Abstract
Grain drying is a complex nonlinear heat and mass transfer process whose efficiency depends on maintaining optimal temperature and moisture conditions while minimizing energy consumption. Conventional automatic control systems are often unable to adapt effectively to variations in grain properties and operating conditions, resulting in reduced drying efficiency and increased energy consumption. This study presents the development of an intelligent automatic control system for the grain drying process based on mathematical modelling, adaptive control principles, and Internet of Things (IoT) technologies. The proposed system integrates a sensor network, a data acquisition and communication module, an intelligent control unit, actuators, and a real-time monitoring environment into a unified control architecture. A mathematical model of the drying process was developed and implemented in Python to analyse the dynamic behaviour of the main process variables and evaluate the performance of the proposed control strategy. The proposed approach enables continuous monitoring of process parameters, supports adaptive decision-making under varying operating conditions, and provides a flexible framework for improving process stability and energy efficiency. The scientific contribution of this study lies in the integration of mathematical modelling, intelligent control methods, and IoT technologies into a unified architecture for grain drying automation. The proposed solution can be applied to the modernization of existing grain drying facilities and the development of next-generation intelligent drying systems with enhanced operational reliability, product quality, and energy efficiency.