Solar drying is an energy-efficient and environmentally friendly method for dehydrating agricultural and medicinal products; however, its performance is strongly affected by fluctuating climatic conditions and nonlinear heat and mass transfer processes. In cabinet-type solar dryers, maintaining the drying air temperature and relative humidity within optimal ranges is particularly critical for medicinal plants such as Plantago major leaves, which are sensitive to overheating and non-uniform drying. In this study, a Mamdani-type fuzzy logic-based intelligent control system is developed and experimentally validated for a cabinet solar dryer operating under real summer climatic conditions in Tashkent, Uzbekistan. The proposed controller regulates fan speed using drying air temperature and relative humidity as inputs. To evaluate its effectiveness, the fuzzy logic controller is benchmarked against a conventionally tuned Proportional–Integral–Derivative (PID) controller under identical operating and climatic conditions. A coupled thermodynamic–hygrometric dynamic model of the drying process is implemented in MATLAB/Simulink (R2024a) to support controller design and analysis. Experimental results demonstrate that the fuzzy logic controller maintains the drying air temperature within the optimal range of 45–50 °C despite significant fluctuations in solar irradiance (650–900 W/m²), whereas the PID-controlled system exhibits noticeable overshoot and oscillations. Compared with PID control, the fuzzy-controlled dryer achieves a smoother reduction in relative humidity, a reduction of approximately 22% in total drying time for the same final moisture content (8–10% wet basis), and an 18% decrease in auxiliary electrical energy consumption. In addition, tray-wise moisture measurements indicate improved drying uniformity under fuzzy control, with moisture variation remaining within ±4%. Overall, the results confirm that fuzzy-logic-based intelligent control provides a robust and energy-efficient solution for cabinet solar dryers operating under hot continental climatic conditions, offering clear advantages over conventional PID control in terms of stability, drying performance, and uniformity.

Calcined soda production is one of the important parts of the industry. One of the main problems in the production of soda is the waste of raw materials. Disposing of some raw materials as waste not only causes economic damage but also has a negative impact on the environment. Absorption is the main process in the production of calcined soda. By finding the optimal consumption of the chemical components involved in absorption process, it will be possible to reduce the amount of raw materials released into the waste. In this work, initially, the kinetic model of the absorption process was found using the stoichiometric matrix method. Based on the stoichiometric matrix, the main components (NH₃, CO₂, NH₄HCO₃, NaCl) participating in the chemical reaction were identified. Using the found kinetic model, a computer model of the absorption process was simulated in the MATLAB program. Based on the simulated computer model, a diagram of changes in the concentration of components in the absorption process over time was obtained. The use of the considered mathematical model as part of the absorption process control system allows: (1) Reduce the loss of the amount of gases used in the absorption process; (2) Provide the required concentration of liquid saturated with ammonia; (3) Determine the required temperature regime along the entire length of the absorber.

Currently, the main focus in the automation of technological processes is on developing control systems that enhance the quality of the control process. Because the systems being controlled are often complex, multidimensional, and nonlinear, quantum computing algorithms offer an effective solution. Although there are several intelligent control methods available to improve the quality of technological processes, each has certain drawbacks. Quantum algorithms, which rely on the principles of quantum correlation and superposition, are designed to optimize control while minimizing energy and resource consumption. This article discusses the diesel fuel hydrotreating process, a critical step in oil refining. The primary goal of hydrotreating is to enhance fuel quality by removing sulfur, nitrogen, and oxygen compounds. To accurately model this process, it is essential to consider not only the external factors affecting it but also its physical characteristics. By doing so, the mathematical model becomes more precise. Based on this approach, a quantum fuzzy control system for the diesel fuel hydrotreating process was developed using quantum algorithms. These algorithms can rapidly analyze large amounts of data and make decisions. At the same time, a computer model of a fuzzy quantum control system for the process of hydrotreating diesel fuel was constructed, and a number of computational experiments were carried out. As a result, a 1.8% reduction in energy costs for the diesel fuel hydrotreating process was achieved.

In recent years, intelligent control of complex thermodynamic systems has gained increasing attention due to global demands for higher energy efficiency and reduced environmental impact in industrial settings. This study explores the integration of quantum control methodologies-grounded in established principles of quantum mechanics—into the automation of thermal processes in power plant operations. Specifically, it investigates a hybrid quantum-fuzzy control system for managing steam heating processes, a critical subsystem in thermal power generation. Unlike conventional control strategies that often struggle with nonlinearity, time delays, and parameter uncertainty, the proposed method incorporates quantum-inspired optimization algorithms to enhance adaptability and robustness. The quantum component, based on recognized models of coherent control and quantum interference, is utilized to refine the inference mechanisms within the fuzzy logic framework, allowing more precise handling of state transitions in multivariable environments. A simulation model was constructed using validated physical parameters of a pilot-scale steam heating unit, and the methodology was tested against baseline scenarios with conventional proportional-integral-derivative (PID) control. Experimental protocols and statistical analysis confirmed measurable improvements: up to 25% reduction in fuel usage under specific operational conditions, with an average of 1 to 2% improvement in energy efficiency. The results suggest that quantum-enhanced intelligent control offers a feasible pathway for bridging the gap between quantum theoretical models and macroscopic thermal systems, contributing to the development of more energy-resilient industrial automation solutions.

Maintaining optimal control of heating boiler systems using intelligent control strategies remains a significant challenge due to strong nonlinearities, time delays, and unpredictable variations in fuel quality and thermal load. Conventional fuzzy logic controllers, while effective under nominal conditions, often exhibit limited robustness when exposed to abrupt parameter changes. To address this limitation, this study proposes a novel Quantum-Fuzzy Adaptive Intelligent Proportional-Integral-Derivative (QFAI-PID) control architecture, in which probabilistic inference mechanisms inspired by quantum principles are implemented algorithmically within a classical computing framework and validated through MATLAB/Simulink simulations. The proposed approach enhances the adaptability of fuzzy rule-based control by enabling probabilistic superposition and dynamic activation of control rules, allowing the knowledge base to self-organize in real time. The control system is evaluated using a nonlinear heating boiler model developed in MATLAB/Simulink under realistic industrial disturbances, including ±25% fuel flow variations, up to 30% changes in thermal demand, and measurement delays of 5–8 s. Simulation results demonstrate that the proposed controller achieves up to 36% improvement in control stability, 30% faster response time, and 22% reduction in energy-related control effort compared with conventional fuzzy control systems. These results confirm that the proposed quantum-inspired fuzzy approach provides a robust, energy-efficient, and practically implementable solution for intelligent control of nonlinear thermal energy systems.

Around the world, the food industry is focusing on achieving energy and resource efficiency. One of the main challenges in the field of process automation is the creation of effective control systems using intelligent technologies to improve the quality of processes and achieve the production of high-quality products with less energy and resources. Therefore, it is necessary to work with a large amount of data. Particular attention is paid to the development of computational algorithms for automated high-speed computational analysis systems for processing this data at high speed. Therefore, the article discusses the use of quantum computing methods in controlling complex dynamic objects operating under conditions of uncertainty, taking into account the use of quantum computing methods that have high speed and efficiency.