ANALYSIS OF THE SINGULAR SPECTRUM OF WATER CONSUMPTION IN A DIFFUSION UNIT OF A SUGAR PRODUCTION PLANT FOR AUTOMATION APPLICATIONS

Abstract

This paper investigates the application of Singular Spectrum Analysis for processing time series of water flow rate in a diffusion unit of sugar production. Stable water flow is an important factor affecting the efficiency of the diffusion process, since it influences hydrodynamic conditions, sucrose extraction efficiency, and energy consumption of the technological equipment. In industrial environments, measurement signals often contain noise, random disturbances, and short term anomalies, which complicates their direct use in automatic control systems. The study applies Singular Spectrum Analysis as a non parametric method for time series decomposition that allows separating trend, periodic components, and noise without assuming a predefined mathematical model of the process. A practical implementation procedure is proposed, including trajectory matrix construction, singular value decomposition, component grouping, and signal reconstruction using diagonal averaging. Experimental analysis of water flow rate data demonstrates that the leading components represent the main physical structure of the signal, including trend and seasonal variations, while higher order components correspond to noise. Reconstruction based on the dominant components significantly reduces random disturbances while preserving the informative dynamics of the technological process. The quality of reconstruction is evaluated using statistical accuracy indicators such as Mean Absolute Error, Root Mean Square Error, and Mean Absolute Percentage Error. The obtained results confirm that Singular Spectrum Analysis can effectively improve the reliability of measurement signals and may be applied in industrial automation systems for control, diagnostics, and forecasting of diffusion unit operating modes.