Drying is a critical and energy-intensive operation in many industrial sectors, including food processing, pharmaceuticals, and chemical manufacturing. Conventional drying methods often rely on fixed parameters or manual control, which can result in inefficiencies, inconsistent product quality, and higher energy consumption. This study focuses on the development of an intelligent control system that integrates real-time sensor data, artificial intelligence, and fuzzy logic algorithms to optimize drying conditions. The system was tested in a pilot drying unit, and its performance was evaluated in terms of drying time, energy consumption, and moisture uniformity. Results showed that the intelligent control system reduced drying time by 12–15%, decreased energy consumption by 10–12%, and significantly improved moisture uniformity compared to traditional methods. The findings highlight the potential of AI-driven intelligent control systems to enhance efficiency, product quality, and energy sustainability in industrial drying operations.

Intelligent control system, Industrial drying, Artificial intelligence, Fuzzy logic, Energy efficiency, Moisture uniformity

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