Steady-state detection of evaporation process based on multivariate data fusion.

ABSTRACT: In this paper, we introduce an innovative multivariable data fusion strategy for adaptive steady-state detection, specifically tailored for the alumina evaporation process. This approach is designed to counteract the production instabilities that often arise from frequent alterations in production conditions. At the core of our strategy is the application of an adaptive denoising algorithm based on the Gaussian filter, which adeptly eliminates erroneous data from selected variables without compromising the fidelity of the original signal. Subsequently, we implement a multivariable R-test methodology, integrated with the adaptive Gaussian filter, to conduct a thorough and precise steady-state detection via data fusion. The efficiency of this method is rigorously validated using actual data

SUBMITTER: Qian X 

PROVIDER: S-EPMC11379215 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature