1. Introduction

Significant portion of agricultural products, including grains, vegetables, fruits, marine, and herbal items, face losses during postharvest stages. Many of these goods deteriorate due to inadequate drying and storage practices. Reports indicate substantial losses, with fruits experiencing 50%–70% spoilage and grains suffering 10%–30% losses, largely because of inefficient drying and preservation methods [1]. Solar drying stands as one of the most traditional yet effective methods for minimizing postharvest losses. Solar-powered drying systems provide a cost-efficient approach to food preservation [2]. With fossil fuels becoming scarcer and more expensive, along with uncertainty about their future costs and availability, the use of solar energy for drying agricultural products is expected to rise and could become a highly economical solution in the future [3]. Solar dryers are among the most effective systems for dehydrating fresh crops. These thermal systems are highly favored because they utilize a free, safe, and sustainable energy source [4]. Numerous prominent researchers have emphasized the benefits of solar drying over other drying methods, particularly in terms of cost-effectiveness and environmental sustainability, positioning it as a viable solution to address food wastage. However, as noted in Kumar and Kumar [5], solar dryers have some draw backs such as inadequate design and limited performance.

Another challenge associated with solar drying systems is uneven airflow within the drying chamber, which results in nonuniform drying, diminished product quality. To mitigate this issue, various authors have proposed and implemented methods to enhance airflow uniformity. Additionally, the design and components of solar dryers have been optimized using diverse approaches, as documented in various studies. Optimization techniques can be employed to modify the design of the solar dryers, improve airflow distribution, enhance thermal efficiency, and so on.

Several research studies have been conducted on optimizing indirect solar drying systems. Various types of optimizations have been outlined in the literature.

Advanced techniques, such as artificial neural networks (ANNs), genetic algorithms (GAs), response surface methodology (RSM), particle swarm optimization (PSO), and the Taguchi method, have been utilized to model solar drying processes and equipment, enabling precise prediction of optimal configurations [6]. Taguchi method is a highly regarded approach for design of experiment (DOE) and optimization, extensively applied across diverse industries and engineering disciplines. It systematically analyzes various design factors and parameters, identifying the optimal combination to achieve desired performance targets. By selecting the most effective control factors, the method minimizes the number of experimental trials while maximizing efficiency [7]. Taguchi method is an effective approach in experimental design that reduces the number of experiments required based on the given control factors. Each experiment consists of a specific level for each control factor, and the combinations of these levels are determined using predefined orthogonal arrays (OAs). Afterward, each experiment is repeated several times, with the result from each replication referred to as the process response. The responses from all experiments are then converted into a signal-to-noise (S/N) ratio for comparison. The experiment with the highest S/N ratio identifies the optimal levels of the control factors [8]. Taguchi method employs OAs from experimental design theory to efficiently analyze a large number of variables with fewer experiments [9].

The Taguchi method involves the creation of OAs to systematically evaluate a set of parameters, including certain input variables. The experimental results are then converted into S/N ratios to assess performance. The S/N ratio is categorized into three types: larger-the-better, smaller-the-better, and nominal-the-best [10]. The higher-the-better approach is used when the objective is to maximize a parameter, while the lower-the-better approach is applied when minimizing a parameter is desired. In contrast, the nominal-the-best approach is suitable for scenarios where the objective must be maintained at a specific target value to achieve optimal performance [11].

Various studies have focused on optimizing different aspects of solar energy systems, utilizing the Taguchi method to optimize solar drying systems and their parameters. For example, Christopher [12] used the method to optimize flow parameters of solar air heaters (SAHs); Badaoui et al. [13] employed the Taguchi method to select the optimal configuration for a SAH; Midili and Kucuk [14] focused on optimizing the design of an impinging jet solar thermal collector; Aghaie et al. [15] applied the method to optimize the thermo-hydraulic behavior of air flow in a SAH, improving its efficiency; Kolioak et al. [16] used it to determine the optimal heat energy characteristics of a solar box collector; Gupta and Rana [17] employed the Taguchi method to enhance the performance parameters of solar air systems; Jibhakate et al. [18] used it to optimize the performance of solar tunnels; Hussein et al. [19] applied it to refine the processing parameters for drying tomatoes; Padmanaban et al. [20] utilized this approach to identify the best drying parameters for solar drying of copra; Osodo et al. [21] employed it to refine the basic parameters of a forced convection grain dryer; and Al-Hajji et al. [22] employed it to optimize the performance of SAHs. These studies highlight the versatility and effectiveness of the Taguchi method in improving the design and performance of various solar energy systems. The details of these studies are summarized in Table 1.

A critical challenge in multitray solar drying systems as noted in Abdulmalek et al. [23] is the nonuniform distribution of airflow within the drying chamber, which leads to uneven moisture removal, reduced product quality, and nutritional degradation. Researchers have attempted various methods to improve flow uniformity in the drying system. For example, Al-Kayiem and Gitan [24] made an inclination of the trays into 0°, 10°, 20°, 30°, and 35°. Christopher [12] used vertical V-shaped air distribution channels to achieve uniform airflow and temperature distribution across the drying chamber. Abdulmalek et al. [25] suggested that solar drying uniformity could be enhanced through dehumidification systems or structural design optimizations in the dryer.

The novelty of this study lies in its pioneering use of the Taguchi method to evaluate and compare different airflow uniformity enhancement mechanisms (e.g., triangular baffles, rectangular deflectors, and swirlers) in solar drying applications. While the Taguchi approach has been widely applied to optimize thermal performance parameters such as heat transfer efficiency and pressure drop in solar dryers, its potential for improving airflow uniformity a critical factor for drying consistency and product quality remains unexplored. Additionally, previous studies have not investigated the integration of swirl-inducing structures or strategically placed baffles to enhance uniform air distribution within the drying chamber. This study addresses these gaps by employing Taguchi analysis, which was employed to identify the most effective airflow modification strategy while considering both energy efficiency and drying performance. The objective of the study is to investigate the performance of a solar dryer with different airflow enhancement methods and to identify the optimal configuration using Taguchi analysis for achieving uniform drying efficiency. The study aims to evaluate the effectiveness of the solar dryer in terms of different performance indicators like thermal analysis, drying rate, and so on. The findings will aid in the development of high-performance solar dryers and provide insights for future research on advanced airflow control strategies, including the optimal placement and integration of multiple uniformity-enhancing features.

2. Materials and Methods

2.1. Study Area

The experiment was conducted in the Mechanical Engineering Laboratory of the Hungarian University of Agriculture and Life Sciences (MATE), formerly Szent István University, located in Gödöllő, Hungary (47°35′39″N, 19°34′58″E). The experiment was carried out between July and September 2024.

Four kilograms of apples were purchased from Coop in Gödöllő, Hungary (Egyetem tér 17, 2100) during the experiment. The apples were selected based on uniform size, and their diameters were measured to ensure consistency. Using a manual knife, the apples were sliced into pieces of standardized thickness. These sliced samples were then placed in the drying chamber for processing. To enhance airflow efficiency, three different flow-enhancer methods (triangular baffles, rectangular baffles, and a spiral design) were integrated into the plenum of the drying chamber. A Taguchi analysis was subsequently conducted to determine the optimal enhancement method and identify the ideal operational parameters. A summary of the experimental procedure is illustrated in Figure 1.

2.2. Basic Material and Instruments Used

In the present study, an indirect type, forced convection solar dryer consisting of a flat plate SAH coupled to a drying chamber is constructed and investigated experimentally for drying of golden apple slices. The detail of the drying system is shown in Table 2.

Table 2. Specifications of the solar drying system components.

Component	Description
Solar air heater	Dimensions: 1.25 m × 0.5 m Single-glazed with diffuser inlet Circular outlet (0.1 m diameter) Plexiglass cover with 0.004 m thickness and 0.16 W/m·K thermal conductivity Copper absorber plate with 0.0012 m thickness and thermal conductivity 385 W/m·K 0.05 m gap between copper absorber and the glass cover Installed at a 45° inclination
Connector pipe	Length: 1.45 m Integrated with a venturi meter (measures flow rate and pressure drop)
Drying chamber	Dimensions: 1 m (height) × 0.5 m (width) × 0.5 m (length) Material: 5-cm-thick extruded polystyrene (XPS) with thermal conductivity of 0.033 W/(m·K)
Trays	Materia: fiberglass mesh trays (48 cm × 50 cm) Trays spaced 0.1 m apart for air circulation Number of trays: 4
Auxiliary equipment	Fans: 21–33 W and airflow rate 145 m3/h −187 m3/h flow rate Tray supporters Junction box used to the electric systema and the dimmers

The fabricated dryers are shown in Figure 2. Two similar-sized and similarly shaped dryers were fabricated, with one serving as the benchmark.

The fully integrated solar dryer, equipped with a SAH, fans, and a flow measuring device, is shown in Figure 3.

2.3. Data Collection

The sensors and instruments used for collecting the experimental data are detailed in Table 3 and Figure 4.

Table 3. List of sensors and instruments used during the experiment.

Instrument	Model	Accuracy	Operational range	Purpose
Humidity sensor	HIH-40000-004	±3.5%	0%–100% RH	To measure the humidity level
Pressure sensor	SDP816-500Pa	±5 millibar	−500–500 Pa	To measure the pressure, drop, and flow rates with the help of orifice meter
RTD sensor	PT-100	±0.1°C at 0°C	−75°C–250°C	To measure temperature
Temperature recorder	BTM-4208SD	±0.5°C	−200°C to +400°C	Served as data logger and connected to T-type of thermocouples
Digital anemometer	MS6252A	±0.01 m/s	0.40−30.00 m/s	To measure the ambient wind speed and used to calibrate the flow rates
Digital weighing machine	Lutron GM-500	0.1% + 2	0–500 g	To measure the weight of samples
Pyranometer	Kipp and Zonen CM11	±0.1 W/m2	1–4000 W/m2	To measure the solar intensity

The cDAQ chassis was served as the central data acquisition system, linking multiple sensors via extension cables to the ADAM module.

The pressure drops (D_P) (differential pressure, Pa) of the Sensirion SDP806/SDP816-500Pa pressure sensor with square root configuration can be calculated using Equation (1):

$$ (1)

where sign () = indication flow direction (+ or −).

For the humidity sensors, the following formulas were employed based on the manufacturer’s guidelines:

$$ (2)

Thus,

$$ (3)

The temperature compensation is as follows:

$$ (4)

where T is °C.

2.7. Material Used to Uniform the Flow Behavior

Rectangular baffles, triangular baffles, and a swirler were used to improve the airflow distribution. Additionally, Taguchi analysis was used to identify the optimal configuration, ensuring the best performance in terms of uniform drying efficiency. The triangular baffles measuring 0.18 m by 0.18 m with a base of 0.06 m and rectangular baffles measuring 0.12 m in height by 0.23 m in length and swirler were integrated into the plenum of the drying chamber. Curved metal shaped was also incorporated into the side walls of the drying chamber. The enhancement materials integrated within the drying chamber are illustrated in Figure 5.

To assess uniformity, statistical methods such as mean, variance, standard deviation (σ), and coefficient of variance (C_V) were applied. The expressions for mean, variance, and standard deviation can be formulated as follows [34]:

$$ (16)

$$ (17)

$$ (18)

where x_i is the given data, $$ is the mean value of the given data, and n is the number of data points.

To measure the extent of variation the coefficient of variation is a widely accepted metric to measure the extent variation, defined as the ratio of the standard deviation (σ) to the mean ($$) [35]. The smaller the C_v, the greater the stability [36]:

$$ (19)

3. Result and Discussion

3.1. SR and Ambient Temperature the Experiment

The solar intensity and the ambient temperature are shown in Figure 6. The SR typically increases during the morning, peaks around midday (12:00–13:00), and gradually decreases in the afternoon. The average SR values were recorded as 830 W/m² on day 1 (with triangular baffles), 810 W/m² on day 2 (with the rectangular baffles integrated), and 850 W/m² on day 3 (with swirler baffles). The corresponding average ambient temperatures (Figure 7) for these days were 30°C, 27°C, and 33°C, respectively.

3.2. Performance Analysis of the Solar Drying System

The efficiency of the SAH and corresponding dryer is shown in Figure 8. The SAH’s efficiency starts low in the morning, rises rapidly, and stabilizes by late morning. It reaches peak performance around midday when sunlight is strongest and then gradually declines through the afternoon. The efficiency of dryer (Figure 9) also follows the same pattern with day 3 demonstrating the highest efficiency compared to days 1 and 2. In the afternoon, efficiency gradually declined as SR diminished, highlighting the system’s strong dependence on sunlight availability.

3.2.1. Comparison of the Results

The comparison of dryer efficiency and SAH efficiency of the current study with study is presented in Figure 10. In the current investigation, the dryer efficiencies were recorded as 18.25%, 17.19%, and 19.10% for days 1, 2, and 3, respectively, while the SAH efficiencies were 52.67%, 51.11%, and 54.41% for the corresponding days.

3.3. Mass Reduction of the Three Enhancement Methods

3.3.1. Integrating Triangular Baffles

The weight loss of the samples with and without triangular baffles is shown in Figure 11. The dryer without baffles showed greater moisture variation between trays compared to the one with triangular baffles. The triangular baffles likely improved airflow consistency, promoting more uniform drying. During weight loss, the average coefficient of variation (C_v) was 10.26% with triangular baffles and 11.16% without them.

3.3.2. Adding Rectangular Baffles

The weight loss of sample in the drying chamber with rectangular baffles and without rectangular baffle is shown in Figure 12. The implementation of rectangular baffles was observed to modulate the drying kinetics, producing a more uniform weight reduction gradient across all trays compared to nonbaffled configurations. Integrating rectangular baffles into drying systems significantly improves process uniformity, as evidenced by a 14.5% reduction in the coefficient of variation (C_v) from 12.55% (unbaffled) to 10.96% (baffled). This enhancement demonstrates that baffle design can optimize drying homogeneity, offering potential benefits for industrial efficiency, product quality, and energy savings.

3.3.3. Adding Swirler

Figure 13 shows drying characteristics between systems employing swirlers and with no swirler. With a swirler, drying is more uniform, with controlled moisture removal and tighter mass differentials. It also creates a unique reverse drying pattern, where upper trays dry slower than some middle trays. Without a swirler, drying is faster in lower trays but less consistent, with greater variability and more aggressive moisture loss early on. The swirler enhances airflow dynamics by creating controlled turbulence, improving heat distribution, reducing uneven drying, and preventing case hardening, leading to better intertray consistency at a steady drying rate. The system incorporating the swirler demonstrated significantly improved uniformity, evidenced by a 39.3% reduction in the average coefficient of variation (C_v = 8.86%) compared to the nonswirler configuration (C_v = 14.95%).

3.4. Tempreture Distribution of the Three Enhancement Methods

3.4.1. Adding the Triangular Baffles

The temperature distribution with triangular baffles is significantly more uniform compared to without baffles as depicted in Figure 14a,b,). In the case of triangular baffles, the temperature differences range from 1.0 to 3.3°C, indicating a relatively uniform distribution across the trays. On the other hand, without the triangular baffles, the temperature differences are much larger, ranging from 3.8 to 7.6°C, which suggests a less uniform distribution. This demonstrates that the presence of triangular baffles helps to improve heat distribution by reducing temperature gradients, resulting in a more uniform temperature profile across the trays.

3.4.2. Adding the Rectangular Baffles

The temperature distribution with rectangular baffles shows generally uniform results as illustrated in Figure 15a,b, with temperature differences ranging from 1.2 to 6.8°C. The largest variation occurs at the beginning of the day, but this difference progressively decreases as the day continues. In contrast, without rectangular baffles, the temperature differences are larger, ranging from 4.2 to 8.7°C, indicating a less uniform distribution. Overall, the temperature distribution with rectangular baffles is more uniform compared to without them.

3.4.3. Integrating Swirler

The temperature distribution with the swirler shows a generally uniform pattern, with temperature differences ranging from 2.0 to 4.8°C. The largest variation occurs at 12:00, but overall, the temperature differences are smaller compared to those observed without the swirler as shown in Figure 16a,b. In contrast, without the swirler, the temperature differences are larger, ranging from 5.0 to 7.3°C, indicating a less uniform distribution. In general, the temperature distribution with the swirler is more uniform than without it, in which the swirler enhances heat distribution, reduces temperature gradients, and promotes better thermal uniformity across the trays.

3.5. Taguchi Analysis

The calculated results for the coefficient of variation in temperature distribution, weight loss reduction, and the measured pressure drop were used as response variables. The coefficient of variation for mass reduction across the trays or the drying chamber as a whole, along with the pressure drop in dryers utilizing flow-enhancing designs such as triangular baffles, rectangular baffles, or swirl-type designs, is presented in Table 5. The coefficient of variation for temperature distribution is not displayed here.

Table 5. Results of the experimental design conducted using the Taguchi L9 orthogonal array (OA) and the response parameters.

EX no.	Parameters/levels				CV of the mass as response variables			Pressure drops as response variables
	SR	Tam	Tin	Type-C	RB	TB	SW	DPRB	DPTB	DPSW
1	810	33	40	TB	0	0.00	0.00	2.80	2.66	2.63
2	810	27	41	SW	4.77	3.92	2.13	3.12	24.200	23.24
3	810	30	42	RB	7.58	7.47	3.27	28.23	23.79	24.200
4	830	33	41	RB	12.41	11.73	7.90	27.18	23.31	23.79
5	830	27	42	TB	13.94	14.46	10.38	28.67	23.59	23.31
6	830	30	40	SW	18.68	16.80	15.47	27.36	23.03	23.594
7	850	33	42	SW	19.38	17.44	22.90	27.86	22.740	23.03
8	850	27	40	RB	0	0	0	28.40	22.59	22.74
9	850	30	41	TB	0	0	0	26.86	23.29	22.59

• Note: DP is pressure drop.

3.5.1. Coefficient of Variance as Response Parameter

Table 6 presents Taguchi analysis results, highlighting the impact of different factors like SR, temperature in (T_in), ambient temperature (T_am), and type of the enhancement methods (Type-C) of the drying uniformity. Among these factors, T_in was identified as the most influential, indicating that fluctuations in inlet temperature significantly affect moisture removal rates. Precise control of T_in is essential to ensuring stable heat distribution and minimizing drying in consistencies. The second most influential factor was Type-C, highlighting the importance baffles, swirlers, and other configurations influence turbulence and moisture distribution, meaning an optimized configuration can reduce drying variability. SR) ranked third suggesting that while it plays a role in drying uniformity, its impact is moderate compared to T_in and Type-C. The least influential factor was T_am, indicating that ambient temperature and humidity have minimal impact on drying uniformity. While external conditions can contribute to slight variations, their effect is much smaller than that of heat input factors like T_in and SR. Overall, optimizing T_in and Type-C should be prioritized to enhance drying uniformity, while managing SR exposure can further improve stability. Controlling ambient conditions may offer minor benefits but is not as critical as adjusting the primary heat and airflow parameters. The best combinations are SR level 3, T_am level 1, T_in level 1, and Type-C level 1 as shown in Figure 17.

Table 6. Response for signal-to-noise ratios of the C_v.

Level	SR	Tam	Tin	Type-C
1	1.2683	1.7331	0.8941	0.7403
2	2.0883	1.5459	1.2597	1.9094
3	1.2791	1.3567	2.4819	1.9860
Delta	0.8201	0.3764	1.5879	1.2458
Rank	3	4	1	2

3.5.2. Pressure as Response Variable

The Taguchi analysis results considered the pressured drop of the flow enhancement materials used within the dryer as response variables are depicted in Figure 18. SR was the most influential factor as shown in Table 7. Variations in SR significantly impact temperature gradients and airflow behavior, leading to fluctuations in pressure resistance within the system. The second most significant factor was T_in. Maintaining a stable T_in is essential to ensure a balanced pressure profile, preventing excessive resistance buildup or inefficiencies in the drying chamber. Type-C ranks third indicating that the design of baffles, swirlers, or duct systems influences airflow resistance. Poorly designed configurations can create air stagnation zones or excessive turbulence, leading to uneven pressure distribution. Optimizing these components by fine-tuning baffles or swirlers can enhance airflow uniformity and reduce pressure drop variations. Finally, T_am have the least impact. The optimal combination is SR level 2, T_am level 3, T_in level 3, and Type-C level 3 (swirler) (Figure 18).

Table 7. Pressured drop response for signal-to-noise ratios.

Level	SR	Tam	Tin	Type-C
1	−20.85	−21.46	−21.46	−21.47
2	−27.94	−27.24	−27.13	−27.16
3	−27.81	−27.90	−28.01	−27.96
Delta	7.10	6.45	6.55	6.49
Rank	1	4	2	3

4. Conclusion

The study investigates the impact of selected factors on the solar drying system and explores methods to improve the uniform distribution of air within the system. To address these issues, enhancement tools such as triangular and rectangular baffles and a swirler were employed to improve drying uniformity within the dryer. The findings indicate that the integration of these airflow enhancers significantly improves moisture uniformity during the drying process of golden apple slices in a solar dryer.

Taguchi optimization analysis was to evaluate the performance of the flow enhancement tools used and to select the optimal combination and its flow uniformity. Four critical control parameters are varied during the experiments: SR, ambient temperature, inlet temperature to the drying chamber, and integration of baffles. The response parameters measured include mass uniformity, temperature distribution, and pressure drop. The Taguchi analysis focuses on minimizing the S/N ratio, adhering to the principle that SB. The heated temperature entering the dryer (T_in) and solar intensity (SR) are the most dominant factors influencing the drying process. These parameters also play a crucial role in drying uniformity. The enhancement methods have a significant impact on drying uniformity, as indicated by the Taguchi results.

5. Limitations and Future Directions

Nomenclature

Abbreviations

Subscript

Conflicts of Interest

The authors declare no conflicts of interest.

Author Contributions

Halefom Kidane: writing – original draft, review and editing, visualization, validation, data curation, conceptualization. Istvan Farkas: supervision, editing. Janos Buzas: editing, supervision.

Funding

The authors received no specific funding for this work.