Predict System Performance Research Articles

Use of Distributed Energy Resources Integrated with the Electric Grid in the Amazon: A Case Study of the Universidade Federal do Pará Poraquê Electric Boat Using a Digital Twin

Electric mobility is a global trend and necessity, with electric and solar boats offering a promising alternative for transportation electrification and carbon emission reduction, especially in the Amazon region. This study analyzes the system of a solar boat from an electric mobility project—to be implemented at Universidade Federal do Pará (UFPA)—using MATLAB software for modeling. The Simulink tool was utilized to model the system, focusing on operational parameters such as module voltage, converter voltage, and speed. The results indicate that the solar boat’s operational cost is significantly lower compared to a similar internal combustion model, considering diesel’s high consumption and cost. The environmental impact is also reduced, with nearly 72 tons of CO2 emissions avoided annually, thanks to Brazil’s renewable energy matrix. Simulations confirmed the project’s parameters, demonstrating the efficiency of digital-twin technology in monitoring and predicting system performance. The study underscores the importance of digital twins and renewable energy in promoting sustainable transportation solutions, advocating for the replication of such projects globally. Future research should focus on further advancing digital-twin applications in electric mobility to enhance predictive maintenance and operational efficiency.

Machine learning­based approaches for enhancing human resource management using automated employee performance prediction systems

Purpose This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance. Design/methodology/approach In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employee’s performance meets expectations or needs improvement. Findings All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce. Research limitations/implications The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the model’s effectiveness across various contexts. Practical implications The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment. Social implications Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance. Originality/value This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach.

AMORA: An Advanced Malleable and Operational Framework for Performance Prediction of Big Data Systems

AMORA: An Advanced Malleable and Operational Framework for Performance Prediction of Big Data Systems

Impact of collector aspect ratio on the energy and exergy efficiency of a louvered fin solar air heater

Solar air heaters have huge applications in renewable energy utilization segments concerning the issues of space heating, drying, and ventilation systems. Performance prediction of such systems largely depends upon the design of the collector itself. The present study has highlighted the influence of various aspect ratios of the collector on the thermal and exergy performance of louvered finned solar or air heaters. In this context, an experimental analysis has been carried out to compare the performance of LFSAH with conventional PSAH. These results show that an increase in aspect ratios enhances thermal efficiencies of heat transfer and the systems. An aspect ratio of 4:1 showed the maximum thermal efficiencies as 81.63 % for LFSAH and 68.13 % for PSAH. On the other hand, at larger mass flow rates, the exergetic efficiency of LFSAH becomes lower owing to the larger friction and exergy losses and sometimes even lower than that of PSAH. The novelties of the present study are in emphasizing the importance of aspect ratio in optimizing solar air heater design and providing relevant insight for better energy use.

Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm

ABSTRACTThe research proposes a hybrid algorithm model that combines model‐driven and data‐driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi‐objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi‐source heterogeneous data, selection of key sub‐parameters using Principal Component Analysis (PCA), enhanced K‐means clustering analysis, determination of structural component target thresholds, time‐dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double‐layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub‐parameters that have significant impacts on the performance of structural components. Enhanced K‐means clustering analysis is carried out to effectively group and classify the identified key sub‐parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi‐parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan–Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double‐layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.

Unsteady-state entropy generation analysis of the counter-flow dew-point evaporative coolers

The surging demand for air conditioning, especially in hot climates, underscores the urgency of strategies aimed at curbing fossil fuel reliance and fostering energy saving strategy. Despite the longstanding utility and advantages of the conventional mechanical vapor compression refrigeration system, sustainability hurdles persist owing to its pronounced electricity demand. While evaporative cooling, harnessing water evaporation, emerges as a more efficient alternative, its efficacy is contingent upon environmental conditions. Addressing this, dew-point evaporative cooling (DPEC) presents an innovative direction, showcasing superior efficiency by directing supply air into a wet channel. This study delves into the thermodynamic losses within a counter-flow configured DPEC system, employing a transient entropy generation model rooted in the second law of thermodynamics to provide a comprehensive analysis of DPEC’s performance. Key findings that emerged from this study reveal that (1) the transient entropy generation is intricately distributed across different layers of the dew point evaporative cooling (DPEC) system, including dry air, wet air, the channel plate, and the water film; (2) Parametric analyses highlight the significant impact of factors on entropy generation, inlet air temperature has a minimal effect on system efficiency, but higher temperatures increase thermal losses. Excessive humidity limits evaporative potential, while low humidity significantly increases entropy generation. The system performs optimally at a working ratio of 0.3 and lower air velocities (within the inlet air velocity range of 0.6 to 2.2 m/s). Channel length has little impact, while the system is more sensitive to channel height, with a height of 2.5–3.5 mm being conducive to better performance. Experimental validation demonstrates the model’s accuracy in predicting system performance. The research contributes to a deeper understanding of DPEC systems, offering insights for achieving optimal operating conditions and enhancing overall efficiency in the pursuit of sustainable development.

Thermophysical Investigation of Multiform NiO Nanowalls@carbon Foam/1-Octadecanol Composite Phase Change Materials for Thermal Management.

Multiform NiO nanowalls with a high specific surface area were constructed in situ on carbon foam (CF) to construct NiO@CF/OD composite phase change materials (CPCMs). The synthesis mechanism, microstructures, thermal management capability, and photothermal conversion of NiO@CF/OD CPCMs were systematically studied. Additionally, the collaborative enhancement effects of CF and multiform NiO nanowalls on the thermal properties of OD PCMs were also investigated. NiO@CF not only maintains the porous 3D network structure of CF, but also effectively prevents the aggregation of NiO nanosheets. The chemical structures of NiO@CF/OD CPCMs were analyzed using XRD and FTIR spectroscopy. When combined with CF and NiO nanosheets, OD has high compatibility with NiO@CF. The thermal conductivity of NiO@CF/OD-L CPCMs was 1.12 W/m·K, which is 366.7% higher than that of OD. The improvement in thermal conductivity of CPCMs was theoretically analyzed according to the Debye model. NiO@CF/OD-L CPCMs have a photothermal conversion efficiency up to 77.6%. This article provided a theoretical basis for the optimal design and performance prediction of thermal storage materials and systems.

Optimal demodulation domain for microwave SQUID multiplexers in presence of readout system noise

The Microwave SQUID Multiplexer (μMUX) is the device of choice for the readout of a large number of low-temperature detectors in a wide variety of experiments within the fields of astronomy and particle physics. While it offers large multiplexing factors, the system noise performance is highly dependent on the cold- and warm-readout electronic systems used to read it out, as well as the demodulation domain and parameters chosen. In order to understand the impact of the readout systems in the overall detection system noise performance, first, we extended the available μMUX simulation frameworks, including additive and multiplicative noise sources in the probing tones (i.e., phase and amplitude noise), along with the capability of demodulating the scientific data, either in the resonator’s phase or the scattering amplitude. Then, considering the additive noise as a dominant noise source, the optimum readout parameters to achieve minimum system noise were found for both open-loop and flux-ramp demodulation schemes in the aforementioned domains. Later, we evaluated the system noise sensitivity to multiplicative noise sources under the optimum readout parameters. Finally, as a case study, we evaluated the optimal demodulation domain and the expected system noise level for a typical software-defined radio readout system. This work leads to an improved system performance prediction and noise engineering based on the available readout electronics and the selected demodulation domain.

Performance Analysis and Rapid Optimization of Vehicle ORC Systems Based on Numerical Simulation and Machine Learning

The organic Rankine cycle (ORC) system is an important technology for recovering energy from the waste heat of internal combustion engines, which is of significant importance for the improvement of fuel utilization. This study analyses the performance of vehicle ORC systems and proposes a rapid optimization method for enhancing vehicle ORC performance. This study constructed a numerical simulation model of an internal combustion engine-ORC waste heat recovery system based on GT-Suite software v2016. The impact of key operating parameters on the performance of two organic Rankine cycles: the simple organic Rankine cycle (SORC) and the recuperative organic Rankine cycle (RORC) was investigated. In order to facilitate real-time prediction and optimization of system performance, a data-driven rapid prediction model of the performance of the waste heat recovery system was constructed based on an artificial neural network. Meanwhile, the NSGA-II multi-objective algorithm was used to investigate the competitive relationship between different performance objective functions. Furthermore, the optimal operating parameters of the system were determined by utilizing the TOPSIS method. The results demonstrate that the highest thermal efficiencies of the SORC and RORC are 6.21% and 8.61%, respectively, the highest power outputs per unit heat transfer area (POPAs) are 6.98 kW/m2 and 8.99 kW/m2, respectively, the lowest unit electricity production costs (EPC) are 7.22 × 10−2 USD/kWh and 3.15 × 10−2 USD/kWh, respectively, and the lowest CO2 emissions are 2.85 ton CO2,eq and 3.11 ton CO2,eq, respectively. The optimization results show that the RORC exhibits superior thermodynamic and economic performance in comparison to the SORC, yet inferior environmental performance.

A Combined Scheme based on the Multiscale Stochastic Perturbed Parameterization Tendencies and Perturbed Boundary Layer Parameterization for a Global Ensemble Prediction System

Abstract Stochastic representations of model uncertainties are of great importance for the performance of ensemble prediction systems (EPSs). The stochastically perturbed parameterization tendencies (SPPT) scheme with a single-scale random pattern has been used in the operational global EPS of China Meteorological Administration (CMA-GEPS) since 2018. To deal with deficiencies in this operational single-scale SPPT scheme, a combined scheme based on the multiscale SPPT (mSPPT) scheme and the stochastically perturbed parameterization for the planetary boundary layer (SPP-PBL) scheme is developed. In the combined scheme, the mSPPT component aims to expand model uncertainties characterized by SPPT at mesoscale, synoptic scale, and planetary scale. The SPP-PBL component with six vital parameters is used to capture uncertainties in PBL processes, which is underrepresented by SPPT for the tapering treatment within PBL. Comparisons between the operational SPPT scheme and the mSPPT scheme reveal that the mSPPT scheme can generate more improvements in both ensemble reliability and forecast skills mainly in tropics. Besides, additional benefits from SPP-PBL on top of mSPPT are shown to be primarily distributed in tropics at the lower layers below 850 hPa and surface. Furthermore, the combined scheme of mSPPT and SPP-PBL is suggested to yield better spread–error relationships and forecast skills than the operational SPPT scheme in terms of objective verification scores for standard upper-air variables and surface parameters. A case study for the extreme precipitation event on 20 July 2021 in Henan Province of China also demonstrates the better ability of the combined scheme in forecasting the precipitation intensity and location. Significance Statement A comprehensive and reasonable representation of model uncertainties helps to improve the performance of ensemble prediction systems (EPSs). Despite the popular usage in simulating model uncertainties, the stochastically perturbed parameterization tendencies (SPPT) scheme possesses several shortcomings. To overcome this, a combined scheme based on the multiscale SPPT (mSPPT) scheme and the stochastically perturbed parameterization for the planetary boundary layer (SPP-PBL) scheme is proposed. Based on the global EPS of China Meteorological Administration (CMA-GEPS), additional benefits from the independent usage of mSPPT and SPP-PBL are disclosed. And the combined scheme can inherit the merits of mSPPT and SPP-PBL and generate more improvements on ensemble performance than the original single-scale SPPT scheme. This research provides a guideline for future upgradation of CMA-GEPS.

A model for energy predictions and diagnostics of large-scale photovoltaic systems based on electric data and thermal imaging of the PV fields

The aim of this investigation is the development of robust models for the performance prediction and automatic monitoring of large photovoltaic systems, based on historical and real-time electric and thermal data. This issue is increasingly important due to the worldwide diffusion of large photovoltaic systems and their need to identify and predict failures and malfunctions, in order to promptly assess the convenience of maintenance actions. The present model describes the response to irradiance and temperature conditions of both modules and inverters and also it is able to predict shading conditions able to affect the energy yield. The model has been validated against real electric measurements in 6 large PV plants located in southern Italy and it demonstrated to be able to predict the real time power production within a 4.1 % error. Even more importantly, the model and its comparison with subhourly measurements over several years has demonstrated its effectiveness in detecting downtime conditions caused by inverter or string problems. Simulations and measurements revealed that missed energy production due to electrical grid coupling downtime can exceed 50 % on certain days and that the shading conditions (up to 5 % of the daily energy production) can be easily detected and separated from component problems, thus avoiding false alarms. Finally, the analysis of aerial infrared images allowed to further test the model in failure detection capability, assess the relationship between thermal anomalies and underperformance conditions and in predicting the yearly deterioration rate at the PV plants.

Analyzing the likelihood of OAM mode energy transfer in turbulent environments with stochastic Cn2

Abstract Orbital angular momentum (OAM) is a crucial property of electromagnetic waves used in various applications such as free space communication, light detection and ranging, and remote sensing. However, turbulence can disrupt OAM-based systems by causing energy transfer between OAM modes, significantly impacting emerging fields in the optical spectrum. While traditional analyses assumed a constant turbulence parameter (Cn 2), recent research highlights its stochastic nature in specific scenarios. Our study introduces a new model that considers the stochastic nature of Cn 2. By incorporating this characteristic, our approach provides better predictions of system performance and valuable insights for accurately characterizing and designing communication and remote sensing systems in weak turbulent environments, enhancing their reliability and efficiency.

Improving Cleaning of Solar Systems through Machine Learning Algorithms

The study focuses on the importance of maintaining photovoltaic (PV) systems for optimal performance in sustainable energy generation. It highlights the impact of dust accumulation on reducing system efficiency and proposes a method to predict system performance, aiding in scheduling cleaning activities effectively. Two prediction models are developed: one using time-series prediction techniques (LSTM, ARIMA, SARIMAX) to forecast Performance Ratio (PR), and another employing ensemble voting classifiers (RF, Log, GBM) to predict the need for cleaning. The SARIMAX model performs best, achieving high accuracy in PR prediction (R2 = 92.12%), while the classification model accurately predicts cleaning needs (91%). The research provides valuable insights for improving maintenance strategies and enhancing the efficiency and sustainability of PV systems.

Integrating machine learning and thermodynamic modeling for performance prediction and optimization of supercritical CO2 and gas turbine combined power systems

The supercritical carbon dioxide (SCO2) cycle has drawn attention to extracting energy and generating power from low-grade heat resources due to its higher efficiency, lower cost, and compactness. Combining the SCO2 cycle with a gas turbine (GT) is a promising field of research with applications in industries such as waste heat recovery (WHR) systems. In this research, three different configurations of power generation SCO2 cycles, including recuperation, precompression, and reheating, were thermodynamically designed and modeled. Extracting the main parameters of cycles, and the appropriate objective, which is cycle energy efficiency, up to 80,000 data constructed. Random forest (RF) and support vector regression (SVR), machine learning (ML) techniques, are used for the performance prediction of cycles. The results show the strength of the RF method to predict the cycle efficiency with up to 3% error and the SVR method with more than 90% accuracy. Sensitivity analysis and Particle Swarm Optimization (PSO) algorithm are employed for optimizing the recuperation cycle. The outcomes of the optimization model, based on the prediction models as proxies, have results in nearly close to those of traditional optimization, which is based on a thermodynamic model. In conclusion, ML models can be used in industry for prediction and maintenance since the methods are up to 5 times faster than thermodynamic models.

The development and evaluation of a parameter-free circuit-based model of bipolar membrane electrodialysis for process design and optimisation

Bipolar membrane electrodialysis (BPMED) is an emerging electromembrane technology which has the potential to replace existing pH manipulation process units among others and take advantage of the benefits posed by process electrification. The development of robust and flexible process models of BPMED for design and optimisation is paramount in derisking potential instillations and improving commercial viability. Herein, a circuit-based model of BPMED is presented which avoids reliance on empirical fitting parameters and training data. The resulting model is flexible enough that extension to account for added complexities may be readily adopted. The mass transfer and electrical resistance of six different domains (three membranes and three streams) were computed by applying fundamental laws such as Ohm’s law and Faraday’s first law. Acid-base reactions and their effect releasing current within the membranes were also considered. Furthermore, the stack model can be readily embedded in a broader process model. To this end, the stack model is applied to a recirculating-batch experiment using a delayed differential material balance to account for dead-time within the tubing and measurement flow-cells. Two orthogonal methods of experimental validation were conducted to assess the performance of the model over a range of concentrations and applied voltages. These involved running a recirculating-batch experiment and collecting current–voltage polarisation data, respectively, and both showed good agreement with the model predictions. Overall, a robust model of BPMED has been produced which is able to accurately predict system performance and will prove useful for the design and optimisation of industrial systems.

Improvement of decadal predictions of monthly extreme Mei-yu rainfall via a causality guided approach

Abstract While the improved performance of climate prediction systems has allowed better predictions of the East Asian Summer Monsoon rainfall to be made, the ability to predict extreme Mei-yu rainfall (MYR) remains a challenge. Given that large scale climate modes (LSCMs) tend to be better predicted by climate prediction systems than local extremes, one useful approach is to employ causality-guided statistical models (CGSMs), which link known LSCMs to improve MYR prediction. However, previous work suggests that CGSMs trained with data from 1979–2018 might struggle to model MYR in the pre-1978 period. One hypothesis is that this is due to potential changes in causal processes, which modulate MYR in different phases of the multidecadal variability, such as the Pacific decadal oscillation (PDO). In this study, we explore this hypothesis by constructing CGSMs for different PDO phases, which reflect the different phases of specific causal process, and examine the difference in quality as well as with respect to difference drivers and thus causal links between CGSMs of different PDO phases as well as the non-PDO phase specific CGSMs. Our results show that the set of predictors of CGSMs is PDO phase specific. Furthermore, the performance of PDO phase specific CGSMs are better than the non-PDO phase specific CGSMs. To demonstrate the added value of CGSMs, the PDO phase specific versions are applied to the latest UK Met Office decadal prediction system, DePreSys4, and it is shown that the root-mean squared errors of MYR prediction based on PDO phase specific CGSMs is consistently smaller than the MYR predicted based on the direct DePreSys4 extreme rainfall simulations. We conclude that the use of a causality approach improves the prediction of extreme precipitation based solely on known LSCMs because of the change in the main drivers of extreme rainfall during different PDO-phases.

AI-Driven Solar Energy Generation and Smart Grid Integration A Holistic Approach to Enhancing Renewable Energy Efficiency

This paper comprehensively analyzes AI-driven solar energy generation and smart grid integration, focusing on enhancing renewable energy efficiency. The study examines applying advanced artificial intelligence techniques in optimizing solar power production, forecasting, and grid management. Machine learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are evaluated for effectiveness in solar irradiance prediction and PV system performance estimation. The integration of AI in smart grids is explored, highlighting its role in demand-side management, energy storage optimization, and grid stability control. A holistic approach to improving renewable energy efficiency is proposed, encompassing integrated AI frameworks for solar-plus-storage systems, multi-objective optimization techniques for energy management, and AI-enabled microgrids and virtual power plants. The paper also addresses the challenges and future trends in AI application to renewable energy systems, including scalability issues, regulatory considerations, and ethical implications. By leveraging big data analytics and advanced AI algorithms, this research demonstrates the potential for significant improvements in overall system efficiency, reliability, and sustainability of solar energy systems integrated with smart grids.

Artificial Neural Networks as Efficient Models of Proton Exchange Membrane Fuel Cells

Abstract Utilization of machine learning methodologies, particularly artificial neural networks (ANNs), presents a good approach to accurately model physical systems. Such predictive simulations offer the ability to predict system performance across diverse operational conditions without the need of use mathematical descriptions. This approach contrast with traditional, time-consuming and unstable approaches reliant on partial differential equations. In this study, ANN methodology is used to obtain the characteristics of proton exchange membrane fuel cells (PEMFCs). PEMFC are low temperature devices which convert chemical energy into electricity. This devices are promising applications in the automotive sector. Utilizing data gained from computational fluid dynamics simulations of PEMFCs, was explored various data collection techniques and network architectures to check their impact on predictive fidelity. Conclusions show that the ANN-based framework enables rapid prediction of current-voltage characteristics, achieving accuracy levels surpassing 90%. Practical of machine learning model implications were discussed, accenting its utility in optimizing PEMFC operational parameters and its potential integration within digital twin frameworks as a data-driven surrogate model. This study underscores the efficiency of machine learning techniques in advancing the comprehension and optimization of complex physical systems such as PEM-FCs, thereby paving the way for their use in engineering and energy sectors.

Football Player Analysis for Identifying Best Team using Machine Learning

In the game of football (soccer), the evaluation of players for transfer, scouting, squad formation and strategic planning is important. However, due to the vast pool of grassroots level player, short career span, differing performance throughout the individual’s career, differing play conditions, positions and varying club budgets, it becomes difficult to identify the individual player's performance value altogether. The Player Performance Prediction system aims at solving this complex problem analytically and involves learning from various attributes and skills of a football player. It considers the skill set values of the football player and predicts the performance value, which depicts the scope of improvement and the capability of the player. The objective of this project is to help the coaches and team management at the grassroots as well as higher levels to identify the future prospects in the game of football without being biased to subjective conditions like club budget, competitiveness in the league, and importance of the player in the team or region. The system is based on a data-driven approach and we train our models to generate an appropriate holistic relationship between the players’ attributes values, market value and performance value to be predicted. These values are dependent on the position that the football player plays in and the skills they possess. In This project best player is predicted by algorithms namely Naïve Bayes (NB) as proposed and K Nearest Neighbor (KNN) as existing system and compared in terms of Accuracy. From the results obtained its proved that proposed NB works better than existing KNN..

Experimental and numerical investigation of the incorporation of an air temperature controller for indirect solar dryers

Solar drying systems often face the challenge of overheating due to uncontrolled solar collectors, which can degrade the quality of dried products by destroying enzymes, vitamins, and their chemical composition. To address this issue, we developed and validated a new control system for stabilizing drying air temperature using both experimental and CFD numerical methods. This system not only effectively maintains the desired air temperature but also extends the lifespan of solar collectors by adjusting their exposure during periods of excessive solar radiation. The experimental results demonstrated that without the control system, the air temperature peaked at 72 °C, leading to potential product degradation. In contrast, the control system has succeeded in stabilizing the air temperature at an optimum level. Additionally, the validated CFD model confirmed the effectiveness of this control technique in various climatic conditions, including cold semi-arid, typically Mediterranean, hot semi-arid, and sub-desert conditions. The findings underline the importance and necessity of temperature control in solar drying systems, as well as the effectiveness of the CFD method in predicting system performance. Furthermore, this work significantly enhances the efficiency and applicability of solar drying technology, offering a practical solution for improving product quality and system durability.