Center for Advanced Research in Drying » Drying Process, Endpoint & Product Quality Monitoring Using Optical Sensing & Machine Learning

Overview

Timely detection of drying endpoint and drying process monitoring is crucial for product quality and process economics. Usually, endpoint detection in the drying process is carried out based on indirect parameters such as humidity, temperature of outlet dry air, product temperature etc. Such measurements do not directly represent the moisture content of dried products. These measurements are also susceptible to environmental interference. These measurements do not guarantee prediction accuracy. Consequently, direct measurement of moisture content is ideal for the drying process. Near-infrared spectroscopy (NIRS) has been established as a key tool. NIR is a multi-analytical method, non-destructive, low-cost, repeatable, collects data in real-time, and can be used for feature development of drying. If optimized with an AI-powered calibration model, NIRS and conventional imaging will be incomparable for monitoring real-time drying processes for quality control and endpoint detection.

M. Kamruzzaman