Evaluation of a Cyber-Physical System with Fuzzy Control for Efficiency Optimization in Rotary Dryers: Real-Time Multivariate Monitoring of Humidity, Temperature, Air Velocity and Mass Loss

Response to Reviewer 1 Comments

Dear Reviewer 1

Receive a cordial greeting. We deeply appreciate the time and dedication you have invested in reviewing our manuscript. Your valuable comments and suggestions have been of great help in improving the quality and clarity of our work.

We have reviewed each of your observations in detail and we completely agree with your assessments. Consequently, we have made the necessary changes and adjustments to the manuscript to address each of the points raised. Below we present a detailed response to each of your suggestions, explaining the modifications made.

Comments 1: [The manuscript is generally understandable, but several sentences are overly long or contain awkward phrasing. A thorough revision by a native English speaker or professional editor is recommended to enhance fluency and academic tone.]

Response 1: We sincerely thank the reviewer for this valuable observation. Following the recommendation, we carefully revised the entire manuscript to improve clarity, sentence structure, and academic tone. Several long sentences were simplified and restructured to enhance readability and coherence. In addition, we sought professional editing support to refine the English writing and ensure that the research is communicated more clearly.

The revisions were applied throughout the manuscript, particularly in the Introduction, Methodology, Results and Discussion, and Conclusions, where sentence structure and fluency were significantly improved.

Comments 2: [The proposed controller is not adaptive. In control theory, a controller is considered adaptive when it modifies its own internal parameters or structure in real time to cope with changes in the system dynamics or environment. The fuzzy controller described: Has a fixed rule base; Uses fixed membership functions; Operates based on predefined logic (no parameter adjustment).]

Response 2: We sincerely appreciate your valuable observation regarding the characterization of the fuzzy controller as “adaptive.” We acknowledge that, in control theory, a controller is considered adaptive only when it can modify its internal parameters or structure in real time to accommodate changes in the system dynamics or environmental conditions.

In our study, the implemented fuzzy controller maintains a fixed rule base and uses constant membership functions, operating under predefined inference logic. This means that it does not automatically adjust its parameters during operation; therefore, strictly speaking, it does not meet the formal criteria of an adaptive controller.

Nonetheless, the system does perform real-time regulation of the dryer temperature through the application of fuzzy logic, allowing it to maintain stable and efficient conditions within the rotary dryer. This dynamic response to process conditions may create a perception of functional “adaptability,” although it does not constitute formal adaptation of the controller’s structure.

In response to your comment, we have revised the manuscript to correct the terminology and accurately describe the system as a rule-based fuzzy controller, avoiding the term “adaptive,” which could be misleading. We believe this modification enhances the conceptual and technical accuracy of the article, aligning it with the clarity standards required for publication.

In Section 2.4, Dryer Design and Implementation of Fuzzy Control in Arduino, it is now explicitly stated that the employed fuzzy controller is not adaptive. Unlike an adaptive controller, which adjusts its internal parameters or structure during operation to respond to changes in system dynamics or environment, the developed system has a fixed rule base, static membership functions, and predefined inference logic, without real-time adjustments.

The controller allows real-time regulation of the dryer temperature, adjusting the heating element power based on variations in humidity and airflow velocity. This regulation capability ensures precise thermal management, contributing to efficient and uniform drying without the need to modify the controller’s structure or rules.

We reiterate our sincere gratitude for your observation, which enabled us to clarify this technical aspect and strengthen the rigor of our work.

Comments 3: [The manuscript would benefit from a broader discussion of prior applications of fuzzy control in drying systems, as the current references are limited in this regard. In particular, the study titled “Energy savings in a rotary dryer due to a fuzzy multivariable control application” provides a relevant industrial example that could strengthen the contextualization of the proposed approach and highlight its practical relevance. Including this reference would enhance the literature review and underscore the contribution of fuzzy control to energy efficiency in similar processes.]

Response 3: We sincerely appreciate your valuable comment. Following your recommendation, we have expanded the discussion on previous applications of fuzzy control in drying systems. In particular, the suggested reference “Energy savings in a rotary dryer due to a fuzzy multivariable control application” (now cited as reference number 55) has been incorporated, enriching the literature review and highlighting the practical relevance of our approach.

To further strengthen this section, three additional references addressing related applications of fuzzy control in drying processes [56–58] have been added, allowing for a broader and more contextualized discussion on the impact of this technique on the energy efficiency of similar systems.

These modifications are reflected in Section 3.5 Evaluation of Drying Efficiency in the Rotary Dehydration Equipment, where the results confirm the effectiveness of fuzzy control in nonlinear industrial processes with high parameter variability. Specifically, Júnior et al. [55] demonstrated that an industrial multivariable fuzzy controller can reduce energy consumption while maintaining product quality by regulating the temperature of the exhaust gases, achieving significant energy savings and reductions in CO₂ emissions. Complementarily, self-tuning fuzzy schemes enhance drying stability and efficiency [56], while the literature shows the versatility and robustness of fuzzy control compared to traditional methods [57,58]. In our study, the implementation of a Mamdani-type fuzzy system enabled the optimization of both drying time and energy efficiency, demonstrating its capability to handle uncertainty and variability under controlled experimental conditions.

Comments 4: [What distinguishes the proposed system from other Arduino-based fuzzy control applications in drying or thermal processes? Clarifying the novelty would strengthen the contribution of the work.]

Response 4:  We sincerely appreciate the reviewer’s valuable comment, which allowed us to clarify the novelty of our work. The proposed system stands out from other Arduino-based fuzzy control applications in drying or thermal processes due to its real-time multivariable integration.

Our design simultaneously monitors temperature, relative humidity, airflow velocity, and product mass loss, enabling dynamic control of drying conditions and optimizing both dehydration time and energy efficiency. In addition, the system implements a fuzzy control strategy based on experimental rules tailored to the specific dehydration kinetics of carrots, ensuring precise responses to variations in critical process variables. The combination of multivariable sensors, real-time data acquisition, and fuzzy logic applied to a rotary dryer with mass control minimizes residual moisture zones and reduces energy consumption per kilogram of product.

This information has been added to section “2.3 Cyber-physical monitoring and control system applied to carrot dehydration”, immediately after describing the system architecture and before the implementation of fuzzy logic, ensuring that the novelty and differentiation are clearly highlighted prior to detailing the technical aspects of the controller.

Comments 5: [Could the authors elaborate on the limitations of using low-cost sensors and Arduino-based systems in larger-scale industrial settings? While the system is cost-effective and replicable, it may face performance challenges in high-throughput applications.]

Response 5:  We appreciate the reviewer’s comment, which allows us to clarify the limitations of using low-cost sensors and Arduino-based systems in large-scale industrial settings. Although the developed system demonstrates cost-effectiveness, replicability, and adequate performance in small to medium scale applications, there are factors that could affect its performance in high-throughput operations:

Sensor accuracy and stability: Low-cost sensors, such as DHT22, DHT11, or basic load cells, may exhibit greater deviations under wide temperature and humidity ranges and mechanical vibrations typical of industrial installations, generating signal noise and reducing the real-time control precision.

Processing capacity and scalability: The Arduino Mega 2560 offers a limited number of analog and digital inputs as well as processing capacity. In large-scale applications, with multiple drying zones or a greater number of distributed sensors, the microcontroller may not efficiently process data simultaneously, affecting the response speed of the fuzzy control system.

Industrial robustness: In industrial environments, systems may be exposed to electromagnetic interference, mechanical vibrations, dust, and humidity, conditions under which low-cost microcontrollers and sensors can experience failures or performance degradation.

To overcome these limitations in industrial settings, it is recommended to integrate high-precision, industrial-grade sensors, employ microcontrollers with greater processing capabilities, and implement distributed data acquisition architectures to ensure reliability in real-time control and uniformity in the drying process.

This information has been added to the Conclusions section, as it discusses the practical implications and provides recommendations for implementing the system at different operational scales.

Comments 6: [Several equations (e.g., 2–8) are presented in the methods section but are not consistently or explicitly referenced in the main text. To improve clarity and rigor, I recommend integrating each equation into the narrative with clear forward or backward references, brief contextual explanations, and stronger links to the reported experimental results.]

Response 6: We sincerely appreciate the reviewer’s valuable comment and have implemented significant revisions in the methodology and results sections to address this recommendation. Each equation presented in the manuscript, from Equation (1) to Equation (8), has been systematically and explicitly integrated into the narrative, including concise contextual explanations of their purpose and the parameters involved, as well as their direct relationship with the experimental results obtained. This integration allows for a more fluid and rigorous presentation of the experimental procedure, highlighting how each equation contributes to the analysis of dehydration kinetics and the assessment of the rotary dryer’s performance.

Equation (1) is used to calculate the total energy consumed for moisture removal, separating the contributions of sensible heat through Equation (2) and latent heat through Equation (3). These calculations are directly related to temperature and mass measurements, allowing precise quantification of the energy utilized during the drying process. Equation (4) determines the total energy supplied to the product through the hot air flow, considering air density and specific heat. Meanwhile, Equation (5) calculates the effective air volume circulating in the dryer by integrating air flow velocity, the cross-sectional area, and the total operating time, enabling a more accurate estimation of the system’s thermal efficiency.

For the characterization of drying kinetics, Equation (6) is applied to determine the moisture content of the carrot samples, facilitating the construction of detailed drying curves and the evaluation of water loss throughout the process. Equation (7) allows the calculation of the dehydration rate, providing a dynamic indicator of dryer performance and reflecting the progressive decrease in moisture removal as the samples approach equilibrium. Finally, Equation (8) integrates the energy effectively used for water evaporation with the total energy supplied, providing an accurate measure of the system’s thermal efficiency. Experimental results demonstrate how product mass directly affects energy utilization and confirm the capability of the cyber-physical system to optimize energy transfer and maintain controlled drying rates.

Furthermore, with the modifications implemented in the results section, all equations are now clearly explained and linked to experimental evidence, strengthening the scientific rigor of the study while fulfilling the reviewer’s requirements for clarity in references and systematic presentation.

Comments 7: [While the manuscript presents detailed experiments comparing the effects of different load masses on drying performance, it does not clearly demonstrate the impact of the proposed fuzzy controller itself. All tests appear to use the fuzzy control system, but there is no comparison to a baseline controller (e.g., PID or manual control) to validate its effectiveness. As a result, it is unclear whether the reported energy savings or improvements in drying rate are attributable to the control strategy or simply to differences in load conditions. The authors should clarify this distinction or consider including a comparative control test to support their claims]

Response 7: We sincerely thank the reviewer for their excellent scientific judgment and valuable suggestions, which have allowed us to strengthen the clarity and rigor of our work. The primary objective of the research presented in this manuscript was to evaluate the performance of the implemented cyber-physical system for carrot drying, with particular emphasis on the integration of fuzzy control with multivariable data acquisition, as well as the analysis of parameters such as energy efficiency and dehydration kinetics. The central focus was not to perform a direct comparison between different control strategies, but rather to demonstrate how fuzzy logic can be integrated into a rotary dryer to simultaneously manage critical variables such as humidity, temperature, mass loss, and air velocity. A detailed analysis of the fuzzy controller’s performance under different load conditions was conducted, carefully documenting experimental results that reflect its impact on process efficiency and drying rate.

Nonetheless, we acknowledge the importance of conducting comparative evaluations against traditional control schemes, such as PID or manual control, as this would further strengthen the validation of the direct impact of the fuzzy controller. In this regard, we greatly value the recommendation provided and will prioritize it in future research, with the aim of broadening the scope of our analysis and reinforcing the experimental evidence regarding the distinctive advantages of fuzzy logic in agro-industrial drying processes. It is worth noting that this proposal was mentioned in the conclusions section, where we indicated that the comparison of different control types is considered a future research line, aimed at extending the scope of our analysis and further supporting the experimental evidence of the differential benefits of fuzzy logic in industrial drying applications.

We greatly appreciate your comments, which encourage us to continue deepening the rigorous evaluation of the proposed control system and to generate more robust and relevant research. Their feedback has been invaluable in improving the accuracy of our study. We hope that the modifications made are satisfactory and contribute to the acceptance of the manuscript.

We look forward to any additional comments that may arise and appreciate your time and effort in this process.

Sincerely,

Juan Manuel Tabares-Martinez¹, Adriana Guzmán-López ²*, Micael Gerardo Bravo-Sánchez ²*, Salvador Martín Aceves-Saborio³, Yaquelin Verenice Pantoja-Pacheco⁴ and Juan Pablo Aguilera-Álvarez³.

1          Departamento de Posgrado e Investigación (DEPI), Tecnológico Nacional de México (TecNM), Instituto Tecnológico de Celaya (ITC), Celaya, Guanajuato 38010, México; juan.tabares@itcelaya.edu.mx (J.M.T.-M.)

2          Departamento de Ingeniería Bioquímica e Ingeniería Ambiental, Tecnológico Nacional de México (TecNM), Instituto Tecnológico de Celaya (ITC), Celaya, Guanajuato 38010, México; adriana.guzman@itcelaya.edu.mx (A.G.-L.); gerardo.bravo@itcelaya.edu.mx (M.G.B.-S.)

3          Departamento de Ingeniería Mecánica, Tecnológico Nacional de México (TecNM), Instituto Tecnológico de Celaya (ITC), Celaya, Guanajuato 38010, México; salvador.aceves@itcelaya.edu.mx (S.M.A.-S.); juan.aguilera@itcelaya.edu.mx (J.P.A.-Á.)

4          Departamento de Ingeniería Industrial, Tecnológico Nacional de México (TecNM), Instituto Tecnológico de Celaya (ITC), Celaya, Guanajuato 38010, México; yaquelin.pantoja@itcelaya.edu.mx (Y.V.P.-P.)

*           Correspondence: gerardo.bravo@itcelaya.edu.mx (M.G.B.-S.) and adriana.guzman@itcelaya.edu.mx (A.G.-L.)

Response to Reviewer 2 Comments

Dear Reviewer 2

Receive a cordial greeting. We deeply appreciate the time and dedication you have invested in reviewing our manuscript. Your valuable comments and suggestions have been of great help in improving the quality and clarity of our work.

We have reviewed each of your observations in detail and we completely agree with your assessments. Consequently, we have made the necessary changes and adjustments to the manuscript to address each of the points raised. Below we present a detailed response to each of your suggestions, explaining the modifications made.

Comments 1: [The manuscript cites 54 references, which may be excessive for such a specific topic. I was able to trace only about 7 of them, and not always successfully (for example, when searching for technical details in references [28], [29], [30], see line 208). Some important references are missing, e.g., the original Mamdani method (1975).]

Response 1: We sincerely thank the reviewer for their valuable comments, which have contributed to enhancing the clarity and rigor of our manuscript. In response to the feedback regarding references [28–30], we have revised Section 2.1, “Design and Operational Characteristics of the Drying System Applied to Carrot Dehydration,” to provide a more detailed description of the technical aspects of these studies. Specifically, the methods and findings of Francik et al. [28] and Bogusz et al. [29] have been clarified, highlighting the impact of drying parameters on product quality and the importance of precise process monitoring. Additionally, the original reference by Mamdani [30] has been included to acknowledge the foundational methodology of fuzzy logic, describing its application in our system for managing critical variables such as temperature, humidity, air velocity, and mass loss within a cyber-physical framework with distributed sensors. This supports the implementation of fuzzy control to optimize drying performance.

Regarding the number of cited references, a comprehensive set of citations has been incorporated to benefit the manuscript by providing a broader discussion on previous applications of fuzzy control in drying systems. This expanded reference coverage was prompted by a reviewer’s suggestion to include a more extensive discussion of fuzzy control applications in drying processes. This approach allows us to better contextualize our work within the existing scientific landscape, highlighting how fuzzy logic integration has been used to improve energy efficiency, process stability, and product quality in various studies. We believe that this approach strengthens the manuscript by offering readers a more comprehensive perspective on prior methodologies and results reported in the literature.

The revised Section 2.1 now reads as follows:

"The design of the drying unit is based on recent research on rotary dryers applied to agri-food products, which has demonstrated their effectiveness in preserving thermal stability and key physicochemical and functional properties of vegetables and other food matrices [28–30]. Francik et al. [28] employed advanced modeling approaches, including artificial neural networks, to accurately predict the drying behavior of onion slices, emphasizing the value of precise process monitoring. Similarly, Bogusz et al. [29] examined the effects of different drying methods on the physical and functional properties of powdered black soldier fly larvae, illustrating that the selection of drying parameters has a direct impact on product quality. In designing the control strategy for the present system, we built upon the foundational work of Mamdani [30], who introduced fuzzy logic algorithms for controlling dynamic processes. His approach enables the integration of expert knowledge into control rules, allowing real-time adjustments in response to changing system conditions. This principle has been applied in our drying system to manage critical variables such as temperature, humidity, air velocity, and mass loss, creating a robust cyber-physical framework with distributed sensors. This instrumentation supports the implementation of fuzzy logic control, which dynamically adjusts operational parameters to optimize drying performance, as illustrated in Figure 1."

We hope that these revisions address the reviewer’s concerns and clearly demonstrate both the technical basis and the relevance of the cited studies.

Comments 2: [There is some uncertainty regarding the design of the cylindrical drum. Is it horizontal or inclined? Are buffers or flights included? The drying drum has a volume of 24 L, while the initial carrot mass is 3 kg (~3 L). This implies a porosity of about 90%, which seems inconsistent with Fig. 1. The positions of the drying air inlet/outlet, sensors, and electric heater are also unclear. Moreover, it is not specified which temperature is actually being measured (drying air, carrot mass?). ]

Response 2: We thank the reviewer for this valuable observation, which has allowed us to clarify key aspects of the drying drum design and operation. The cylindrical drum used for carrot dehydration is horizontal and not inclined, a configuration chosen to ensure mechanical stability, uniform rotation, and controlled residence time of the product, which is essential for small-to-medium scale rotary dryers. The system also incorporates internal flights (paddles) that lift and disperse the product during rotation, improving mixing and heat–mass transfer efficiency.

Regarding the load, the initial carrot mass of 3.5 kg, with an approximate bulk volume of 4.24 L, represents 17.2% of the total drum capacity (24.7 L). The apparent higher volumetric occupation observed in Fig. 1 can be explained by several technical phenomena:

Effect of interlocking with internal flights: During rotation, carrots are mechanically trapped between the paddles, creating a non-uniform stacked layer. This gives the visual impression of a fuller drum despite the relatively low actual density of occupation.

Perimetral distribution induced by airflow: The hot air stream generated by the blower fluidizes particles and promotes their adhesion to the inner wall of the drum (boundary layer effect). This creates an annular distribution that visually mimics greater filling.

Kinetic expansion due to centrifugal force: The centrifugal force disperses the particles toward the periphery of the drum, increasing the exposed surface area and generating a false perception of filling caused by continuous angular displacement.

Reduction of effective volume due to internal geometry: The lifting flights (typically occupying 15–20% of the volume) and the presence of embedded sensors reduce the effective working capacity to ≈20 L. Under this condition, the 4.24 L of carrots correspond to ~25% of the useful volume.

Air voids and porosity: The bed porosity, combined with forced airflow, temporarily expands the structure during operation, increasing the apparent volume.

In this study, we deliberately implemented moderate initial loads as part of the optimization strategy, aiming to achieve high drying efficiency and high-quality dehydrated product. This configuration also allows us to replicate and scale up the process in future research with larger input masses.

Additionally, Figure 1 has been revised to provide a clearer representation of the drying unit. The updated figure now specifies:

·         The exact positions of the air inlet and outlet

·         The distribution of sensors

·         The location of the electric heater.

Finally, we clarify that the measured temperature in the rotary dryer corresponds to the in-drum drying air temperature, which represents the actual thermal environment surrounding the carrot samples during dehydration.

We trust that these clarifications, along with the modifications to Figure 1, address the reviewer’s concerns and provide a more precise description of the dryer’s design and operating conditions.

Comments 3: [Manufacturers of the instruments used (DHT22, DHT11, IP65, Arduino Mega) should be indicated. In Fig. 7, the title should be presented in English.]

Response 3: We sincerely thank the reviewer for this valuable comment. The title of Figure 7 has been revised and is now presented in English, as requested. In addition, the manufacturers of the instruments used in the study (DHT22, DHT11, IP65 anemometer, and Arduino Mega 2560 R3) have been explicitly included and detailed in Section 2.3, “Cyber-physical monitoring and control system applied to carrot dehydration”, as follows:

The cyber-physical system was designed to enable real-time monitoring of the main variables involved in the dehydration process using high-precision sensors. Drying air temperature and relative humidity were measured with DHT22 (Aosong Electronics Co., Ltd., Guangzhou, China) and DHT11 (Aosong Electronics Co., Ltd., Guangzhou, China) transducers, respectively. Airflow velocity was recorded with an IP65 anemometer (Davis Instruments, California, USA), while sample mass loss was measured using load cells connected to the data acquisition unit. The analog signals were conditioned and digitized through an Arduino Mega 2560 R3 (Arduino AG, Turin, Italy), minimizing electrical noise and improving signal stability, thus ensuring reliable data for subsequent analyses of thermal efficiency and process modeling.

These revisions provide complete manufacturer information and improve the clarity of the figure, fully addressing the reviewer’s request.

Comments 4: [Lines 484–486: “Arduino processed the signals from the temperature sensors (but only one DHT22 sensor is mentioned) in real time, dynamically adjusting the system parameters based on thermal variations during the drying process.” Which parameters are adjusted, and how?.]

Response 4: We sincerely thank the reviewer for their valuable comment, which has allowed us to clarify and improve the description of our control system. To provide clarification, the Arduino Mega 2560 R3 processes in real time the signals from all sensors: the DHT22 temperature sensor, the DHT11 sensor for relative humidity, an IP65 anemometer for air velocity, and load cells to monitor mass loss during the drying process.

Based on the readings from these sensors, the Arduino Mega 2560 R3 dynamically adjusts critical operational parameters, primarily the heater power and the hot air temperature within the dryer, using pulse-width modulation (PWM) to control the electric resistance and maintain the drying air temperature inside the cylindrical chamber of the dehydrator.

These adjustments are implemented through a fuzzy logic algorithm, which continuously translates sensor readings into real-time control actions.

This explanation has been incorporated into Section 2.3, “Cyber-physical monitoring and control system applied to carrot dehydration,” providing a more precise description of the adjusted parameters and the implemented control strategy.

We again thank the reviewer for their observations, which have significantly contributed to enhancing the clarity and rigor of our research.

Comments 5: [If I understand correctly, the only output of the Arduino controller is the heater power (as a function of time), which is determined by Mamdani logic based on three input variables: temperature, humidity, and air velocity. The relationship is expressed through 27 Mamdani rules (3 × 3 × 3 combinations of three variables at three levels: low, medium, high). Two examples (R1 and R7) are provided on line 317. The drying process (and thus the temperature and moisture profiles) depends primarily on the selected Mamdani rules and could be optimized for minimum drying time and energy consumption? How is this optimization carried out? May be that it is a wrong question.]

Response 5: We sincerely thank the reviewer for their observation and interest in the employed fuzzy rule base. Indeed, the Arduino-based system generates as its main output the control of the heater power, which is determined by a Mamdani fuzzy logic controller based on the input variables: temperature, humidity, and airflow velocity.

It is important to note that this control scheme can be optimized both in terms of drying time and energy consumption. Optimization is performed by adjusting the fuzzy rules based on experimental results obtained under different operating conditions. In this way, the parameters that allow for reduced dehydration time without compromising thermal efficiency or product quality are identified.

In the present work, optimization was approached experimentally through controlled tests, comparing temperature, humidity, and airflow profiles under different fuzzy system settings. This enabled the selection of rule configurations that maximize heat transfer and promote efficient moisture evaporation, ensuring a faster drying process with lower energy expenditure.

In future work, we plan to extend the system by incorporating fuzzy control techniques with an expanded rule base to systematically minimize both drying time and energy consumption, as suggested by the reviewer. The detailed information addressing this comment has been incorporated into Section 2.4.4, Fuzzy System Rule Base.

Comments 6: [It might be useful to include in the graphs the time profile of the supplied electric power as evaluated and integrated by the Arduino. This likely represents the major portion of the energy input to the drying process and can be calculated precisely.]

Response 6:  We appreciate the reviewer’s valuable suggestion regarding the inclusion of the time profile of the supplied electrical power. Consequently, we have incorporated into the graphs presented in the manuscript the temporal profile of electrical energy consumption, recorded and integrated by the Arduino microcontroller throughout the drying process. This profile accurately reflects the energy supplied to the main components responsible for generating the hot air flow, namely the centrifugal fan and the electrical resistance.

The supplied energy was calculated based on the operating power of each component and the drying times recorded in each experiment. Specifically, the fan operated at an average power of 14.7 W, well below its nominal maximum capacity of 200 W, corresponding to an average airflow velocity of approximately 1.6 m/s. The electrical resistance operated at an average power of 28.5 W to maintain a controlled drying temperature close to 75 °C during the dehydration process.

The Arduino system integrated the energy consumption over time, enabling a precise estimation of the total electrical energy supplied during the drying process, which was 1445 kJ, 1119 kJ, and 855 kJ for initial masses of 3.5 kg, 2.5 kg, and 1.5 kg, respectively. These detailed results are presented in Figure 9 and discussed in Section 3.3, facilitating a clearer understanding of the contribution of electrical energy to the overall energy balance of the drying system.

Comments 7: [The previous point is also related to the calculation of drying efficiency (see Fig. 10). This value is defined as the ratio of the enthalpy of evaporated water to the enthalpy of the supplied electric energy. The latter consists of (a) the power of the heating resistor, which can be calculated precisely by Arduino, and (b) the power of the fan motor, which is estimated only very approximately from the air velocity, since the actual motor power was not measured (only the maximum rated power of 200 W is specified). The reported drying efficiencies (85% vs. 20%) are attributed to differences in thermal inertia (line 625), but this explanation should be clarified.]

Response 7: We sincerely appreciate your valuable feedback, which allowed us to improve the clarity and accuracy of the manuscript regarding the calculation of drying efficiency. We have specified in the text that efficiency is defined as the ratio between the enthalpy of the evaporated water and the enthalpy of the supplied electrical energy, which includes (a) the power of the heating resistance, measured with Arduino, and (b) the working power of the fan motor, estimated from the air velocity, considering that it does not operate at its maximum nominal capacity. Furthermore, we have expanded the explanation in the discussion section, emphasizing that drying efficiency is related to the magnitude of the mass to be dehydrated: the larger the load, the lower the wasted heat and the greater the probability of effective energy transfer between the hot air and the product. The information clarifying this point has been incorporated into the conclusions section, specifically in the paragraph corresponding to the line indicated.

We greatly appreciate your comments, which encourage us to continue deepening the rigorous evaluation of the proposed control system and to generate more robust and relevant research. Their feedback has been invaluable in improving the accuracy of our study. We hope that the modifications made are satisfactory and contribute to the acceptance of the manuscript.

We look forward to any additional comments that may arise and appreciate your time and effort in this process.

Sincerely,

Juan Manuel Tabares-Martinez1, Adriana Guzmán-López 2*, Micael Gerardo Bravo-Sánchez 2*, Salvador Martín Aceves-Saborio3, Yaquelin Verenice Pantoja-Pacheco4 and Juan Pablo Aguilera-Álvarez3.

1          Departamento de Posgrado e Investigación (DEPI), Tecnológico Nacional de México (TecNM), Instituto Tecnológico de Celaya (ITC), Celaya, Guanajuato 38010, México; juan.tabares@itcelaya.edu.mx (J.M.T.-M.)

2          Departamento de Ingeniería Bioquímica e Ingeniería Ambiental, Tecnológico Nacional de México (TecNM), Instituto Tecnológico de Celaya (ITC), Celaya, Guanajuato 38010, México; adriana.guzman@itcelaya.edu.mx (A.G.-L.); gerardo.bravo@itcelaya.edu.mx (M.G.B.-S.)

3          Departamento de Ingeniería Mecánica, Tecnológico Nacional de México (TecNM), Instituto Tecnológico de Celaya (ITC), Celaya, Guanajuato 38010, México; salvador.aceves@itcelaya.edu.mx (S.M.A.-S.); juan.aguilera@itcelaya.edu.mx (J.P.A.-Á.)

4          Departamento de Ingeniería Industrial, Tecnológico Nacional de México (TecNM), Instituto Tecnológico de Celaya (ITC), Celaya, Guanajuato 38010, México; yaquelin.pantoja@itcelaya.edu.mx (Y.V.P.-P.)

*           Correspondence: gerardo.bravo@itcelaya.edu.mx (M.G.B.-S.) and adriana.guzman@itcelaya.edu.mx (A.G.-L.)