Despite the available general literature in intelligent control, there is a definite lack of knowledge and know-how in practical applications of intelligent control in drying. This book fills that gap. Intelligent Control in Drying serves as an innovative and practical guide for researchers and professionals in the field of drying technologies, providing an overview of control principles and systems used in drying operations, from classical to model-based to adaptive and optimal control. At the same time, it lays out approaches to synthesis of control systems, based on the objectives and control strategies, reflecting complexity of drying process and material under drying. This essential reference covers both fundamental and practical aspects of intelligent control, sensor fusion and dynamic optimization with respect to drying.

A control system for multi-stage ginseng drying, based on real-time identification of moisture and quality changes, was developed. Computer-vision system provided real-time imaging of area shrinkage and colour changes. Shrinkage was identified by extracting of the green plane from the RGB color space, while colour changes- from the HSI color space. Shrinkage-moisture and colour-quality relationships were experimentally developed over the entire range of drying conditions in isothermal and non-isothermal drying. Relational models were used as components of control system. Data from computer vision system were used to generate relevant signals to low-level controller for temperature adjustment according to actual moisture content and quality. It enabled to start drying with the predictive model and then re-evaluate the drying conditions on the basis of feedback information from computer-vision system. Testing of a pilot batch dryer with an embedded virtual controller showed robustness and reliability of control, which provided drying to target moisture content 0.1 g/g (db) with high-standard quality.