, kg/s	Volume flow rate, m3/s	Average Temperature, ᵒC
1	8 a.m.	277	24	5415.2	0.0175	0.0150	31.35
2	9 a.m.	489	26	5414.42	0.0169	0.0151	42.14
3	10 a.m.	674	27	5412.91	0.0153	0.0151	78.98
4	11 a.m.	814	28	5413.25	0.0144	0.0151	95.15
5	12 a.m.	896	30	5412.53	0.0152	0.0151	84.17
6	1 p.m.	916	31	5412.42	0.0150	0.0151	86.7
7	2 p.m.	872	30	5412.13	0.0144	0.0151	96.51
8	3 p.m.	767	29	5412.25	0.0149	0.0151	84.46
9	4 p.m.	572	28	5413.76	0.0167	0.0151	44.5
10	5 p.m.	320	27	5414.43	0.0171	0.0151	36.0

Energy analysis

Figure 12 presents the hourly energy analysis of the TSSC, detailing the , , and . These parameters were calculated based on SRI and the temperature differential between the inlet and outlet air streams of the TSSC. The , representing the total SRI absorbed by the TSSC, is influenced by solar irradiance, collector surface area, and orientation. During the experimental study, the input energy fluctuated between 174.89 W and 1752.7 W, reflecting variations in solar intensity throughout the day. The , derived from the developed TSSD using Eq. (10), exhibited a range of 78.44 W to 1752.7 W across different date varieties, as illustrated in Fig. 12. The energy efficiency of the TSSC, computed via Eq. (11), followed a distinct diurnal trend (Fig. 12), peaking at 57.21% around noon due to maximal solar irradiance and gradually declining to 40.79% by late afternoon (5 p.m.). This trend correlates directly with the useful energy output, which is highest during peak solar hours. Notably, the TSSC’s design significantly improved morning and afternoon performance compared to conventional fixed flat-plate collectors, mitigating typical inefficiencies such as thermal losses and suboptimal angle-related radiation absorption. The maximum efficiency at midday (12 p.m.) underscores the system’s responsiveness to solar intensity, demonstrating its superior thermal conversion capability. Table 2 presents a comparative analysis of the achieved in this study alongside results from previous research.

Fig. 12.

Energy analysis of the TSSC during drying STS.

Table 2.

Previous studies examined the of different types of SC.

Ref.	Type	, %
79	Natural and forced SC	62%
80	Multi-pass SC	52.1%
81	SC without phase change material	52.1%
82	SC with V-corrugated absorption plates	31.50%
83	Top and bottom flow SC	50.0%
84	Double-pass SC	1.15% to 26.46%
56	Tunnel SD	27.45% to 42.50%
85	Flat plate SC	45.32%
Current study	The developed TSSC	40.79–57.21%

Figure 13 presents the of the developed TSSD, which quantifies its effectiveness in transforming incident solar radiation into usable thermal energy for dehydrating various STS. The efficiency was determined through Eq. (12) by computing the ratio between the thermal energy actually utilized for moisture extraction from the STS and the total solar energy input provided by the TSSC. Mirroring the performance trend of , the drying efficiency peaked during midday hours (reaching maximum values between 0.88 and 8.51% depending on sample thickness) when solar radiation intensity (SRI) was at its highest. The system demonstrated notable sensitivity to product characteristics, with efficiency variations directly correlating with STS thickness profiles, as evidenced by the distinct efficiency ranges of 1.46–8.19%, 0.88–8.51%, and 2.02–8.46% recorded for sample thickneses of 4, 8, and 12 mm, respectively. The fundamentally represents the system’s moisture removal capacity per unit of available solar energy, which becomes particularly challenged during prolonged drying operations extending into low-irradiance periods. Three synergistic mechanisms drive the observed afternoon efficiency decline: (1) progressive thermal energy dissipation through combined conduction, convection and radiation pathways, (2) diminished drying kinetics resulting from both reduced insolation and weakening moisture potential gradients within the product matrix, and (3) inherent thermodynamic limitations during terminal drying phases where removing bound moisture requires disproportionately greater energy inputs. In practical operation, these factors collectively caused a marked reduction in TSSD performance during post-meridian hours, especially when processing dates nearing their target moisture content - a critical operational phase where precision moisture control is paramount yet most energetically demanding. This efficiency behavior highlights a fundamental optimization challenge in solar-assisted dehydration systems: reconciling the intermittent nature of solar energy availability with the continuous, often nonlinear thermodynamic demands of agricultural product drying, particularly for low-moisture commodities requiring stringent final moisture specifications.

Fig. 13.

Efficiency of the developed TSSD during drying STS.

Exergy analysis

Figure 14 presents the temporal variation of exergy parameters for the TSSC, including , , , and , calculated using Eqs. (15–19). These parameters exhibited a strong dependence on SRI, following a diurnal pattern that peaked around midday before declining in the afternoon. The , determined from SRI via Eq. (19) and referenced to ambient air temperature, ranged from 138.91 W to 1391.6 W during testing, with maximum values observed between 11 a.m. and 2 p.m. (Fig. 14). Correspondingly, the for the developed TSSD varied from 10.11 W to 426.52 W, while fluctuated between 128.8 W and 995.95 W—peaking during the same high-irradiance period due to increased SRI and optimal collector exposure (Fig. 14). On average, the system demonstrated 767.6 W (), 204.3 W (), and 563.2 W (). Exergy efficiency (), computed via Eq. (23), ranged from 7.28 to 32.83%, with an average of 22%. The is primarily governed by outlet air temperature, as the remains consistent across configurations while the reflects thermal performance. The midday of the TSSC surge correlates with peak SRI, underscoring the TSSD’s sensitivity to solar availability. For broader context, Table 3 compares these results with prior studies, highlighting the TSSC’s advancements in exergy utilization.

Fig. 14.

Exergy analysis of the TSSC during drying STS.

Table 3.

Previous studies examined the of different types of SDs.

Ref.	Temperature	Airflow	Type
86	32–63 ᵒC	Not available	Forced convection indirect SD	2.44%
	34–69 ᵒC	Not available	Natural convection indirect SD	2.03%
87	60 ᵒC	0.010–0.016 kg/s	Active greenhouse SD	3.45%
56	54.2 ᵒC	Not available	Tunnel SD	41.42%
88	36–62 ᵒC	0.018–0.062 kg/s	Mixed-mode SD with dual double-pass SC	41.4%
25	29–122.78.78 ᵒC	Not available	Hybrid mixed mode greenhouse SD	28.96%
56	54.2 ᵒC	Not available	Tunnel SD	69%
89	26.3–60.3 ᵒC	0.031–0.0381.031.0381 m3/s	Carbon nanotubes-based SD	11.9%
90	28–82 ᵒC	Not available	Indirect type natural convection SD	45.23%
Current study	31.35–96.51 ᵒC	2 m/s	The developed TSSC	7.28–32.83%

The , and of the drying chamber (DCh) were calculated using Eqs. (20–23) and their trends are displayed in Fig. 15. The exergy inflow into the drying chamber () is primarily influenced by the temperature of the air exiting the TSSC and entering the DCh. The analysis revealed that , and varied between 10.17 and 427.41 W and 6.06–340.31 W, respectively, while ranged from 1.3 to 146.34 W. As shown in Fig. 15, these exergy values peaked around midday due to higher inlet air temperatures at that time. Furthermore, the exergy rates were affected by both the air mass flow rate and the temperature difference (ΔT) between the inlet and outlet air temperature from the DCh. In the developed TTSD, the DCh inlet air temperature remained moderate, fluctuating between 31.35 °C and 96.51 °C and the inlet air temperature fluctuated between 30 °C and 86 °C. Since the inlet air temperature significantly impacts , and , these values were comparatively higher in the TTSD setup. Figure 15 also illustrates the hourly variation in the exergy efficiency , determined using Eq. (23). The results indicated that the outlet air temperature closely approached the inlet temperature, leading to an exergy efficiency range of 36.37–87.19%. This efficiency improved over time because the ratio of to increased as the temperature drop within the DCh diminished later in the drying process. This reduction in temperature decline occurred because moisture removal from the products decreased toward the end of drying. For comparison, Table 4 summarizes findings from previous studies on the of various types of SDs.

Fig. 15.

Exergy analysis of the drying chamber during drying STS.

Table 4.

Previous studies examined the of different types of SDs.

Ref.	Temperature	Airflow	Type
90	62 ᵒC	0.062 kg/s	Triple-pass SD	2.8–87.02.8.02%
91	60–80 ᵒC	1.0–1.5 m/s	Two-tray hot air cyclone dryer	18–100%
92	55–70 ᵒC	1.5 m/s	Laboratory tray dryer	67.27–97.29%
93	60–85 ᵒC	0.5–1.5 m/s	Experimental tray dryer	24.81–100%
94	60–80 ᵒC	1.5 m/s	Two-tray hot air cyclone type dryer	32–100%
95	50–80 ᵒC	1.0–2.0 m/s	Rotating tray dryer equipped with heat exchanger	23–96.1.1
96	40–60 ᵒC	1.23 m/s	Forced convection SD	15.65–100%
97	60–80 ᵒC	1.0–1.5 m/s	Two-tray hot air cyclone type dryer	19.4–100%
98	71–93 ᵒC	0.82–1.18 m/s	Thin layer air dryer	80–97%
99	40–50 ᵒC	0.01–0.05 kg/s	Ground source heat	76.03–97.24%
Current study	31.35–96.51 ᵒC	2 m/s	The developed TSSD	36.73–87.19%

Sustainable indicators

Exergy-based sustainability indicators—including IP, WER, and SI—play a vital role in assessing the performance of TSSD and optimizing the DCh design. These metrics help engineers pinpoint inefficiencies, minimize energy waste, and improve the overall sustainability of the drying process, leading to more environmentally friendly and energy-efficient operations. IP measures the potential for enhancing the exergy efficiency of the DCh while reducing waste exergy, acting as a benchmark to evaluate how far the TSSD deviates from ideal thermodynamic conditions. By identifying exergy losses , IP guides improvements that enhance sustainability, resource utilization, and exergy efficiency. Calculated using exergy dissipation (Eq. 24), the IP values in this study ranged from 1.19 W to 7.22 W (Fig. 16), suggesting relatively low exergy losses—a sign of an efficiently designed DCh. Meanwhile, WER (computed via Eq. 25) indicates the proportion of wasted exergy in the system, with values between 0.67 and 0.93, meaning a significant portion of input exergy is lost rather than utilized effectively. On the other hand, SI (derived from Eq. 26) reflects the TSSD’s overall sustainability, where higher values denote better performance. The observed SI range of 1.08 to 1.49 suggests moderate sustainability, indicating room for further optimization to reduce waste and improve efficiency. Unfortunately, there is no comparison level for these values ​​in all the previously published research, but only numerical results ​​in all the previously published research, but only numerical results, as shown in Table 5.

Fig. 16.

Sustainability indicators of the developed TSSD during drying STS.

Table 5.

Comparison between the observed sustainable indicators and previous studies.

Ref.	Type	Crop	Sustainable indicators
			IP	WER	SI
57	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
102	Forced convection SD	Pepper	0 to 17 W	0.38 to 0.55	0.393 to 6.156
103	Hybrid solar-biomass dryer	--	0.036 to 20.6 W	0.38 to 0.55	2.3 to 6.11
88	Forced convection mixed mode SD	Cluster figs	--	--	1.26 to 1.71
Current study	Developed TSSD		1.19 to 7.22 W	0.67 to 0.93	1.08 to 1.49

Practical Meaning of IP, WER, and SI Values:

• The low IP range (1.19–7.22 W) implies that the TSSD already operates efficiently, with minimal exergy losses. A higher IP would indicate greater inefficiency, requiring design modifications.

• A WER between 0.67 and 0.93 means 67–93% of input exergy is wasted, highlighting significant energy losses. Lower WER values would indicate better exergy utilization.

• An SI range of 1.08–1.49 suggests the system is somewhat sustainable but could be improved. Higher SI values (closer to or above 2) would indicate a more sustainable and efficient drying process.

These findings suggest that while the current TSSD design is relatively efficient, further refinements could reduce waste exergy (lower WER) and enhance sustainability (higher SI), making the system even more energy-efficient and eco-friendly.

Although sustainability indicators were effectively calculated in this study to evaluate the thermodynamic performance of the TSSD, their practical relevance in real-world applications requires further clarification. These indicators are useful in detecting inefficiencies and directing improvements. For example, a high IP highlights significant opportunities to enhance exergy efficiency, enabling engineers to target specific components—such as the collector or airflow channels—for optimization. The WER measures the fraction of energy lost irreversibly during drying, and minimizing this parameter is vital for maximizing energy use and lowering costs, particularly in off-grid or resource-limited agricultural contexts. The SI, which reflects the balance between effective energy use and environmental impact, provides a broader measure of system viability. Higher SI values not only indicate superior exergy efficiency but also align with global sustainability objectives such as reducing emissions and advancing renewable energy adoption. Practically, these indicators can serve as benchmarks for comparing and scaling drying technologies across various crops and regions. Systems with consistently low WER and high SI values are especially well-suited for deployment in remote farming areas where energy conservation and environmental protection are priorities. Incorporating these indicators into system design, operational strategies, and policy frameworks supports more informed decisions for advancing sustainable agricultural processing^88,100,101.

Conclusion

This study presents the development and comprehensive evaluation of an innovative TSSD, designed to enhance solar energy utilization. Based on the obtained data, the following conclusions were drawn:

• The STS samples dried using the TSSD reached the equilibrium MC after 16, 18, and 20 h for layer thicknesses of 4, 8 and 12 mm, respectively.

• The energy efficiency of the TSSC ranged between 40.79 and 57.21%, where the efficiency was increased during morning and afternoon compared to traditional flat plate solar collector.

• The highest energy efficiency of the TSSD was 8.19%, 8.51%, and 8.46% at strip thicknesses of 4, 8, and 12 mm, respectively.

• The exergy efficiency of TSSC and Drying chamber were in a range from 7.28 to 32.83 W, and 36.37 to 87.19, respectively.

• The IP was in the range of 1.19–7.22 W while the SI and WER varied from 1.08 to 1.49 and 0.67 to 0.93, respectively.

The study was limited to specific climatic conditions, tilapia fish strips, and three slice thicknesses, reducing generalizability. CFD simulations assumed steady-state and ideal conditions, which may differ from real-world variability. Long-term durability, seasonal performance, broader product applicability, and extended economic evaluations were not addressed, leaving opportunities for further validation.

Future studies should assess performance across seasons and climates, and for various products beyond tilapia. Integration with auxiliary heat sources could ensure continuous operation. Advanced CFD modeling should simulate real-time weather impacts. Life-cycle and techno-economic analyses are needed to evaluate environmental benefits, long-term costs, and commercial viability of the system.

List of symbols

Moisture content

Sample weight

Mass flow rate

Energy flow rate

Enthalpy

Air velocity

 Height

g

Gravity acceleration

Work done

Heat transfer

Useful energy

Input energy

Energy loss

Solar radiation intensity

Surface area of the solar collector

Specific heat of air

Air temperature

Energy efficiency

Quantity of removed water from date sample

Latent heat of vaporization of water

Drying time

Exergy efficiency

Internal energy

Entropy

Exergy

Transmissivity of glass

Chemical energy

Atmospheric temperature

Subscription

i

Inlet

o

Outlet

TSSC

Three sides solar collector

Dryer

The solar dryer

DR

Drying room

Abbreviation

TSSD

Triple-sided solar dryer

CFD

Computational fluid dynamics

EMC

Equilibrium moisture content

DR

Drying room

TSSC

Three sides solar collector

DCh

Drying chamber

IP

Improvement potential

WER

Waste exergy ratio

SI

Sustainability index

SRI

Solar radiation intensity

Author contributions

Conceptualization, T.H.M.G., L.S.N., O.S.Y., and A.E.E., methodology, A.A.T., and A.E.E., software, A.E.E., validation, A.A.T., A.Z.E., A.A.T.O., G.A., and A.E.E., formal analysis, A.Z.E., and A.E.E., investigation, M.A., A.F.A., K.M.E., and A.E.E., resources, M.A., A.F.A., K.M.E., A.A.T.O., G.A., and A.E.E., data curation, A.E.E., Writing - original draft, A.E.E., writing—review and editing, T.H.M.G., L.S.N., O.S.Y., A.Z.E., G.A., and A.E.E., visualization, T.H.M.G., L.S.N., O.S.Y., G.A., and A.E.E., supervision, T.H.M.G., L.S.N., O.S.Y., G.A., and A.E.E., project administration, T.H.M.G., L.S.N., O.S.Y., G.A., and A.E.E., funding acquisition, G.A., All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Deanship of Graduate Studies and Scientific Research, Taif University.

Data availability

All data are presented within the article.

Declarations

Competing interests

The authors declare no competing interests.