CONTROLLING AND MANAGEMENT SYSTEMS IN DRYING PROCESSES

Аннотация

Drying processes play a vital role across multiple industries, yet they remain among the most energy-intensive and technically complex operations. The effective control and management of drying systems are essential for ensuring product quality, operational efficiency, and energy conservation. This paper explores the evolution, current practices, and emerging technologies in the control and automation of drying processes. Beginning with traditional PID-based control, the study reviews the shift toward model-based strategies such as Model Predictive Control (MPC) and soft computing approaches including fuzzy logic and neural networks. It also highlights the importance of sensor integration, real-time monitoring, and the adoption of SCADA and IoT frameworks for intelligent process management. Despite significant advancements, challenges such as modeling complexity, cost of implementation, and skill requirements continue to hinder widespread adoption, particularly in small and medium-sized enterprises. The discussion emphasizes that future solutions should prioritize scalability, user-friendliness, and energy efficiency. Ultimately, the research underscores the transformative potential of intelligent control systems in optimizing industrial drying operations withinthe contextofdigitalization and sustainableproduction. Keywords: drying process control, industrial automation, model predictive control, fuzzy logic, neural networks, real-time monitoring, energy efficiency, SCADA systems, sensor integration, process optimization Introduction. Drying processes are among the most energy-intensive and technically sensitive operations in various industrial sectors, including food processing, pharmaceuticals, agriculture, chemical production, and materials engineering. The efficiency, consistency, and quality of these processes directly depend on the precision with which thermal and mass transfer mechanisms are monitored and regulated. As such, controlling and management systems have emerged as critical components in the optimization of drying technologies, aiming to enhance energy efficiency, reduce processing times, maintain product integrity, and meet stringent environmental and economic constraints. The primary objective of drying is to remove moisture from raw materials to a desired level without compromising the physical, chemical, or structural properties of the product. Achieving this balance requires meticulous control of variables such as temperature, humidity, air velocity, pressure, and product residence time. Traditional manual control methods are often inadequate for managing the complex, nonlinear dynamics involved in industrial drying. Consequently, the implementation of automated control systems—ranging from classical PID controllers to advanced model predictive and fuzzylogic systems—has become increasingly essential. The evolution of control and management systems in drying processes parallels broader trends in industrial automation and digital transformation. The integration of sensors, programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA) systems, and artificial http://www.internationaljournal.co.in/index.php/jasass 762 Volume 15Issue 06, June 2025 Impact factor: 2019: 4.679 2020: 5.015 2021: 5.436, 2022: 5.242, 2023: 6.995, 2024 7.75 intelligence (AI)-based algorithms has revolutionized process monitoring and decision-making. These innovations not only allow for real-time adjustments based on feedback from critical process variables but also enable predictive maintenance, fault detection, and adaptive optimization—features that are crucial in achieving consistent product quality and minimizing energy consumption. Despite these advancements, the deployment of control systems in drying processes poses numerous challenges. These include the inherent variability of raw materials, the dynamic nature of drying kinetics, the high cost of system integration, and the technical skill required for design and maintenance. Additionally, different industries impose varying requirements on control accuracy, system flexibility, and scalability, necessitating customized solutions for different drying applications. This paper aims to explore and critically analyze the current state, methodologies, and technological innovations in controlling and management systems within drying processes. Through a review of scholarly literature, industrial practices, and emerging technologies, the study seeks to identify effective strategies and frameworks for enhancing control precision, operational efficiency, and sustainabilityin modern drying systems. Literature Review. The scientific and industrial literature over the past few decades has increasingly emphasized the critical role of control and management systems in enhancing the performance of drying processes. Given the inherently nonlinear, multivariable, and time-variant nature of drying dynamics, a wide array of studies has been dedicated