22PR4400, Development and demonstration of artificial intelligence composite sensor to expand energy management system,
Park Wan Ki
Abstract
Since the working process in the dyeing process is performed at high temperatures and high pressure, real-time measurement is difficult. Therefore, for real-time measurement of the dyeing process, this pH, conductivity, and chromaticity sensor was additionally installed, and a correlation and prediction model with the exhaustion rate that can determine the degree of dyeing completion was implemented based on Automated Machine Learning (AutoML) regression, and Extra tree with excellent performance indicators It was predicted using regressor, and the possibility of energy saving and process optimization was confirmed.
KSP Keywords
Dyeing process, Energy saving, High Temperature, Performance indicators, Process Optimization, Real-time measurement, Working process, excellent performance, high-pressure, machine Learning, prediction model
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