INTEGRATION OF INTELLIGENT CONTROL IN DRYING SYSTEMS: PROCESS OPTIMIZATION THROUGH SENSORS, ARTIFICIAL INTELLIGENCE, AND MODULAR DRYING
DOI:
https://doi.org/10.5281/zenodo.17941461Keywords:
Intelligent control systems, drying process optimization, artificial intelligence, sensor technologies, neural networks and fuzzy logic, hybrid control models, modular drying systems, cloud-based control, multimodal monitoring (data fusion), ecological monitoring and energy efficiencyAbstract
The article analyzes the integration of intelligent control systems—based on sensors, artificial intelligence,
hybrid, and modular approaches—into industrial drying technologies. Traditional drying equipment typically operates
according to predefined parameters such as temperature, airflow, and time, which often fail to deliver optimal results
under varying conditions, raw material properties, and external factors. Therefore, the paper explores modern approaches
including neural networks, neuro-fuzzy regulators, model-based control, data fusion, multimodal monitoring (hyperspectral
imaging, computer vision), and cloud-based management systems.
The analysis shows that intelligent control-based drying systems can reduce energy consumption by up to 10–15%,
shorten drying time, maintain consistent product moisture and quality, and significantly minimize operator errors. Moreover,
modular and multi-agent architectures improve adaptability for various raw materials, while cloud monitoring enables
remote control and large-scale data analysis. Integration with environmental monitoring raises drying technologies to a
new level of energy efficiency and ecological safety.
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