## 1 Introduction  
The dehydration of food is one of the oldest and most effective techniques for preserving food, extending its shelf life without the need for artificial preservatives [1]. By reducing the weight and volume of food, dehydration also facilitates storage and transportation, lowering costs and the environmental impact associated with the distribution of fresh food [2, 3, 4]. Additionally, by utilizing renewable energy sources, such as solar power, for the dehydration process, the ecological footprint can be further minimized [5, 6]. This practice not only contributes to food security and waste reduction but also supports the local economy by allowing producers to preserve surplus harvests and sell them out of season [7]. Drying equipment is categorized as a direct or adiabatic dryer. In the first category, the product to be dried is directly exposed to hot air, and in the second category, indirect or non-adiabatic dryers, heat is transferred from a medium in contact with the product. Among direct dryers, try dryers are the form most commonly used for small-scale production, due to their simple construction and instrumentation [8, 9]. One advantage of this type of equipment is their uniformity in drying products, which is achieved through temperature-controlled environments. Fuzzy logic (FL) has been employed successfully in the control of temperature and air flow in non-linear systems, demonstrating a greater efficacy than proportional-integral (PI) controllers. This approach has demonstrated the capacity to reduce energy and time demands, as well as the ability to adapt and operate under a wide range of conditions while maintaining product quality. This ensures the economic viability of the equipment [5; 10; 11; 12]. The main disadvantages of direct dehydrators are the lengthy drying time of approximately 50 h per load and their high energy consumption, which accounts for about 25% of the total energy consumed by the agribusiness industry. Although the traditional drying process relies solely on solar radiation, it results in a markedly slow drying time, often taking up to three days, depending on environmental conditions. Additionally, several external factors, such as animals, dust, and microorganisms, can potentially contaminate the product. This justifies the use of dehydrators, which accelerate the drying process and minimize the risk of contamination [13; 14; 15]. The temperature range for drying agricultural products varies depending on the specific product in question. However, the most limiting factor is the drying air, which must be hot and low in humidity. The temperature range for dryer operation has been found to vary from 40 \({}^{\circ}\)C to 70 \({}^{\circ}\)C, depending on the product. The current improvements in dehydrators include the implementation of an efficient design to ensure the proper drying conditions and hybrid or solar systems that aim to replace the use of fossil energy [4; 7; 15; 16; 17; 18]. However, the industrial type of dehydrators are still mostly electric and the experimental hybrid ones are smaller than the design proposed in this research. In recent decades, significant research has been conducted seeking to elucidate the chemical and physical alterations in the drying process and to develop innovative techniques in order to avert unfavorable quality deterioration. The application of computational fluid dynamics (CFD) in the dehydration process enhances the efficiency and uniformity of drying by accurately simulating and optimizing airflow and heat transfer within dehydrators [19; 20]. Additionally, CFD is a valuable software tool used for predicting the performance and characteristics of air movement. It provides a solution to engineering challenges, improves process control, and reduces resource expenditures, including energy, time, and costs [21; 22; 23; 24; 25]. Furthermore, modern computers have sufficient processing power to enable the implementation of this method, making it accessible and affordable [26; 27].